1
|
Jelescu IO, Grussu F, Ianus A, Hansen B, Barrett RLC, Aggarwal M, Michielse S, Nasrallah F, Syeda W, Wang N, Veraart J, Roebroeck A, Bagdasarian AF, Eichner C, Sepehrband F, Zimmermann J, Soustelle L, Bowman C, Tendler BC, Hertanu A, Jeurissen B, Verhoye M, Frydman L, van de Looij Y, Hike D, Dunn JF, Miller K, Landman BA, Shemesh N, Anderson A, McKinnon E, Farquharson S, Dell'Acqua F, Pierpaoli C, Drobnjak I, Leemans A, Harkins KD, Descoteaux M, Xu D, Huang H, Santin MD, Grant SC, Obenaus A, Kim GS, Wu D, Le Bihan D, Blackband SJ, Ciobanu L, Fieremans E, Bai R, Leergaard TB, Zhang J, Dyrby TB, Johnson GA, Cohen‐Adad J, Budde MD, Schilling KG. Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging. Magn Reson Med 2025; 93:2507-2534. [PMID: 40008568 PMCID: PMC11971505 DOI: 10.1002/mrm.30429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2024] [Revised: 12/19/2024] [Accepted: 12/26/2024] [Indexed: 02/27/2025]
Abstract
Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.
Collapse
Affiliation(s)
- Ileana O. Jelescu
- Department of RadiologyLausanne University Hospital and University of LausanneLausanneSwitzerland
- CIBM Center for Biomedical ImagingEcole Polytechnique Fédérale de LausanneLausanneSwitzerland
| | - Francesco Grussu
- Radiomics GroupVall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital CampusBarcelonaSpain
- Queen Square MS Centre, Queen Square Institute of Neurology, Faculty of Brain SciencesUniversity College LondonLondonUK
| | - Andrada Ianus
- Champalimaud ResearchChampalimaud FoundationLisbonPortugal
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Brian Hansen
- Center of Functionally Integrative NeuroscienceAarhus UniversityAarhusDenmark
| | - Rachel L. C. Barrett
- Department of NeuroimagingInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
- NatBrainLab, Department of Forensics and Neurodevelopmental SciencesInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - Manisha Aggarwal
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Stijn Michielse
- Department of Neurosurgery, School for Mental Health and Neuroscience (MHeNS)Maastricht University Medical CenterMaastrichtThe Netherlands
| | - Fatima Nasrallah
- The Queensland Brain InstituteThe University of QueenslandSt LuciaQueenslandAustralia
| | - Warda Syeda
- Melbourne Neuropsychiatry CentreThe University of MelbourneParkvilleVictoriaAustralia
| | - Nian Wang
- Department of Radiology and Imaging SciencesIndiana UniversityBloomingtonIndianaUSA
- Stark Neurosciences Research InstituteIndiana University School of MedicineBloomingtonIndianaUSA
| | - Jelle Veraart
- Center for Biomedical ImagingNYU Grossman School of MedicineNew YorkNew YorkUSA
| | - Alard Roebroeck
- Faculty of psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
| | - Andrew F. Bagdasarian
- Department of Chemical and Biomedical Engineering, FAMU‐FSU College of EngineeringFlorida State UniversityTallahasseeFloridaUSA
- Center for Interdisciplinary Magnetic ResonanceNational HIgh Magnetic Field LaboratoryTallahasseeFloridaUSA
| | - Cornelius Eichner
- Department of NeuropsychologyMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Farshid Sepehrband
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USCUniversity of Southern CaliforniaCaliforniaLos AngelesUSA
| | - Jan Zimmermann
- Department of Neuroscience, Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
| | | | - Christien Bowman
- Bio‐Imaging Lab, Faculty of Pharmaceutical, Biomedical and Veterinary SciencesUniversity of AntwerpAntwerpBelgium
- μNEURO Research Centre of ExcellenceUniversity of AntwerpAntwerpBelgium
| | - Benjamin C. Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Andreea Hertanu
- Department of RadiologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Ben Jeurissen
- imec Vision Lab, Department of PhysicsUniversity of AntwerpAntwerpenBelgium
- Lab for Equilibrium Investigations and Aerospace, Department of PhysicsUniversity of AntwerpAntwerpenBelgium
| | - Marleen Verhoye
- Bio‐Imaging Lab, Faculty of Pharmaceutical, Biomedical and Veterinary SciencesUniversity of AntwerpAntwerpBelgium
- μNEURO Research Centre of ExcellenceUniversity of AntwerpAntwerpBelgium
| | - Lucio Frydman
- Department of Chemical and Biological PhysicsWeizmann Institute of ScienceRehovotIsrael
| | - Yohan van de Looij
- Division of Child Development and Growth, Department of Pediatrics, Gynaecology and Obstetrics, School of MedicineUniversité de GenèveGenèveSwitzerland
| | - David Hike
- Department of Chemical and Biomedical Engineering, FAMU‐FSU College of EngineeringFlorida State UniversityTallahasseeFloridaUSA
- Center for Interdisciplinary Magnetic ResonanceNational HIgh Magnetic Field LaboratoryTallahasseeFloridaUSA
| | - Jeff F. Dunn
- Department of Radiology, Cumming School of MedicineUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain Institute, Cumming School of MedicineUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research Institute, Cumming School of MedicineUniversity of CalgaryCalgaryAlbertaCanada
| | - Karla Miller
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Bennett A. Landman
- Department of Electrical and Computer EngineeringVanderbilt UniversityNashvilleTennesseeUSA
| | - Noam Shemesh
- Champalimaud ResearchChampalimaud FoundationLisbonPortugal
| | - Adam Anderson
- Vanderbilt University Institute of Imaging ScienceVanderbilt UniversityNashvilleTennesseeUSA
- Department of Radiology and Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Emilie McKinnon
- Medical University of South CarolinaCharlestonSouth CarolinaUSA
| | - Shawna Farquharson
- National Imaging FacilityThe University of QueenslandBrisbaneQueenslandAustralia
| | - Flavio Dell'Acqua
- Department of Forensic and Neurodevelopmental SciencesKing's College LondonLondonUK
| | - Carlo Pierpaoli
- Laboratory on Quantitative Medical imaging, NIBIBNational Institutes of HealthBethesdaMarylandUSA
| | - Ivana Drobnjak
- Department of Computer ScienceUniversity College LondonLondonUK
| | - Alexander Leemans
- PROVIDI Lab, Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Kevin D. Harkins
- Vanderbilt University Institute of Imaging ScienceVanderbilt UniversityNashvilleTennesseeUSA
- Radiology and Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
- Biomedical EngineeringVanderbilt UniversityNashvilleTennesseeUSA
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaing Lab (SCIL), Computer Science DepartmentUniversité de SherbrookeSherbrookeQuebecCanada
- Imeka SolutionsSherbrookeQuebecCanada
| | - Duan Xu
- Department of Radiology and Biomedical ImagingUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Hao Huang
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Mathieu D. Santin
- Centre for NeuroImaging Research (CENIR), Inserm U 1127, CNRS UMR 7225Sorbonne UniversitéParisFrance
- Paris Brain InstituteParisFrance
| | - Samuel C. Grant
- Department of Chemical and Biomedical Engineering, FAMU‐FSU College of EngineeringFlorida State UniversityTallahasseeFloridaUSA
- Center for Interdisciplinary Magnetic ResonanceNational HIgh Magnetic Field LaboratoryTallahasseeFloridaUSA
| | - Andre Obenaus
- Division of Biomedical SciencesUniversity of California RiversideRiversideCaliforniaUSA
- Preclinical and Translational Imaging CenterUniversity of California IrvineIrvineCaliforniaUSA
| | - Gene S. Kim
- Department of RadiologyWeill Cornell Medical CollegeNew YorkNew YorkUSA
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument ScienceZhejiang UniversityHangzhouChina
| | - Denis Le Bihan
- CEA, DRF, JOLIOT, NeuroSpinGif‐sur‐YvetteFrance
- Université Paris‐SaclayGif‐sur‐YvetteFrance
| | - Stephen J. Blackband
- Department of NeuroscienceUniversity of FloridaGainesvilleFloridaUSA
- McKnight Brain InstituteUniversity of FloridaGainesvilleFloridaUSA
- National High Magnetic Field LaboratoryTallahasseeFloridaUSA
| | - Luisa Ciobanu
- NeuroSpin, UMR CEA/CNRS 9027Paris‐Saclay UniversityGif‐sur‐YvetteFrance
| | - Els Fieremans
- Department of RadiologyNew York University Grossman School of MedicineNew YorkNew YorkUSA
| | - Ruiliang Bai
- Interdisciplinary Institute of Neuroscience and Technology, School of MedicineZhejiang UniversityHangzhouChina
- Frontier Center of Brain Science and Brain‐Machine IntegrationZhejiang UniversityZhejiangChina
| | - Trygve B. Leergaard
- Department of Molecular Biology, Institute of Basic Medical SciencesUniversity of OsloOsloNorway
| | - Jiangyang Zhang
- Department of RadiologyNew York University School of MedicineNew YorkNew YorkUSA
| | - Tim B. Dyrby
- Danish Research Centre for Magnetic ResonanceCentre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and HvidovreHvidovreDenmark
- Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKongens LyngbyDenmark
| | - G. Allan Johnson
- Duke Center for In Vivo Microscopy, Department of RadiologyDuke UniversityDurhamNorth CarolinaUSA
- Department of Biomedical EngineeringDuke UniversityDurhamNorth CarolinaUSA
| | - Julien Cohen‐Adad
- NeuroPoly Lab, Institute of Biomedical EngineeringPolytechnique MontrealMontrealQuebecCanada
- Functional Neuroimaging Unit, CRIUGMUniversity of MontrealMontrealQuebecCanada
- Mila ‐ Quebec AI InstituteMontrealQuebecCanada
| | - Matthew D. Budde
- Department of NeurosurgeryMedical College of WisconsinMilwaukeeWisconsinUSA
- Clement J Zablocki VA Medical CenterMilwaukeeWisconsinUSA
| | - Kurt G. Schilling
- Vanderbilt University Institute of Imaging ScienceVanderbilt UniversityNashvilleTennesseeUSA
- Radiology and Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
| |
Collapse
|
2
|
Ojha A, Tommasin S, Piervincenzi C, Baione V, Gangemi E, Gallo A, d'Ambrosio A, Altieri M, De Stefano N, Cortese R, Valsasina P, Tedone N, Pozzilli C, Rocca MA, Filippi M, Pantano P. Clinical and MRI features contributing to the clinico-radiological dissociation in a large cohort of people with multiple sclerosis. J Neurol 2025; 272:327. [PMID: 40204954 PMCID: PMC11982092 DOI: 10.1007/s00415-025-12977-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2024] [Revised: 02/11/2025] [Accepted: 02/14/2025] [Indexed: 04/11/2025]
Abstract
BACKGROUND People with Multiple Sclerosis (PwMS) often show a mismatch between disability and T2-hyperintense white matter (WM) lesion volume (LV), that in general is referred to as the clinico-radiological paradox. OBJECTIVES This study aimed to understand how an extensive clinical, neuropsychological, and MRI analysis could better elucidate the clinico-radiological dissociation in a large cohort of PwMS. METHODS Clinical scores, such as Expanded Disability Status Scale (EDSS), 9 Hole Peg Test (9HPT), 25-foot Walking Test (25-FWT), Paced Auditory Serial Addition Test at 3 s (PASAT3), Symbol digit Modalities Test (SDMT), demographics, and 3 T-MRI of 717 PwMS and 284 healthy subjects (HS) were downloaded from the INNI database. Considering medians of LV and EDSS scores, PwMS were divided into four groups: low LV and disability (LL/LD); high LV and low disability (HL/LD); low LV and high disability (LL/HD); high LV and disability (HL/HD). MRI measures included: volumes of gray matter (GM), WM, cerebellum, basal ganglia and thalamus, spinal cord (SC) area, and functional connectivity of resting-state networks. RESULTS The clinico-radiological dissociation involved 36% of our sample. HL/LD showed worse SDMT scores and lower global and deep GM volumes than HS and LL/LD. LL/HD showed lower GM, thalamus, and cerebellum volumes, and SC area than HS, and lower SC area than LL/LD. CONCLUSIONS A more extensive clinical assessment, including cognitive tests, and MRI evaluation including deep GM and SC, could better describe the real status of the disease and help clinicians in early and tailored treatment in PwMS.
Collapse
Affiliation(s)
- Abhineet Ojha
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Silvia Tommasin
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy.
- Unicamillus-Saint Camillus International University of Health Sciences, Rome, Italy.
| | | | - Viola Baione
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Emma Gangemi
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Antonio Gallo
- Department of Advanced Medical and Surgical Sciences, 3t MRI‑Center, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Alessandro d'Ambrosio
- Department of Advanced Medical and Surgical Sciences, 3t MRI‑Center, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Manuela Altieri
- Department of Advanced Medical and Surgical Sciences, 3t MRI‑Center, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Rosa Cortese
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Nicolò Tedone
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Carlo Pozzilli
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Patrizia Pantano
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- IRCSS NEUROMED, Pozzilli, Italy
| |
Collapse
|
3
|
Bédard S, Karthik EN, Tsagkas C, Pravatà E, Granziera C, Smith A, Weber Ii KA, Cohen-Adad J. Towards contrast-agnostic soft segmentation of the spinal cord. Med Image Anal 2025; 101:103473. [PMID: 39874684 DOI: 10.1016/j.media.2025.103473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 01/08/2025] [Accepted: 01/13/2025] [Indexed: 01/30/2025]
Abstract
Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis. While several semi and automatic methods exist, one key limitation remains: the segmentation depends on the MRI contrast, resulting in different CSA across contrasts. This is partly due to the varying appearance of the boundary between the spinal cord and the cerebrospinal fluid that depends on the sequence and acquisition parameters. This contrast-sensitive CSA adds variability in multi-center studies where protocols can vary, reducing the sensitivity to detect subtle atrophies. Moreover, existing methods enhance the CSA variability by training one model per contrast, while also producing binary masks that do not account for partial volume effects. In this work, we present a deep learning-based method that produces soft segmentations of the spinal cord that are stable across MRI contrasts. Using the Spine Generic Public Database of healthy participants (n=267; contrasts=6), we first generated participant-wise soft ground truth (GT) by averaging the binary segmentations across all 6 contrasts. These soft GT, along with aggressive data augmentation and a regression-based loss function, were then used to train a U-Net model for spinal cord segmentation. We evaluated our model against state-of-the-art methods and performed ablation studies involving different GT mask types, loss functions, contrast-specific models and domain generalization methods. Our results show that using the soft average segmentations along with a regression loss function reduces CSA variability (p<0.05, Wilcoxon signed-rank test). The proposed spinal cord segmentation model generalizes better than the state-of-the-art contrast-specific methods amongst unseen datasets, vendors, contrasts, and pathologies (compression, lesions), while accounting for partial volume effects. Our model is integrated into the Spinal Cord Toolbox (v6.2 and higher).
Collapse
Affiliation(s)
- Sandrine Bédard
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, Québec, Canada.
| | - Enamundram Naga Karthik
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, Québec, Canada; Mila - Québec Artificial Intelligence Institute, Montréal, Québec, Canada.
| | - Charidimos Tsagkas
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Emanuele Pravatà
- Neuroradiology Department, Neurocenter of Southern Switzerland, Ospedale Regionale di Lugano, Lugano, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Andrew Smith
- Department of Physical Medicine and Rehabilitation Physical Therapy Program, University of Colorado School of Medicine, Aurora, CO, USA
| | - Kenneth Arnold Weber Ii
- Division of Pain Medicine, Department of Anesthesiology, Perioperative, and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, Québec, Canada; Mila - Québec Artificial Intelligence Institute, Montréal, Québec, Canada; Functional Neuroimaging Unit, CRIUGM, University of Montreal, Montreal, Québec, Canada; Centre de recherche du CHU Sainte-Justine, Université de Montréal, Montréal, Québec, Canada.
| |
Collapse
|
4
|
Qiu Z, Liu T, Zeng C, Yang M, Yang H, Xu X. Exploratory study on the ascending pain pathway in patients with chronic neck and shoulder pain based on combined brain and spinal cord diffusion tensor imaging. Front Neurosci 2025; 19:1460881. [PMID: 40012685 PMCID: PMC11861079 DOI: 10.3389/fnins.2025.1460881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2024] [Accepted: 01/27/2025] [Indexed: 02/28/2025] Open
Abstract
Objective To explore the changes in the white matter microstructure of the ascending pain conduction pathways in patients with chronic neck and shoulder pain (CNSP) using combined brain and spinal cord diffusion tensor imaging techniques, and to assess its correlation with clinical indicators and cognitive functions. Materials and methods A 3.0T MRI scanner was used to perform combined brain and spinal cord diffusion tensor imaging scans on 31 CNSP patients and 24 healthy controls (HCs), extracting the spinothalamic tract (STT) and quantitatively analyzing the fractional anisotropy (FA) and mean diffusivity (MD) which reflect the microstructural integrity of nerve fibers. Additionally, these differences were subjected to partial correlation analysis in relation to Visual Analog Scale (VAS) scores, duration of pain, Self-Rating Anxiety Scale (SAS), and Self-Rating Depression Scale (SDS). Results Compared to HCs, CNSP patients showed decreased mean FA values and increased mean MD values in bilateral intracranial STT compared to the HC group, but two-sample t-test results indicated no statistically significant differences (p > 0.05). FA values of the left STT (C2 segment, C5 segment) and right STT (C1 segment, C2 segment) were significantly decreased in bilateral cervical STTs of CNSP patients; MD values of the left STT (C1 segment, C2 segment, C5 segment) and right STT (C1 segment, C5 segment) were significantly increased (p < 0.05). Partial correlation analysis results showed that FA values of STT in CNSP patients were negatively correlated with VAS scores, duration of pain, SAS scores, and SDS scores, while MD values were positively correlated with VAS scores and duration of pain (Bonferroni p < 0.05). Conclusion This research identified that patients with CNSP exhibited reduced mean FA and increased mean MD in the bilateral intracranial STT, although these differences were not statistically significant (p > 0.05). Conversely, significant abnormalities were observed in specific segments of the bilateral cervical STT (p < 0.05), which were also correlated with variations in pain intensity, illness duration, and levels of anxiety and depression. These findings contribute a novel neuroimaging perspective to the evaluation and elucidation of the pathophysiological mechanisms underlying chronic pain in the ascending conduction pathways.
Collapse
Affiliation(s)
- Zhiqiang Qiu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Tianci Liu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Chengxi Zeng
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Maojiang Yang
- Department of Pain, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - HongYing Yang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| | - Xiaoxue Xu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong, China
| |
Collapse
|
5
|
Naga Karthik E, Valošek J, Smith AC, Pfyffer D, Schading-Sassenhausen S, Farner L, Weber KA, Freund P, Cohen-Adad J. SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans. Radiol Artif Intell 2025; 7:e240005. [PMID: 39503603 PMCID: PMC11791505 DOI: 10.1148/ryai.240005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 09/11/2024] [Accepted: 10/07/2024] [Indexed: 11/13/2024]
Abstract
Purpose To develop a deep learning tool for the automatic segmentation of the spinal cord and intramedullary lesions in spinal cord injury (SCI) on T2-weighted MRI scans. Materials and Methods This retrospective study included MRI data acquired between July 2002 and February 2023. The data consisted of T2-weighted MRI scans acquired using different scanner manufacturers with various image resolutions (isotropic and anisotropic) and orientations (axial and sagittal). Patients had different lesion etiologies (traumatic, ischemic, and hemorrhagic) and lesion locations across the cervical, thoracic, and lumbar spine. A deep learning model, SCIseg (which is open source and accessible through the Spinal Cord Toolbox, version 6.2 and above), was trained in a three-phase process involving active learning for the automatic segmentation of intramedullary SCI lesions and the spinal cord. The segmentations from the proposed model were visually and quantitatively compared with those from three other open-source methods (PropSeg, DeepSeg, and contrast-agnostic, all part of the Spinal Cord Toolbox). The Wilcoxon signed rank test was used to compare quantitative MRI biomarkers of SCI (lesion volume, lesion length, and maximal axial damage ratio) derived from the manual reference standard lesion masks and biomarkers obtained automatically with SCIseg segmentations. Results The study included 191 patients with SCI (mean age, 48.1 years ± 17.9 [SD]; 142 [74%] male patients). SCIseg achieved a mean Dice score of 0.92 ± 0.07 and 0.61 ± 0.27 for spinal cord and SCI lesion segmentation, respectively. There was no evidence of a difference between lesion length (P = .42) and maximal axial damage ratio (P = .16) computed from manually annotated lesions and the lesion segmentations obtained using SCIseg. Conclusion SCIseg accurately segmented intramedullary lesions on a diverse dataset of T2-weighted MRI scans and automatically extracted clinically relevant lesion characteristics. Keywords: Spinal Cord, Trauma, Segmentation, MR Imaging, Supervised Learning, Convolutional Neural Network (CNN) Published under a CC BY 4.0 license.
Collapse
Affiliation(s)
| | | | - Andrew C. Smith
- From the NeuroPoly Laboratory, Institute of Biomedical Engineering,
Polytechnique Montréal, 2500 Chemin de Polytechnique, Montréal,
Québec, Canada H3T 1J4 (E.N.K., J.V., J.C.A.); Mila-Quebec AI Institute,
Montréal, Québec, Canada (E.N.K., J.V., J.C.A.); Department of
Neurosurgery and Department of Neurology, Faculty of Medicine and Dentistry,
Palacký University Olomouc, Olomouc, Czechia (J.V.); Department of
Physical Medicine and Rehabilitation Physical Therapy Program, University of
Colorado School of Medicine, Aurora, Colo (A.C.S.); Spinal Cord Injury Center,
Balgrist University Hospital, University of Zürich, Zürich,
Switzerland (D.P., S.S.S., L.F., P.F.); Department of Anesthesiology,
Perioperative and Pain Medicine, Stanford University School of Medicine,
Stanford, Calif (D.P., K.A.W.); Department of Neurophysics, Max Planck Institute
for Human Cognitive and Brain Sciences, Leipzig, Germany (P.F.); and Functional
Neuroimaging Unit, CRIUGM and Centre de Recherche du CHU Sainte-Justine,
Université de Montréal, Montréal, Québec, Canada
(J.C.A.)
| | - Dario Pfyffer
- From the NeuroPoly Laboratory, Institute of Biomedical Engineering,
Polytechnique Montréal, 2500 Chemin de Polytechnique, Montréal,
Québec, Canada H3T 1J4 (E.N.K., J.V., J.C.A.); Mila-Quebec AI Institute,
Montréal, Québec, Canada (E.N.K., J.V., J.C.A.); Department of
Neurosurgery and Department of Neurology, Faculty of Medicine and Dentistry,
Palacký University Olomouc, Olomouc, Czechia (J.V.); Department of
Physical Medicine and Rehabilitation Physical Therapy Program, University of
Colorado School of Medicine, Aurora, Colo (A.C.S.); Spinal Cord Injury Center,
Balgrist University Hospital, University of Zürich, Zürich,
Switzerland (D.P., S.S.S., L.F., P.F.); Department of Anesthesiology,
Perioperative and Pain Medicine, Stanford University School of Medicine,
Stanford, Calif (D.P., K.A.W.); Department of Neurophysics, Max Planck Institute
for Human Cognitive and Brain Sciences, Leipzig, Germany (P.F.); and Functional
Neuroimaging Unit, CRIUGM and Centre de Recherche du CHU Sainte-Justine,
Université de Montréal, Montréal, Québec, Canada
(J.C.A.)
| | - Simon Schading-Sassenhausen
- From the NeuroPoly Laboratory, Institute of Biomedical Engineering,
Polytechnique Montréal, 2500 Chemin de Polytechnique, Montréal,
Québec, Canada H3T 1J4 (E.N.K., J.V., J.C.A.); Mila-Quebec AI Institute,
Montréal, Québec, Canada (E.N.K., J.V., J.C.A.); Department of
Neurosurgery and Department of Neurology, Faculty of Medicine and Dentistry,
Palacký University Olomouc, Olomouc, Czechia (J.V.); Department of
Physical Medicine and Rehabilitation Physical Therapy Program, University of
Colorado School of Medicine, Aurora, Colo (A.C.S.); Spinal Cord Injury Center,
Balgrist University Hospital, University of Zürich, Zürich,
Switzerland (D.P., S.S.S., L.F., P.F.); Department of Anesthesiology,
Perioperative and Pain Medicine, Stanford University School of Medicine,
Stanford, Calif (D.P., K.A.W.); Department of Neurophysics, Max Planck Institute
for Human Cognitive and Brain Sciences, Leipzig, Germany (P.F.); and Functional
Neuroimaging Unit, CRIUGM and Centre de Recherche du CHU Sainte-Justine,
Université de Montréal, Montréal, Québec, Canada
(J.C.A.)
| | - Lynn Farner
- From the NeuroPoly Laboratory, Institute of Biomedical Engineering,
Polytechnique Montréal, 2500 Chemin de Polytechnique, Montréal,
Québec, Canada H3T 1J4 (E.N.K., J.V., J.C.A.); Mila-Quebec AI Institute,
Montréal, Québec, Canada (E.N.K., J.V., J.C.A.); Department of
Neurosurgery and Department of Neurology, Faculty of Medicine and Dentistry,
Palacký University Olomouc, Olomouc, Czechia (J.V.); Department of
Physical Medicine and Rehabilitation Physical Therapy Program, University of
Colorado School of Medicine, Aurora, Colo (A.C.S.); Spinal Cord Injury Center,
Balgrist University Hospital, University of Zürich, Zürich,
Switzerland (D.P., S.S.S., L.F., P.F.); Department of Anesthesiology,
Perioperative and Pain Medicine, Stanford University School of Medicine,
Stanford, Calif (D.P., K.A.W.); Department of Neurophysics, Max Planck Institute
for Human Cognitive and Brain Sciences, Leipzig, Germany (P.F.); and Functional
Neuroimaging Unit, CRIUGM and Centre de Recherche du CHU Sainte-Justine,
Université de Montréal, Montréal, Québec, Canada
(J.C.A.)
| | - Kenneth A. Weber
- From the NeuroPoly Laboratory, Institute of Biomedical Engineering,
Polytechnique Montréal, 2500 Chemin de Polytechnique, Montréal,
Québec, Canada H3T 1J4 (E.N.K., J.V., J.C.A.); Mila-Quebec AI Institute,
Montréal, Québec, Canada (E.N.K., J.V., J.C.A.); Department of
Neurosurgery and Department of Neurology, Faculty of Medicine and Dentistry,
Palacký University Olomouc, Olomouc, Czechia (J.V.); Department of
Physical Medicine and Rehabilitation Physical Therapy Program, University of
Colorado School of Medicine, Aurora, Colo (A.C.S.); Spinal Cord Injury Center,
Balgrist University Hospital, University of Zürich, Zürich,
Switzerland (D.P., S.S.S., L.F., P.F.); Department of Anesthesiology,
Perioperative and Pain Medicine, Stanford University School of Medicine,
Stanford, Calif (D.P., K.A.W.); Department of Neurophysics, Max Planck Institute
for Human Cognitive and Brain Sciences, Leipzig, Germany (P.F.); and Functional
Neuroimaging Unit, CRIUGM and Centre de Recherche du CHU Sainte-Justine,
Université de Montréal, Montréal, Québec, Canada
(J.C.A.)
| | - Patrick Freund
- From the NeuroPoly Laboratory, Institute of Biomedical Engineering,
Polytechnique Montréal, 2500 Chemin de Polytechnique, Montréal,
Québec, Canada H3T 1J4 (E.N.K., J.V., J.C.A.); Mila-Quebec AI Institute,
Montréal, Québec, Canada (E.N.K., J.V., J.C.A.); Department of
Neurosurgery and Department of Neurology, Faculty of Medicine and Dentistry,
Palacký University Olomouc, Olomouc, Czechia (J.V.); Department of
Physical Medicine and Rehabilitation Physical Therapy Program, University of
Colorado School of Medicine, Aurora, Colo (A.C.S.); Spinal Cord Injury Center,
Balgrist University Hospital, University of Zürich, Zürich,
Switzerland (D.P., S.S.S., L.F., P.F.); Department of Anesthesiology,
Perioperative and Pain Medicine, Stanford University School of Medicine,
Stanford, Calif (D.P., K.A.W.); Department of Neurophysics, Max Planck Institute
for Human Cognitive and Brain Sciences, Leipzig, Germany (P.F.); and Functional
Neuroimaging Unit, CRIUGM and Centre de Recherche du CHU Sainte-Justine,
Université de Montréal, Montréal, Québec, Canada
(J.C.A.)
| | - Julien Cohen-Adad
- From the NeuroPoly Laboratory, Institute of Biomedical Engineering,
Polytechnique Montréal, 2500 Chemin de Polytechnique, Montréal,
Québec, Canada H3T 1J4 (E.N.K., J.V., J.C.A.); Mila-Quebec AI Institute,
Montréal, Québec, Canada (E.N.K., J.V., J.C.A.); Department of
Neurosurgery and Department of Neurology, Faculty of Medicine and Dentistry,
Palacký University Olomouc, Olomouc, Czechia (J.V.); Department of
Physical Medicine and Rehabilitation Physical Therapy Program, University of
Colorado School of Medicine, Aurora, Colo (A.C.S.); Spinal Cord Injury Center,
Balgrist University Hospital, University of Zürich, Zürich,
Switzerland (D.P., S.S.S., L.F., P.F.); Department of Anesthesiology,
Perioperative and Pain Medicine, Stanford University School of Medicine,
Stanford, Calif (D.P., K.A.W.); Department of Neurophysics, Max Planck Institute
for Human Cognitive and Brain Sciences, Leipzig, Germany (P.F.); and Functional
Neuroimaging Unit, CRIUGM and Centre de Recherche du CHU Sainte-Justine,
Université de Montréal, Montréal, Québec, Canada
(J.C.A.)
| |
Collapse
|
6
|
Luchetti L, Prados F, Cortese R, Gentile G, Calabrese M, Mortilla M, De Stefano N, Battaglini M. Evaluation of cervical spinal cord atrophy using a modified SIENA approach. Neuroimage 2024; 298:120775. [PMID: 39106936 DOI: 10.1016/j.neuroimage.2024.120775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 07/12/2024] [Accepted: 08/02/2024] [Indexed: 08/09/2024] Open
Abstract
Spinal cord (SC) atrophy obtained from structural magnetic resonance imaging has gained relevance as an indicator of neurodegeneration in various neurological disorders. The common method to assess SC atrophy is by comparing numerical differences of the cross-sectional spinal cord area (CSA) between time points. However, this indirect approach leads to considerable variability in the obtained results. Studies showed that this limitation can be overcome by using a registration-based technique. The present study introduces the Structural Image Evaluation using Normalization of Atrophy on the Spinal Cord (SIENA-SC), which is an adapted version of the original SIENA method, designed to directly calculate the percentage of SC volume change over time from clinical brain MRI acquired with an extended field of view to cover the superior part of the cervical SC. In this work, we compared SIENA-SC with the Generalized Boundary Shift Integral (GBSI) and the CSA change. On a scan-rescan dataset, SIENA-SC was shown to have the lowest measurement error than the other two methods. When comparing a group of 190 Healthy Controls with a group of 65 Multiple Sclerosis patients, SIENA-SC provided significantly higher yearly rates of atrophy in patients than in controls and a lower sample size when measured for treatment effect sizes of 50%, 30% and 10%. Our findings indicate that SIENA-SC is a robust, reproducible, and sensitive approach for assessing longitudinal changes in spinal cord volume, providing neuroscientists with an accessible and automated tool able to reduce the need for manual intervention and minimize variability in measurements.
Collapse
Affiliation(s)
- Ludovico Luchetti
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy; Siena Imaging S.r.l., Siena, Italy
| | - Ferran Prados
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom; Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering Department, University College London, London, United Kingdom; e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Rosa Cortese
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | | | - Massimilano Calabrese
- Department of Neuroscience, Biomedicine and Movements, The Multiple Sclerosis Center of the University Hospital of Verona, Verona, Italy
| | - Marzia Mortilla
- Anna Meyer Children's University Hospital-IRCCS, Florence, Italy
| | - Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Marco Battaglini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy; Siena Imaging S.r.l., Siena, Italy.
| |
Collapse
|
7
|
Al-Shawwa A, Ost K, Anderson D, Cho N, Evaniew N, Jacobs WB, Martin AR, Gaekwad R, Tripathy S, Bouchard J, Casha S, Cho R, duPlessis S, Lewkonia P, Nicholls F, Salo PT, Soroceanu A, Swamy G, Thomas KC, Yang MMH, Cohen-Adad J, Cadotte DW. Advanced MRI metrics improve the prediction of baseline disease severity for individuals with degenerative cervical myelopathy. Spine J 2024; 24:1605-1614. [PMID: 38679077 DOI: 10.1016/j.spinee.2024.04.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 04/05/2024] [Accepted: 04/23/2024] [Indexed: 05/01/2024]
Abstract
BACKGROUND CONTEXT Degenerative cervical myelopathy (DCM) is the most common form of atraumatic spinal cord injury globally. Degeneration of spinal discs, bony osteophyte growth and ligament pathology results in physical compression of the spinal cord contributing to damage of white matter tracts and grey matter cellular populations. This results in an insidious neurological and functional decline in patients which can lead to paralysis. Magnetic resonance imaging (MRI) confirms the diagnosis of DCM and is a prerequisite to surgical intervention, the only known treatment for this disorder. Unfortunately, there is a weak correlation between features of current commonly acquired MRI scans ("community MRI, cMRI") and the degree of disability experienced by a patient. PURPOSE This study examines the predictive ability of current MRI sequences relative to "advanced MRI" (aMRI) metrics designed to detect evidence of spinal cord injury secondary to degenerative myelopathy. We hypothesize that the utilization of higher fidelity aMRI scans will increase the effectiveness of machine learning models predicting DCM severity and may ultimately lead to a more efficient protocol for identifying patients in need of surgical intervention. STUDY DESIGN/SETTING Single institution analysis of imaging registry of patients with DCM. PATIENT SAMPLE A total of 296 patients in the cMRI group and 228 patients in the aMRI group. OUTCOME MEASURES Physiologic measures: accuracy of machine learning algorithms to detect severity of DCM assessed clinically based on the modified Japanese Orthopedic Association (mJOA) scale. METHODS Patients enrolled in the Canadian Spine Outcomes Research Network registry with DCM were screened and 296 cervical spine MRIs acquired in cMRI were compared with 228 aMRI acquisitions. aMRI acquisitions consisted of diffusion tensor imaging, magnetization transfer, T2-weighted, and T2*-weighted images. The cMRI group consisted of only T2-weighted MRI scans. Various machine learning models were applied to both MRI groups to assess accuracy of prediction of baseline disease severity assessed clinically using the mJOA scale for cervical myelopathy. RESULTS Through the utilization of Random Forest Classifiers, disease severity was predicted with 41.8% accuracy in cMRI scans and 73.3% in the aMRI scans. Across different predictive model variations tested, the aMRI scans consistently produced higher prediction accuracies compared to the cMRI counterparts. CONCLUSIONS aMRI metrics perform better in machine learning models at predicting disease severity of patients with DCM. Continued work is needed to refine these models and address DCM severity class imbalance concerns, ultimately improving model confidence for clinical implementation.
Collapse
Affiliation(s)
- Abdul Al-Shawwa
- Cumming School of Medicine, Hotchkiss Brain Institute, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta, T2N4N1, Canada
| | - Kalum Ost
- Cumming School of Medicine, Hotchkiss Brain Institute, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta, T2N4N1, Canada
| | - David Anderson
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, HMRB 231, 3330 Hospital Drive NW, Calgary, Alberta, T2N4N1, Canada
| | - Newton Cho
- Department of Neurosurgery, University of Toronto,149 College Street, 5th Floor, Toronto, Ontario, M5T1P5, Canada
| | - Nathan Evaniew
- Combined Orthopedic and Neurosurgery Spine Program, University of Calgary, 1409 29 Street NW, Calgary, Alberta, T2N2T9, Canada; Section of Orthopaedic Surgery, Department of Surgery, University of Calgary, 1403 29 Street NW, T2N2T9, Calgary, Alberta, T2N2T9, Canada
| | - W Bradley Jacobs
- Combined Orthopedic and Neurosurgery Spine Program, University of Calgary, 1409 29 Street NW, Calgary, Alberta, T2N2T9, Canada; Department of Clinical Neurosciences, Section of Neurosurgery, Cumming School of Medicine, University of Calgary, 1403 29th Street NW, Calgary, Alberta, T2N2T9, Canada
| | - Allan R Martin
- Department of Neurological Surgery, University of California - Davis, 3301 C Street, Suite 1500, Sacramento, CA, 95816, USA
| | - Ranjeet Gaekwad
- Department of Clinical Neurosciences, Section of Neurosurgery, Cumming School of Medicine, University of Calgary, 1403 29th Street NW, Calgary, Alberta, T2N2T9, Canada
| | - Saswati Tripathy
- Combined Orthopedic and Neurosurgery Spine Program, University of Calgary, 1409 29 Street NW, Calgary, Alberta, T2N2T9, Canada
| | - Jacques Bouchard
- Combined Orthopedic and Neurosurgery Spine Program, University of Calgary, 1409 29 Street NW, Calgary, Alberta, T2N2T9, Canada; Section of Orthopaedic Surgery, Department of Surgery, University of Calgary, 1403 29 Street NW, T2N2T9, Calgary, Alberta, T2N2T9, Canada
| | - Steve Casha
- Combined Orthopedic and Neurosurgery Spine Program, University of Calgary, 1409 29 Street NW, Calgary, Alberta, T2N2T9, Canada; Department of Clinical Neurosciences, Section of Neurosurgery, Cumming School of Medicine, University of Calgary, 1403 29th Street NW, Calgary, Alberta, T2N2T9, Canada
| | - Roger Cho
- Combined Orthopedic and Neurosurgery Spine Program, University of Calgary, 1409 29 Street NW, Calgary, Alberta, T2N2T9, Canada; Section of Orthopaedic Surgery, Department of Surgery, University of Calgary, 1403 29 Street NW, T2N2T9, Calgary, Alberta, T2N2T9, Canada
| | - Stephen duPlessis
- Combined Orthopedic and Neurosurgery Spine Program, University of Calgary, 1409 29 Street NW, Calgary, Alberta, T2N2T9, Canada; Department of Clinical Neurosciences, Section of Neurosurgery, Cumming School of Medicine, University of Calgary, 1403 29th Street NW, Calgary, Alberta, T2N2T9, Canada
| | - Peter Lewkonia
- Combined Orthopedic and Neurosurgery Spine Program, University of Calgary, 1409 29 Street NW, Calgary, Alberta, T2N2T9, Canada; Section of Orthopaedic Surgery, Department of Surgery, University of Calgary, 1403 29 Street NW, T2N2T9, Calgary, Alberta, T2N2T9, Canada
| | - Fred Nicholls
- Combined Orthopedic and Neurosurgery Spine Program, University of Calgary, 1409 29 Street NW, Calgary, Alberta, T2N2T9, Canada; Section of Orthopaedic Surgery, Department of Surgery, University of Calgary, 1403 29 Street NW, T2N2T9, Calgary, Alberta, T2N2T9, Canada
| | - Paul T Salo
- Combined Orthopedic and Neurosurgery Spine Program, University of Calgary, 1409 29 Street NW, Calgary, Alberta, T2N2T9, Canada; Section of Orthopaedic Surgery, Department of Surgery, University of Calgary, 1403 29 Street NW, T2N2T9, Calgary, Alberta, T2N2T9, Canada
| | - Alex Soroceanu
- Combined Orthopedic and Neurosurgery Spine Program, University of Calgary, 1409 29 Street NW, Calgary, Alberta, T2N2T9, Canada; Section of Orthopaedic Surgery, Department of Surgery, University of Calgary, 1403 29 Street NW, T2N2T9, Calgary, Alberta, T2N2T9, Canada
| | - Ganesh Swamy
- Combined Orthopedic and Neurosurgery Spine Program, University of Calgary, 1409 29 Street NW, Calgary, Alberta, T2N2T9, Canada; Section of Orthopaedic Surgery, Department of Surgery, University of Calgary, 1403 29 Street NW, T2N2T9, Calgary, Alberta, T2N2T9, Canada
| | - Kenneth C Thomas
- Combined Orthopedic and Neurosurgery Spine Program, University of Calgary, 1409 29 Street NW, Calgary, Alberta, T2N2T9, Canada; Section of Orthopaedic Surgery, Department of Surgery, University of Calgary, 1403 29 Street NW, T2N2T9, Calgary, Alberta, T2N2T9, Canada
| | - Michael M H Yang
- Combined Orthopedic and Neurosurgery Spine Program, University of Calgary, 1409 29 Street NW, Calgary, Alberta, T2N2T9, Canada; Department of Clinical Neurosciences, Section of Neurosurgery, Cumming School of Medicine, University of Calgary, 1403 29th Street NW, Calgary, Alberta, T2N2T9, Canada
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Pavillon Lassonde 2700 Ch de la Tour, Montreal, Quebec, H3T1N8, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, 4565 Queen Mary Rd, Montreal, Quebec, H3W1W5, Canada; Mila - Quebec AI Institute, 6666 Saint-Urbain Street, #200, Montreal, Quebec, H2S3H1, Canada
| | - David W Cadotte
- Cumming School of Medicine, Hotchkiss Brain Institute, University of Calgary, 3330 Hospital Drive NW, Calgary, Alberta, T2N4N1, Canada; Combined Orthopedic and Neurosurgery Spine Program, University of Calgary, 1409 29 Street NW, Calgary, Alberta, T2N2T9, Canada; Department of Clinical Neurosciences, Section of Neurosurgery, Cumming School of Medicine, University of Calgary, 1403 29th Street NW, Calgary, Alberta, T2N2T9, Canada.
| |
Collapse
|
8
|
Valošek J, Cohen-Adad J. Reproducible Spinal Cord Quantitative MRI Analysis with the Spinal Cord Toolbox. Magn Reson Med Sci 2024; 23:307-315. [PMID: 38479843 PMCID: PMC11234946 DOI: 10.2463/mrms.rev.2023-0159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/02/2024] Open
Abstract
The spinal cord plays a pivotal role in the central nervous system, providing communication between the brain and the body and containing critical motor and sensory networks. Recent advancements in spinal cord MRI data acquisition and image analysis have shown a potential to improve the diagnostics, prognosis, and management of a variety of pathological conditions. In this review, we first discuss the significance of standardized spinal cord MRI acquisition protocol in multi-center and multi-manufacturer studies. Then, we cover open-access spinal cord MRI datasets, which are important for reproducible science and validation of new methods. Finally, we elaborate on the recent advances in spinal cord MRI data analysis techniques implemented in the open-source software package Spinal Cord Toolbox (SCT).
Collapse
Affiliation(s)
- Jan Valošek
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Department of Neurosurgery, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia
- Department of Neurology, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
- Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montreal, QC, Canada
| |
Collapse
|
9
|
Karthik EN, Valosek J, Smith AC, Pfyffer D, Schading-Sassenhausen S, Farner L, Weber KA, Freund P, Cohen-Adad J. SCIseg: Automatic Segmentation of T2-weighted Intramedullary Lesions in Spinal Cord Injury. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.03.24300794. [PMID: 38699309 PMCID: PMC11065035 DOI: 10.1101/2024.01.03.24300794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Purpose To develop a deep learning tool for the automatic segmentation of T2-weighted intramedullary lesions in spinal cord injury (SCI). Material and Methods This retrospective study included a cohort of SCI patients from three sites enrolled between July 2002 and February 2023. A deep learning model, SCIseg, was trained in a three-phase process involving active learning for the automatic segmentation of intramedullary SCI lesions and the spinal cord. The data consisted of T2-weighted MRI acquired using different scanner manufacturers with heterogeneous image resolutions (isotropic/anisotropic), orientations (axial/sagittal), lesion etiologies (traumatic/ischemic/hemorrhagic) and lesions spread across the cervical, thoracic and lumbar spine. The segmentations from the proposed model were visually and quantitatively compared with other open-source baselines. Wilcoxon signed-rank test was used to compare quantitative MRI biomarkers (lesion volume, lesion length, and maximal axial damage ratio) computed from manual lesion masks and those obtained automatically with SCIseg predictions. Results MRI data from 191 SCI patients (mean age, 48.1 years ± 17.9 [SD]; 142 males) were used for model training and evaluation. SCIseg achieved the best segmentation performance for both the cord and lesions. There was no statistically significant difference between lesion length and maximal axial damage ratio computed from manually annotated lesions and those obtained using SCIseg. Conclusion Automatic segmentation of intramedullary lesions commonly seen in SCI replaces the tedious manual annotation process and enables the extraction of relevant lesion morphometrics in large cohorts. The proposed model segments lesions across different etiologies, scanner manufacturers, and heterogeneous image resolutions. SCIseg is open-source and accessible through the Spinal Cord Toolbox.
Collapse
Affiliation(s)
- Enamundram Naga Karthik
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
| | - Jan Valosek
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Department of Neurosurgery, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia
- Department of Neurology, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czechia
| | - Andrew C. Smith
- Department of Physical Medicine and Rehabilitation Physical Therapy Program, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Dario Pfyffer
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA
| | | | - Lynn Farner
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland
| | - Kenneth A. Weber
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Patrick Freund
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zürich, Zürich, Switzerland
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
- Centre de Recherche du CHU Sainte-Justine, Université de Montréal, Montreal, QC, Canada
| |
Collapse
|
10
|
Stuart CM, Varatharaj A, Zou Y, Darekar A, Domjan J, Gandini Wheeler-Kingshott CAM, Perry VH, Galea I. Systemic inflammation associates with and precedes cord atrophy in progressive multiple sclerosis. Brain Commun 2024; 6:fcae143. [PMID: 38712323 PMCID: PMC11073756 DOI: 10.1093/braincomms/fcae143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/05/2024] [Accepted: 04/17/2024] [Indexed: 05/08/2024] Open
Abstract
In preclinical models of multiple sclerosis, systemic inflammation has an impact on the compartmentalized inflammatory process within the central nervous system and results in axonal loss. It remains to be shown whether this is the case in humans, specifically whether systemic inflammation contributes to spinal cord or brain atrophy in multiple sclerosis. Hence, an observational longitudinal study was conducted to delineate the relationship between systemic inflammation and atrophy using magnetic resonance imaging: the SIMS (Systemic Inflammation in Multiple Sclerosis) study. Systemic inflammation and progression were assessed in people with progressive multiple sclerosis (n = 50) over two and a half years. Eligibility criteria included: (i) primary or secondary progressive multiple sclerosis; (ii) age ≤ 70; and (iii) Expanded Disability Status Scale ≤ 6.5. First morning urine was collected weekly to quantify systemic inflammation by measuring the urinary neopterin-to-creatinine ratio using a validated ultra-performance liquid chromatography mass spectrometry technique. The urinary neopterin-to-creatinine ratio temporal profile was characterized by short-term responses overlaid on a background level of inflammation, so these two distinct processes were considered as separate variables: background inflammation and inflammatory response. Participants underwent MRI at the start and end of the study, to measure cervical spinal cord and brain atrophy. Brain and cervical cord atrophy occurred on the study, but the most striking change was seen in the cervical spinal cord, in keeping with the corticospinal tract involvement that is typical of progressive disease. Systemic inflammation predicted cervical cord atrophy. An association with brain atrophy was not observed in this cohort. A time lag between systemic inflammation and cord atrophy was evident, suggesting but not proving causation. The association of the inflammatory response with cord atrophy depended on the level of background inflammation, in keeping with experimental data in preclinical models where the effects of a systemic inflammatory challenge on tissue injury depended on prior exposure to inflammation. A higher inflammatory response was associated with accelerated cord atrophy in the presence of background systemic inflammation below the median for the study population. Higher background inflammation, while associated with cervical cord atrophy itself, subdued the association of the inflammatory response with cord atrophy. Findings were robust to sensitivity analyses adjusting for potential confounders and excluding cases with new lesion formation. In conclusion, systemic inflammation associates with, and precedes, multiple sclerosis progression. Further work is needed to prove causation since targeting systemic inflammation may offer novel treatment strategies for slowing neurodegeneration in multiple sclerosis.
Collapse
Affiliation(s)
- Charlotte M Stuart
- Clinical Neurosciences, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK
| | - Aravinthan Varatharaj
- Clinical Neurosciences, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK
- Wessex Neurological Centre, University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK
| | - Yukai Zou
- Clinical Neurosciences, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK
- Department of Medical Physics, University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK
| | - Angela Darekar
- Department of Medical Physics, University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK
| | - Janine Domjan
- Wessex Neurological Centre, University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK
| | - Claudia A M Gandini Wheeler-Kingshott
- Department of Neuroinflammation, Faculty of Brain Sciences, NMR Research Unit, Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London, London WC1B 5EH, UK
| | - V Hugh Perry
- School of Biological Sciences, University of Southampton, Southampton SO16 6YD, UK
| | - Ian Galea
- Clinical Neurosciences, Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK
- Wessex Neurological Centre, University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK
| |
Collapse
|
11
|
Kowalczyk OS, Medina S, Tsivaka D, McMahon SB, Williams SCR, Brooks JCW, Lythgoe DJ, Howard MA. Spinal fMRI demonstrates segmental organisation of functionally connected networks in the cervical spinal cord: A test-retest reliability study. Hum Brain Mapp 2024; 45:e26600. [PMID: 38339896 PMCID: PMC10831202 DOI: 10.1002/hbm.26600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 12/21/2023] [Accepted: 01/04/2024] [Indexed: 02/12/2024] Open
Abstract
Resting functional magnetic resonance imaging (fMRI) studies have identified intrinsic spinal cord activity, which forms organised motor (ventral) and sensory (dorsal) resting-state networks. However, to facilitate the use of spinal fMRI in, for example, clinical studies, it is crucial to first assess the reliability of the method, particularly given the unique anatomical, physiological, and methodological challenges associated with acquiring the data. Here, we characterise functional connectivity relationships in the cervical cord and assess their between-session test-retest reliability in 23 young healthy volunteers. Resting-state networks were estimated in two ways (1) by estimating seed-to-voxel connectivity maps and (2) by calculating seed-to-seed correlations. Seed regions corresponded to the four grey matter horns (ventral/dorsal and left/right) of C5-C8 segmental levels. Test-retest reliability was assessed using the intraclass correlation coefficient. Spatial overlap of clusters derived from seed-to-voxel analysis between sessions was examined using Dice coefficients. Following seed-to-voxel analysis, we observed distinct unilateral dorsal and ventral organisation of cervical spinal resting-state networks that was largely confined in the rostro-caudal extent to each spinal segmental level, with more sparse connections observed between segments. Additionally, strongest correlations were observed between within-segment ipsilateral dorsal-ventral connections, followed by within-segment dorso-dorsal and ventro-ventral connections. Test-retest reliability of these networks was mixed. Reliability was poor when assessed on a voxelwise level, with more promising indications of reliability when examining the average signal within clusters. Reliability of correlation strength between seeds was highly variable, with the highest reliability achieved in ipsilateral dorsal-ventral and dorso-dorsal/ventro-ventral connectivity. However, the spatial overlap of networks between sessions was excellent. We demonstrate that while test-retest reliability of cervical spinal resting-state networks is mixed, their spatial extent is similar across sessions, suggesting that these networks are characterised by a consistent spatial representation over time.
Collapse
Affiliation(s)
- Olivia S. Kowalczyk
- Department of Neuroimaging, Institute of Psychology, Psychiatry & NeuroscienceKing's College LondonLondonUK
- The Wellcome Centre for Human Neuroimaging, Queen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Sonia Medina
- Department of Neuroimaging, Institute of Psychology, Psychiatry & NeuroscienceKing's College LondonLondonUK
| | - Dimitra Tsivaka
- Department of Neuroimaging, Institute of Psychology, Psychiatry & NeuroscienceKing's College LondonLondonUK
- Medical Physics Department, Medical SchoolUniversity of ThessalyLarisaGreece
| | | | - Steven C. R. Williams
- Department of Neuroimaging, Institute of Psychology, Psychiatry & NeuroscienceKing's College LondonLondonUK
| | | | - David J. Lythgoe
- Department of Neuroimaging, Institute of Psychology, Psychiatry & NeuroscienceKing's College LondonLondonUK
| | - Matthew A. Howard
- Department of Neuroimaging, Institute of Psychology, Psychiatry & NeuroscienceKing's College LondonLondonUK
| |
Collapse
|
12
|
Zhao J, Sun L, Sun Z, Zhou X, Si H, Zhang D. MSEF-Net: Multi-scale edge fusion network for lumbosacral plexus segmentation with MR image. Artif Intell Med 2024; 148:102771. [PMID: 38325928 DOI: 10.1016/j.artmed.2024.102771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Revised: 12/08/2023] [Accepted: 01/14/2024] [Indexed: 02/09/2024]
Abstract
Nerve damage of spine areas is a common cause of disability and paralysis. The lumbosacral plexus segmentation from magnetic resonance imaging (MRI) scans plays an important role in many computer-aided diagnoses and surgery of spinal nerve lesions. Due to the complex structure and low contrast of the lumbosacral plexus, it is difficult to delineate the regions of edges accurately. To address this issue, we propose a Multi-Scale Edge Fusion Network (MSEF-Net) to fully enhance the edge feature in the encoder and adaptively fuse multi-scale features in the decoder. Specifically, to highlight the edge structure feature, we propose an edge feature fusion module (EFFM) by combining the Sobel operator edge detection and the edge-guided attention module (EAM), respectively. To adaptively fuse the multi-scale feature map in the decoder, we introduce an adaptive multi-scale fusion module (AMSF). Our proposed MSEF-Net method was evaluated on the collected spinal MRI dataset with 89 patients (a total of 2848 MR images). Experimental results demonstrate that our MSEF-Net is effective for lumbosacral plexus segmentation with MR images, when compared with several state-of-the-art segmentation methods.
Collapse
Affiliation(s)
- Junyong Zhao
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, the Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing 211106, China
| | - Liang Sun
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, the Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing 211106, China; Nanjing University of Aeronautics and Astronautics Shenzhen Research Institute, Shenzhen 518063, China.
| | - Zhi Sun
- Department of Medical Imaging, Shandong Provincial Hospital, Jinan 250021, China
| | - Xin Zhou
- Department of Orthopedics, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
| | - Haipeng Si
- Department of Orthopedics, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan 250012, China.
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, the Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing 211106, China; Nanjing University of Aeronautics and Astronautics Shenzhen Research Institute, Shenzhen 518063, China.
| |
Collapse
|
13
|
Dvorak AV, Kumar D, Zhang J, Gilbert G, Balaji S, Wiley N, Laule C, Moore GW, MacKay AL, Kolind SH. The CALIPR framework for highly accelerated myelin water imaging with improved precision and sensitivity. SCIENCE ADVANCES 2023; 9:eadh9853. [PMID: 37910622 PMCID: PMC10619933 DOI: 10.1126/sciadv.adh9853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 09/28/2023] [Indexed: 11/03/2023]
Abstract
Quantitative magnetic resonance imaging (MRI) techniques are powerful tools for the study of human tissue, but, in practice, their utility has been limited by lengthy acquisition times. Here, we introduce the Constrained, Adaptive, Low-dimensional, Intrinsically Precise Reconstruction (CALIPR) framework in the context of myelin water imaging (MWI); a quantitative MRI technique generally regarded as the most rigorous approach for noninvasive, in vivo measurement of myelin content. The CALIPR framework exploits data redundancy to recover high-quality images from a small fraction of an imaging dataset, which allowed MWI to be acquired with a previously unattainable sequence (fully sampled acquisition 2 hours:57 min:20 s) in 7 min:26 s (4.2% of the dataset, acceleration factor 23.9). CALIPR quantitative metrics had excellent precision (myelin water fraction mean coefficient of variation 3.2% for the brain and 3.0% for the spinal cord) and markedly increased sensitivity to demyelinating disease pathology compared to a current, widely used technique. The CALIPR framework facilitates drastically improved MWI and could be similarly transformative for other quantitative MRI applications.
Collapse
Affiliation(s)
- Adam V. Dvorak
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
| | - Dushyant Kumar
- Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Jing Zhang
- Global MR Applications & Workflow, GE HealthCare Canada, Mississauga, ON, Canada
| | | | - Sharada Balaji
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
| | - Neale Wiley
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
| | - Cornelia Laule
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
- Radiology, University of British Columbia, Vancouver, BC, Canada
- Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - G.R. Wayne Moore
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
- Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Alex L. MacKay
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- Radiology, University of British Columbia, Vancouver, BC, Canada
- Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Shannon H. Kolind
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, BC, Canada
- Radiology, University of British Columbia, Vancouver, BC, Canada
- Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| |
Collapse
|
14
|
Masse‐Gignac N, Flórez‐Jiménez S, Mac‐Thiong J, Duong L. Attention-gated U-Net networks for simultaneous axial/sagittal planes segmentation of injured spinal cords. J Appl Clin Med Phys 2023; 24:e14123. [PMID: 37735825 PMCID: PMC10562020 DOI: 10.1002/acm2.14123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 07/13/2023] [Accepted: 07/14/2023] [Indexed: 09/23/2023] Open
Abstract
Magnetic resonance imaging is currently the gold standard for the evaluation of spinal cord injuries. Automatic analysis of these injuries is however challenging, as MRI resolutions vary for different planes of analysis and physiological features are often distorted around these injuries. This study proposes a new CNN-based segmentation method in which information is exchanged between two networks analyzing the scans from different planes. Our aim was to develop a robust method for automatic segmentation of the spinal cord in patients having suffered traumatic injuries. The database consisted of 106 sagittal MRI scans from 94 patients with traumatic spinal cord injuries. Our method used an innovative approach where the scans were analyzed in series under the axial and sagittal plane by two different convolutional networks. The results were compared with those of Deepseg 2D from the Spinal Cord Toolbox (SCT), which was taken as state-of-the-art. Comparisons were evaluated using K-Fold cross-validation combined with statistical t-test results on separate test data. Our method achieved significantly better results than Deepseg 2D, with an average Dice coefficient of 0.95 against 0.88 for Deepseg 2D (p <0.001). Other metrics were also used to compare the segmentations, all of which showed significantly better results for our approach. In this study, we introduce a robust method for spinal cord segmentation which is capable of adequately segmenting spinal cords affected by traumatic injuries, improving upon the methods contained in SCT.
Collapse
Affiliation(s)
- Nicolas Masse‐Gignac
- Department of software and IT engineeringÉcole de technologie supérieureMontréalCanada
- Department of orthopedic surgeryHopital Sacré‐CoeurMontréalCanada
| | - Salomón Flórez‐Jiménez
- Department of software and IT engineeringÉcole de technologie supérieureMontréalCanada
- Department of orthopedic surgeryHopital Sacré‐CoeurMontréalCanada
| | - Jean‐Marc Mac‐Thiong
- Department of software and IT engineeringÉcole de technologie supérieureMontréalCanada
- Department of orthopedic surgeryHopital Sacré‐CoeurMontréalCanada
| | - Luc Duong
- Department of software and IT engineeringÉcole de technologie supérieureMontréalCanada
- Department of orthopedic surgeryHopital Sacré‐CoeurMontréalCanada
| |
Collapse
|
15
|
Sáenz-Gamboa JJ, Domenech J, Alonso-Manjarrés A, Gómez JA, de la Iglesia-Vayá M. Automatic semantic segmentation of the lumbar spine: Clinical applicability in a multi-parametric and multi-center study on magnetic resonance images. Artif Intell Med 2023; 140:102559. [PMID: 37210154 DOI: 10.1016/j.artmed.2023.102559] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 04/14/2023] [Accepted: 04/18/2023] [Indexed: 05/22/2023]
Abstract
Significant difficulties in medical image segmentation include the high variability of images caused by their origin (multi-center), the acquisition protocols (multi-parametric), the variability of human anatomy, illness severity, the effect of age and gender, and notable other factors. This work addresses problems associated with the automatic semantic segmentation of lumbar spine magnetic resonance images using convolutional neural networks. We aimed to assign a class label to each pixel of an image, with classes defined by radiologists corresponding to structural elements such as vertebrae, intervertebral discs, nerves, blood vessels, and other tissues. The proposed network topologies represent variants of the U-Net architecture, and we used several complementary blocks to define the variants: three types of convolutional blocks, spatial attention models, deep supervision, and multilevel feature extractor. Here, we describe the topologies and analyze the results of the neural network designs that obtained the most accurate segmentation. Several proposed designs outperform the standard U-Net used as a baseline, primarily when used in ensembles, where the outputs of multiple neural networks are combined according to different strategies.
Collapse
Affiliation(s)
- Jhon Jairo Sáenz-Gamboa
- FISABIO-CIPF Joint Research Unit in Biomedical Imaging, Fundaciò per al Foment de la Investigaciò Sanitària i Biomèdica (FISABIO), Av. de Catalunya 21, 46020 València, Spain.
| | - Julio Domenech
- Orthopedic Surgery Department, Hospital Arnau de Vilanova, Carrer de San Clemente s/n, 46015, València, Spain
| | - Antonio Alonso-Manjarrés
- Radiology Department, Hospital Arnau de Vilanova, Carrer de San Clemente s/n, 46015, València, Spain
| | - Jon A Gómez
- Pattern Recognition and Human Language Technology research center, Universitat Politècnica de València, Camí de Vera, s/n, 46022, València, Spain
| | - Maria de la Iglesia-Vayá
- FISABIO-CIPF Joint Research Unit in Biomedical Imaging, Fundaciò per al Foment de la Investigaciò Sanitària i Biomèdica (FISABIO), Av. de Catalunya 21, 46020 València, Spain; Regional ministry of Universal Health and Public Health in Valencia, Carrer de Misser Mascó 31, 46010 València, Spain.
| |
Collapse
|
16
|
Schading S, Seif M, Leutritz T, Hupp M, Curt A, Weiskopf N, Freund P. Reliability of spinal cord measures based on synthetic T 1-weighted MRI derived from multiparametric mapping (MPM). Neuroimage 2023; 271:120046. [PMID: 36948280 DOI: 10.1016/j.neuroimage.2023.120046] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 03/14/2023] [Accepted: 03/18/2023] [Indexed: 03/24/2023] Open
Abstract
Short MRI acquisition time, high signal-to-noise ratio, and high reliability are crucial for image quality when scanning healthy volunteers and patients. Cross-sectional cervical cord area (CSA) has been suggested as a marker of neurodegeneration and potential outcome measure in clinical trials and is conventionally measured on T1-weigthed 3D Magnetization Prepared Rapid Acquisition Gradient-Echo (MPRAGE) images. This study aims to reduce the acquisition time for the comprehensive assessment of the spinal cord, which is typically based on MPRAGE for morphometry and multi-parameter mapping (MPM) for microstructure. The MPRAGE is replaced by a synthetic T1-w MRI (synT1-w) estimated from the MPM, in order to measure CSA. SynT1-w images were reconstructed using the MPRAGE signal equation based on quantitative maps of proton density (PD), longitudinal (R1) and effective transverse (R2*) relaxation rates. The reliability of CSA measurements from synT1-w images was determined within a multi-center test-retest study format and validated against acquired MPRAGE scans by assessing the agreement between both methods. The response to pathological changes was tested by longitudinally measuring spinal cord atrophy following spinal cord injury (SCI) for synT1-w and MPRAGE using linear mixed effect models. CSA measurements based on the synT1-w MRI showed high intra-site (Coefficient of variation [CoV]: 1.43% to 2.71%) and inter-site repeatability (CoV: 2.90% to 5.76%), and only a minor deviation of -1.65 mm2 compared to MPRAGE. Crucially, by assessing atrophy rates and by comparing SCI patients with healthy controls longitudinally, differences between synT1-w and MPRAGE were negligible. These results demonstrate that reliable estimates of CSA can be obtained from synT1-w images, thereby reducing scan time significantly.
Collapse
Affiliation(s)
- Simon Schading
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Maryam Seif
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Tobias Leutritz
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Markus Hupp
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Armin Curt
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Patrick Freund
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Wellcome Trust Centre for Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK.
| |
Collapse
|
17
|
Deep Learning-Based Auto-Segmentation of Spinal Cord Internal Structure of Diffusion Tensor Imaging in Cervical Spondylotic Myelopathy. Diagnostics (Basel) 2023; 13:diagnostics13050817. [PMID: 36899962 PMCID: PMC10000612 DOI: 10.3390/diagnostics13050817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/14/2023] [Accepted: 02/19/2023] [Indexed: 02/24/2023] Open
Abstract
Cervical spondylotic myelopathy (CSM) is a chronic disorder of the spinal cord. ROI-based features on diffusion tensor imaging (DTI) provide additional information about spinal cord status, which would benefit the diagnosis and prognosis of CSM. However, the manual extraction of the DTI-related features on multiple ROIs is time-consuming and laborious. In total, 1159 slices at cervical levels from 89 CSM patients were analyzed, and corresponding fractional anisotropy (FA) maps were calculated. Eight ROIs were drawn, covering both sides of lateral, dorsal, ventral, and gray matter. The UNet model was trained with the proposed heatmap distance loss for auto-segmentation. Mean Dice coefficients on the test dataset for dorsal, lateral, and ventral column and gray matter were 0.69, 0.67, 0.57, 0.54 on the left side and 0.68, 0.67, 0.59, 0.55 on the right side. The ROI-based mean FA value based on segmentation model strongly correlated with the value based on manual drawing. The percentages of the mean absolute error between the two values of multiple ROIs were 0.07, 0.07, 0.11, and 0.08 on the left side and 0.07, 0.1, 0.1, 0.11, and 0.07 on the right side. The proposed segmentation model has the potential to offer a more detailed spinal cord segmentation and would be beneficial for quantifying a more detailed status of the cervical spinal cord.
Collapse
|
18
|
Theodorsdottir A, Nielsen HH, Ravnborg MH, Illes Z. Patient reported outcomes in a secondary progressive MS cohort related to cognition, MRI and physical outcomes. Mult Scler Relat Disord 2023; 71:104550. [PMID: 36842312 DOI: 10.1016/j.msard.2023.104550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 01/03/2023] [Accepted: 02/02/2023] [Indexed: 02/07/2023]
Abstract
BACKGROUND Patient-reported outcomes (PROs) are increasingly being used as outcomes in secondary progressive multiple sclerosis (SPMS) trials. We examined how PROs reflect disease burden in SPMS. METHODS In this observational prospective study, 65 SPMS patients were examined by five different PROs (Fatigue Scale Motor Cognition (FSMC), Multiple Sclerosis Impact Scale version 2 (MSIS-29v2), 36-Item Short Form Health Survey version 2 (SF-36v2), EQ-5D-5L and Work Productivity and Activity Impairment Questionnaire: Multiple Sclerosis version 2.0 (WPAI:MS)); two different rating scales, Multiple Sclerosis Impairment Scale (MSIS) and Expanded Disability Status Scale (EDSS); functional tests of mobility (Timed-25-Foot Walk (T-25FW), 6-Spot Step Test (6-SST) and (9-Hole Peg Test (9-HPT)); cognitive tests (Symbol Digital Modalities Test (SDMT) and Brief Visuospatial Memory Test-Revised (BVMT-R)); and multimodal Magnetic Resonance Imaging (MRI). RESULTS When the PROs were divided into physical and psychological subscores, the PRO physical subscores of FSMC, MSIS-29v2 and SF-36v2 correlated with physical rating scales (EDSS, MSIS) and physical measures of upper (9-HPT) and lower extremity function (T-25FW and 6-SST)) (p = 0.04-0.0001). 9-HPT correlated the least with physical subscores of PROs but showed the strongest correlation with activity impairment (subscore of WPAI:MS). In contrast, psychological PRO subscores of FSMC, MSIS-29v2 and SF-36v2 did not reflect the cognitive outcomes (SDMT and BVMT-R), although the cognitive scores correlated with disease burden indicated by MRI lesion volumes. The psychological PRO subscores did not correlate with fatigue, physical and MRI outcomes either. CONCLUSION Correlation between PRO physical subscores and physical outcomes supports PROs as potentially useful clinical endpoints in SPMS. The results of this study indicate that patients with SPMS highly perceive their mobility on function of their lower extremities, while they perceive their daily activities highly dependent on function of the upper extremities. Psychological subscores of MS specific PROs may be less suitable as surrogate markers for the cognitive status and should be considered as a mental quality of life measurement independent of disease burden.
Collapse
Affiliation(s)
- A Theodorsdottir
- Department of Neurology, Odense University Hospital, J.B. Winsloewsvej 4, 5000 Odense C, Denmark; OPEN, Odense Patient Data Explorative Network, Odense University Hospital, Odense, J.B. Winsloewsvej 4, 5000 Odense C, Denmark.
| | - H H Nielsen
- Department of Neurology, Odense University Hospital, J.B. Winsloewsvej 4, 5000 Odense C, Denmark; Department of Neurobiology Research, Institute of Molecular Medicine, University of Southern Denmark, J.B. Winsloewsvej 21, st., 5000 Odense C, Denmark; BRIDGE - Brain Research - Inter Disciplinary Guided Excellence, Department of Clinical Research, J.B. Winsloewsvej 19, 3., 5000 Odense C, Denmark
| | - M H Ravnborg
- Filadelfia Epilepsy Hospital, Kolonivej 1, 4293 Dianalund, Denmark
| | - Z Illes
- Department of Neurology, Odense University Hospital, J.B. Winsloewsvej 4, 5000 Odense C, Denmark; Department of Neurobiology Research, Institute of Molecular Medicine, University of Southern Denmark, J.B. Winsloewsvej 21, st., 5000 Odense C, Denmark; BRIDGE - Brain Research - Inter Disciplinary Guided Excellence, Department of Clinical Research, J.B. Winsloewsvej 19, 3., 5000 Odense C, Denmark
| |
Collapse
|
19
|
Nozawa K, Maki S, Furuya T, Okimatsu S, Inoue T, Yunde A, Miura M, Shiratani Y, Shiga Y, Inage K, Eguchi Y, Ohtori S, Orita S. Magnetic resonance image segmentation of the compressed spinal cord in patients with degenerative cervical myelopathy using convolutional neural networks. Int J Comput Assist Radiol Surg 2023; 18:45-54. [PMID: 36342593 DOI: 10.1007/s11548-022-02783-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 10/20/2022] [Indexed: 11/09/2022]
Abstract
PURPOSE Spinal cord segmentation is the first step in atlas-based spinal cord image analysis, but segmentation of compressed spinal cords from patients with degenerative cervical myelopathy is challenging. We applied convolutional neural network models to segment the spinal cord from T2-weighted axial magnetic resonance images of DCM patients. Furthermore, we assessed the correlation between the cross-sectional area segmented by this network and the neurological symptoms of the patients. METHODS The CNN architecture was built using U-Net and DeepLabv3 + and PyTorch. The CNN was trained on 2762 axial slices from 174 patients, and an additional 517 axial slices from 33 patients were held out for validation and 777 axial slices from 46 patients for testing. The performance of the CNN was evaluated on a test dataset with Dice coefficients as the outcome measure. The ratio of CSA at the maximum compression level to CSA at the C2 level, as segmented by the CNN, was calculated. The correlation between the spinal cord CSA ratio and the Japanese Orthopaedic Association score in DCM patients from the test dataset was investigated using Spearman's rank correlation coefficient. RESULTS The best Dice coefficient was achieved when U-Net was used as the architecture and EfficientNet-b7 as the model for transfer learning. Spearman's rs between the spinal cord CSA ratio and the JOA score of DCM patients was 0.38 (p = 0.007), showing a weak correlation. CONCLUSION Using deep learning with magnetic resonance images of deformed spinal cords as training data, we were able to segment compressed spinal cords of DCM patients with a high concordance with expert manual segmentation. In addition, the spinal cord CSA ratio was weakly, but significantly, correlated with neurological symptoms. Our study demonstrated the first steps needed to implement automated atlas-based analysis of DCM patients.
Collapse
Affiliation(s)
- Kyohei Nozawa
- Department of Medical Engineering, Graduate School of Science and Engineering, Chiba University, Chiba, Japan
| | - Satoshi Maki
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan.
- Center for Frontier Medical Engineering, Chiba University, Chiba, Japan.
| | - Takeo Furuya
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Sho Okimatsu
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Takaki Inoue
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Atsushi Yunde
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Masataka Miura
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Yuki Shiratani
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Yasuhiro Shiga
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Kazuhide Inage
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Yawara Eguchi
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Seiji Ohtori
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
| | - Sumihisa Orita
- Department of Orthopaedic Surgery, Chiba University Graduate School of Medicine, Chiba, Japan
- Center for Frontier Medical Engineering, Chiba University, Chiba, Japan
| |
Collapse
|
20
|
Kaptan M, Vannesjo SJ, Mildner T, Horn U, Hartley‐Davies R, Oliva V, Brooks JCW, Weiskopf N, Finsterbusch J, Eippert F. Automated slice-specific z-shimming for functional magnetic resonance imaging of the human spinal cord. Hum Brain Mapp 2022; 43:5389-5407. [PMID: 35938527 PMCID: PMC9704784 DOI: 10.1002/hbm.26018] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 06/21/2022] [Accepted: 06/24/2022] [Indexed: 01/15/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) of the human spinal cord faces many challenges, such as signal loss due to local magnetic field inhomogeneities. This issue can be addressed with slice-specific z-shimming, which compensates for the dephasing effect of the inhomogeneities using a slice-specific gradient pulse. Here, we aim to address outstanding issues regarding this technique by evaluating its effects on several aspects that are directly relevant for spinal fMRI and by developing two automated procedures in order to improve upon the time-consuming and subjective nature of manual selection of z-shims: one procedure finds the z-shim that maximizes signal intensity in each slice of an EPI reference-scan and the other finds the through-slice field inhomogeneity for each EPI-slice in field map data and calculates the required compensation gradient moment. We demonstrate that the beneficial effects of z-shimming are apparent across different echo times, hold true for both the dorsal and ventral horn, and are also apparent in the temporal signal-to-noise ratio (tSNR) of EPI time-series data. Both of our automated approaches were faster than the manual approach, lead to significant improvements in gray matter tSNR compared to no z-shimming and resulted in beneficial effects that were stable across time. While the field-map-based approach performed slightly worse than the manual approach, the EPI-based approach performed as well as the manual one and was furthermore validated on an external corticospinal data-set (N > 100). Together, automated z-shimming may improve the data quality of future spinal fMRI studies and lead to increased reproducibility in longitudinal studies.
Collapse
Affiliation(s)
- Merve Kaptan
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - S. Johanna Vannesjo
- Department of PhysicsNorwegian University of Science and TechnologyTrondheimNorway
| | - Toralf Mildner
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Ulrike Horn
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | | | - Valeria Oliva
- School of Physiology, Pharmacology and NeuroscienceUniversity of BristolBristolUK
| | - Jonathan C. W. Brooks
- School of PsychologyUniversity of East Anglia Wellcome Wolfson Brain Imaging Centre (UWWBIC)NorwichUK
| | - Nikolaus Weiskopf
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany,Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth SciencesLeipzig UniversityLeipzigGermany
| | - Jürgen Finsterbusch
- Department of Systems NeuroscienceUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Falk Eippert
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| |
Collapse
|
21
|
Trò R, Roascio M, Tortora D, Severino M, Rossi A, Cohen-Adad J, Fato MM, Arnulfo G. Diffusion Kurtosis Imaging of Neonatal Spinal Cord in Clinical Routine. FRONTIERS IN RADIOLOGY 2022; 2:794981. [PMID: 37492682 PMCID: PMC10365122 DOI: 10.3389/fradi.2022.794981] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 01/20/2022] [Indexed: 07/27/2023]
Abstract
Diffusion kurtosis imaging (DKI) has undisputed advantages over the more classical diffusion magnetic resonance imaging (dMRI) as witnessed by the fast-increasing number of clinical applications and software packages widely adopted in brain imaging. However, in the neonatal setting, DKI is still largely underutilized, in particular in spinal cord (SC) imaging, because of its inherently demanding technological requirements. Due to its extreme sensitivity to non-Gaussian diffusion, DKI proves particularly suitable for detecting complex, subtle, fast microstructural changes occurring in this area at this early and critical stage of development, which are not identifiable with only DTI. Given the multiplicity of congenital anomalies of the spinal canal, their crucial effect on later developmental outcome, and the close interconnection between the SC region and the brain above, managing to apply such a method to the neonatal cohort becomes of utmost importance. This study will (i) mention current methodological challenges associated with the application of advanced dMRI methods, like DKI, in early infancy, (ii) illustrate the first semi-automated pipeline built on Spinal Cord Toolbox for handling the DKI data of neonatal SC, from acquisition setting to estimation of diffusion measures, through accurate adjustment of processing algorithms customized for adult SC, and (iii) present results of its application in a pilot clinical case study. With the proposed pipeline, we preliminarily show that DKI is more sensitive than DTI-related measures to alterations caused by brain white matter injuries in the underlying cervical SC.
Collapse
Affiliation(s)
- Rosella Trò
- Departments of Informatics, Bioengineering, Robotics, and System Engineering, University of Genoa, Genoa, Italy
| | - Monica Roascio
- Departments of Informatics, Bioengineering, Robotics, and System Engineering, University of Genoa, Genoa, Italy
| | | | | | - Andrea Rossi
- Neuroradiology Unit, Istituto Giannina Gaslini, Genoa, Italy
- Department of Health Sciences, University of Genoa, Genoa, Italy
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada
- Mila—Quebec AI Institute, Montreal, QC, Canada
| | - Marco Massimo Fato
- Departments of Informatics, Bioengineering, Robotics, and System Engineering, University of Genoa, Genoa, Italy
| | - Gabriele Arnulfo
- Departments of Informatics, Bioengineering, Robotics, and System Engineering, University of Genoa, Genoa, Italy
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| |
Collapse
|
22
|
Theodorsdottir A, Larsen PV, Nielsen HH, Illes Z, Ravnborg MH. Multiple sclerosis impairment scale and brain MRI in secondary progressive multiple sclerosis. Acta Neurol Scand 2022; 145:332-347. [PMID: 34799851 DOI: 10.1111/ane.13554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 11/01/2021] [Accepted: 11/02/2021] [Indexed: 11/30/2022]
Abstract
OBJECTIVE To examine the Multiple Sclerosis Impairment Scale (MSIS) in secondary progressive MS (SPMS) in relation to the Expanded Disability Status Scale (EDSS), magnetic resonance imaging (MRI) outcomes, and mobility. METHODS In this observational single-center study, 68 secondary progressive multiple sclerosis (SPMS) patients were examined by MSIS, EDSS, functional mobility tests of upper/lower extremities, and multimodal MRI. Participants had EDSS ≥3.5, a decline in daily activities over the last year unrelated to relapses, and/or 6-month confirmed disability progression. RESULTS Mean disease duration was 23.1 ± 8.3 years and mean age 54.4 ± 8.1 years. MSIS, EDSS, and their corresponding motor, cerebellar, and sensory subscores correlated (p < .0001). Motor subscores of MSIS correlated stronger with Timed-25-Foot-Walk (T25FW) than pyramidal functional system score (FSS) (p = .03), but EDSS had a stronger correlation to T25FW than the total MSIS score (p = .01). MSIS cerebellar subscore correlated stronger with 9-Hole Peg Test (9-HPT) than cerebellar FSS (p = .04). The sensory MSIS subscore also showed correlation with 9-HPT in contrast to sensory FSS (p = .006). MSIS subscores had stronger correlations with MRI volumetry measures than FSS scores (lesion volume and putamen, thalamus, corpus callosum volumetry, p = .0001-0.0017). CONCLUSION In patients with SPMS, MSIS correlated with functional motor tests. MSIS showed stronger correlations with atrophy of central nervous system areas, and may be more sensitive to scale cerebellar and sensory function than EDSS.
Collapse
Affiliation(s)
- Asta Theodorsdottir
- Department of Neurology Odense University Hospital Odense Denmark
- OPEN Odense Patient Data Explorative Network Odense University Hospital Odense Denmark
| | - Pia Veldt Larsen
- Mental Health Services at the Region of Southern Denmark Odense Denmark
| | - Helle Hvilsted Nielsen
- Department of Neurology Odense University Hospital Odense Denmark
- Department of Neurobiology Research Institute of Molecular Medicine University of Southern Denmark Odense Denmark
- Department of Clinical Research BRIDGE ‐ Brain Research – Inter Disciplinary Guided Excellence University of Southern Denmark Odense Denmark
| | - Zsolt Illes
- Department of Neurology Odense University Hospital Odense Denmark
- Department of Neurobiology Research Institute of Molecular Medicine University of Southern Denmark Odense Denmark
- Department of Clinical Research BRIDGE ‐ Brain Research – Inter Disciplinary Guided Excellence University of Southern Denmark Odense Denmark
| | | |
Collapse
|
23
|
Upper cervical cord atrophy is independent of cervical cord lesion volume in early multiple sclerosis: A two-year longitudinal study. Mult Scler Relat Disord 2022; 60:103713. [PMID: 35272146 DOI: 10.1016/j.msard.2022.103713] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 02/10/2022] [Accepted: 02/24/2022] [Indexed: 11/24/2022]
Abstract
BACKGROUND Upper cervical cord atrophy and lesions have been shown to be associated with disease and disability progression already in early relapsing-remitting multiple sclerosis (RRMS). However, their longitudinal relationship remains unclear. OBJECTIVE To investigate the cross-sectional and longitudinal relation between focal T2 cervical cord lesion volume (CCLV) and regional and global mean upper cervical cord area (UCCA), and their relations with disability. METHODS Over a two-year interval, subjects with RRMS (n = 36) and healthy controls (HC, n = 16) underwent annual clinical and MRI examinations. UCCA and CCLV were obtained from C1 through C4 level. Linear mixed model analysis was performed to investigate the relation between UCCA, CCLV, and disability over time. RESULTS UCCA at baseline was significantly lower in RRMS subjects compared to HCs (p = 0.003), but did not decrease faster over time (p ≥ 0.144). UCCA and CCLV were independent of each other at any of the time points or cervical levels, and over time. Lower baseline UCCA, but not CCLV, was related to worsening of both upper and lower extremities function over time. CONCLUSION UCCA and CCLV are independent from each other, both cross-sectionally and longitudinally, in early MS. Lower UCCA, but not CCLV, was related to increasing disability over time.
Collapse
|
24
|
Taheri K, Vavasour IM, Abel S, Lee LE, Johnson P, Ristow S, Tam R, Laule C, Ackermans NC, Schabas A, Cross H, Chan JK, Sayao AL, Bhan V, Devonshire V, Carruthers R, Li DK, Traboulsee AL, Kolind SH, Dvorak AV. Cervical Spinal Cord Atrophy can be Accurately Quantified Using Head Images. Mult Scler J Exp Transl Clin 2022; 8:20552173211070760. [PMID: 35024164 PMCID: PMC8743948 DOI: 10.1177/20552173211070760] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/16/2021] [Indexed: 11/16/2022] Open
Abstract
Background Spinal cord atrophy provides a clinically relevant metric for monitoring MS. However, the spinal cord is imaged far less frequently than brain due to artefacts and acquisition time, whereas MRI of the brain is routinely performed. Objective To validate spinal cord cross-sectional area measurements from routine 3DT1 whole-brain MRI versus those from dedicated cord MRI in healthy controls and people with MS. Methods We calculated cross-sectional area at C1 and C2/3 using T2*-weighted spinal cord images and 3DT1 brain images, for 28 healthy controls and 73 people with MS. Correlations for both groups were assessed between: (1) C1 and C2/3 using cord images; (2) C1 from brain and C1 from cord; and (3) C1 from brain and C2/3 from cord. Results and Conclusion C1 and C2/3 from cord were strongly correlated in controls (r = 0.94, p<0.0001) and MS (r = 0.85, p<0.0001). There was strong agreement between C1 from brain and C2/3 from cord in controls (r = 0.84, p<0.0001) and MS (r = 0.81, p<0.0001). This supports the use of C1 cross-sectional area calculated from brain imaging as a surrogate for the traditional C2/3 cross-sectional area measure for spinal cord atrophy.
Collapse
Affiliation(s)
- Kamyar Taheri
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Irene M Vavasour
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | | | | | | | - Stephen Ristow
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Roger Tam
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Cornelia Laule
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | | | | | | | | | | | | | | | - Robert Carruthers
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - David Kb Li
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | | | - Shannon H Kolind
- Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Adam Vladimir Dvorak
- Department of Physics & Astronomy, University of British Columbia, Vancouver, BC, Canada
| |
Collapse
|
25
|
David G, Vallotton K, Hupp M, Curt A, Freund P, Seif M. Extent of cord pathology in the lumbosacral enlargement in non-traumatic versus traumatic spinal cord injury. J Neurotrauma 2022; 39:639-650. [PMID: 35018824 DOI: 10.1089/neu.2021.0389] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
This study compares remote neurodegenerative changes caudal to a cervical injury in degenerative cervical myelopathy (DCM) (i.e., non-traumatic) and incomplete traumatic spinal cord injury (tSCI) patients, using MRI-based tissue area measurements and diffusion tensor imaging (DTI). Eighteen mild to moderate DCM patients with sensory impairments (mJOA score: 16.2±1.9), 14 incomplete tetraplegic tSCI patients (AIS C&D), and 20 healthy controls were recruited. All participants received DTI and T2*-weighted scans in the lumbosacral enlargement (caudal to injury) and at C2/C3 (rostral to injury). MRI readouts included DTI metrics in the white matter (WM) columns and cross-sectional WM and gray matter area. One-way ANOVA with Tukey's post-hoc comparison (p<0.05) was used to assess group differences. In the lumbosacral enlargement, compared to DCM, tSCI patients exhibited decreased fractional anisotropy in the lateral (tSCI vs. DCM, -11.9%, p=0.007) and ventral WM column (-8.0%, p=0.021), and showed trend toward lower values in the dorsal column (-8.9%, p=0.068). At C2/C3, compared to controls, fractional anisotropy was lower in both groups in the dorsal (DCM vs. controls, -7.9%, p=0.024; tSCI vs. controls, -10.0%, p=0.007) and in the lateral column (DCM: -6.2%, p=0.039; tSCI: -13.3%, p<0.001), while tSCI patients had lower fractional anisotropy than DCM patients in the lateral column (-7.6%, p=0.029). WM areas were not different between patient groups but were lower compared to controls in the lumbosacral enlargement (DCM: -16.9%, p<0.001; tSCI, -10.5%, p=0.043) and at C2/C3 (DCM: -16.0%, p<0.001; tSCI: -18.1%, p<0.001). In conclusion, mild to moderate DCM and incomplete tSCI lead to similar degree of degeneration of the dorsal and lateral columns at C2/C3, but tSCI results in more widespread white matter damage in the lumbosacral enlargement. These remote changes are likely to contribute to the patients' impairment and recovery. DTI is a sensitive tool to assess remote pathological changes in DCM and tSCI patients.
Collapse
Affiliation(s)
- Gergely David
- University of Zurich, Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland.,University Medical Center Hamburg-Eppendorf, 37734, Department of Systems Neuroscience, Hamburg, Germany;
| | - Kevin Vallotton
- University of Zurich, Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland;
| | - Markus Hupp
- University of Zurich, 27217, Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland;
| | - Armin Curt
- University of Zurich, 27217, Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland;
| | - Patrick Freund
- University of Zurich, 27217, Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland.,UCL Institute of Neurology, 61554, Department of Brain Repair and Rehabilitation, London, United Kingdom of Great Britain and Northern Ireland.,UCL Institute of Neurology, 61554, Wellcome Trust Centre for Neuroimaging, London, United Kingdom of Great Britain and Northern Ireland.,Max Planck Institute for Human Cognitive and Brain Sciences, 27184, Department of Neurophysics, Leipzig, Germany;
| | - Maryam Seif
- University of Zurich, 27217, Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland.,Max Planck Institute for Human Cognitive and Brain Sciences, 27184, Leipzig, Department of Neurophysics, Germany;
| |
Collapse
|
26
|
Navas-Sánchez FJ, Marcos-Vidal L, de Blas DM, Fernández-Pena A, Alemán-Gómez Y, Guzmán-de-Villoria JA, Romero J, Catalina I, Lillo L, Muñoz-Blanco JL, Ordoñez-Ugalde A, Quintáns B, Sobrido MJ, Carmona S, Grandas F, Desco M. Tract-specific damage at spinal cord level in pure hereditary spastic paraplegia type 4: a diffusion tensor imaging study. J Neurol 2022; 269:3189-3203. [PMID: 34999956 DOI: 10.1007/s00415-021-10933-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Revised: 12/03/2021] [Accepted: 12/03/2021] [Indexed: 01/15/2023]
Abstract
BACKGROUND SPG4 is a subtype of hereditary spastic paraplegia (HSP), an upper motor neuron disorder characterized by axonal degeneration of the corticospinal tracts and the fasciculus gracilis. The few neuroimaging studies that have focused on the spinal cord in HSP are based mainly on the analysis of structural characteristics. METHODS We assessed diffusion-related characteristics of the spinal cord using diffusion tensor imaging (DTI), as well as structural and shape-related properties in 12 SPG4 patients and 14 controls. We used linear mixed effects models up to T3 in order to analyze the global effects of 'group' and 'clinical data' on structural and diffusion data. For DTI, we carried out a region of interest (ROI) analysis in native space for the whole spinal cord, the anterior and lateral funiculi, and the dorsal columns. We also performed a voxelwise analysis of the spinal cord to study local diffusion-related changes. RESULTS A reduced cross-sectional area was observed in the cervical region of SPG4 patients, with significant anteroposterior flattening. DTI analyses revealed significantly decreased fractional anisotropy (FA) and increased radial diffusivity at all the cervical and thoracic levels, particularly in the lateral funiculi and dorsal columns. The FA changes in SPG4 patients were significantly related to disease severity, measured as the Spastic Paraplegia Rating Scale score. CONCLUSIONS Our results in SPG4 indicate tract-specific axonal damage at the level of the cervical and thoracic spinal cord. This finding is correlated with the degree of motor disability.
Collapse
Affiliation(s)
- Francisco J Navas-Sánchez
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Dr Esquerdo 46, 28007, Madrid, Spain. .,Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.
| | - Luis Marcos-Vidal
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Dr Esquerdo 46, 28007, Madrid, Spain.,Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.,Departamento de Bioingeniería E Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain
| | - Daniel Martín de Blas
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Dr Esquerdo 46, 28007, Madrid, Spain.,Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.,Departamento de Bioingeniería E Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain
| | - Alberto Fernández-Pena
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Dr Esquerdo 46, 28007, Madrid, Spain.,Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.,Departamento de Bioingeniería E Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain
| | - Yasser Alemán-Gómez
- Department of Psychiatry, Centre Hospitalier Universitaire Vaudois, Prilly, Switzerland.,Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.,Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Lausanne, Switzerland
| | - Juan A Guzmán-de-Villoria
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Dr Esquerdo 46, 28007, Madrid, Spain.,Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.,Servicio de Radiodiagnóstico, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Julia Romero
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.,Servicio de Radiodiagnóstico, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Irene Catalina
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.,Servicio de Neurología, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Laura Lillo
- Servicio de Neurología, Hospital Ruber Internacional, Madrid, Spain
| | - José L Muñoz-Blanco
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.,Servicio de Neurología, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Andrés Ordoñez-Ugalde
- Instituto de Investigación Sanitaria, Hospital Clínico Universitario, Santiago de Compostela, Spain.,Laboratorio Biomolecular, Cuenca, Ecuador.,Unidad de Genética y Molecular, Hospital de Especialidades José Carrasco Arteaga, Cuenca, Ecuador
| | - Beatriz Quintáns
- Instituto de Investigación Sanitaria, Hospital Clínico Universitario, Santiago de Compostela, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER-U711), Madrid, Spain.,Fundación Pública Galega de Medicina Xenómica, Santiago de Compostela, Spain
| | - María-Jesús Sobrido
- Instituto de Investigación Sanitaria, Hospital Clínico Universitario, Santiago de Compostela, Spain.,Instituto de Investigación Biomédica, Hospital Clínico Universitario de A Coruña, SERGAS, A Coruña, Spain
| | - Susanna Carmona
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Dr Esquerdo 46, 28007, Madrid, Spain.,Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Francisco Grandas
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.,Servicio de Neurología, Hospital General Universitario Gregorio Marañón, Madrid, Spain
| | - Manuel Desco
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Dr Esquerdo 46, 28007, Madrid, Spain.,Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain.,Departamento de Bioingeniería E Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain.,Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
| |
Collapse
|
27
|
David G, Pfyffer D, Vallotton K, Pfender N, Thompson A, Weiskopf N, Mohammadi S, Curt A, Freund P. Longitudinal changes of spinal cord grey and white matter following spinal cord injury. J Neurol Neurosurg Psychiatry 2021; 92:1222-1230. [PMID: 34341143 PMCID: PMC8522459 DOI: 10.1136/jnnp-2021-326337] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 06/09/2021] [Indexed: 11/21/2022]
Abstract
OBJECTIVES Traumatic and non-traumatic spinal cord injury produce neurodegeneration across the entire neuraxis. However, the spatiotemporal dynamics of spinal cord grey and white matter neurodegeneration above and below the injury is understudied. METHODS We acquired longitudinal data from 13 traumatic and 3 non-traumatic spinal cord injury patients (8-8 cervical and thoracic cord injuries) within 1.5 years after injury and 10 healthy controls over the same period. The protocol encompassed structural and diffusion-weighted MRI rostral (C2/C3) and caudal (lumbar enlargement) to the injury level to track tissue-specific neurodegeneration. Regression models assessed group differences in the temporal evolution of tissue-specific changes and associations with clinical outcomes. RESULTS At 2 months post-injury, white matter area was decreased by 8.5% and grey matter by 15.9% in the lumbar enlargement, while at C2/C3 only white matter was decreased (-9.7%). Patients had decreased cervical fractional anisotropy (FA: -11.3%) and increased radial diffusivity (+20.5%) in the dorsal column, while FA was lower in the lateral (-10.3%) and ventral columns (-9.7%) of the lumbar enlargement. White matter decreased by 0.34% and 0.35% per month at C2/C3 and lumbar enlargement, respectively, and grey matter decreased at C2/C3 by 0.70% per month. CONCLUSIONS This study describes the spatiotemporal dynamics of tissue-specific spinal cord neurodegeneration above and below a spinal cord injury. While above the injury, grey matter atrophy lagged initially behind white matter neurodegeneration, in the lumbar enlargement these processes progressed in parallel. Tracking trajectories of tissue-specific neurodegeneration provides valuable assessment tools for monitoring recovery and treatment effects.
Collapse
Affiliation(s)
- Gergely David
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Dario Pfyffer
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Kevin Vallotton
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Nikolai Pfender
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Alan Thompson
- Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, UK
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Siawoosh Mohammadi
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Armin Curt
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Patrick Freund
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland .,Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, UK.,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK
| |
Collapse
|
28
|
Valsasina P, Horsfield MA, Meani A, Gobbi C, Gallo A, Rocca MA, Filippi M. Improved Assessment of Longitudinal Spinal Cord Atrophy in Multiple Sclerosis Using a Registration-Based Approach: Relevance for Clinical Studies. J Magn Reson Imaging 2021; 55:1559-1568. [PMID: 34582062 DOI: 10.1002/jmri.27937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 09/15/2021] [Accepted: 09/17/2021] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND Reliable measurements of cervical cord atrophy progression may be useful for monitoring neurodegeneration in multiple sclerosis (MS). PURPOSE To compare a new, registration-based (Reg) method with two existing methods (active surface [AS] and propagation segmentation [PropSeg]) to measure cord atrophy changes over time in MS. STUDY TYPE Retrospective. SUBJECTS Cohort I: Eight healthy controls (HC) and 28 MS patients enrolled at a single institution, and cohort II: 25 HC and 63 MS patients enrolled at three European sites. FIELD STRENGTH/SEQUENCE 3D T1-weighted gradient echo sequence, acquired at 1.5 T (cohort I) and 3.0 T (cohort II). ASSESSMENT Percentage cord area changes (PCACs) between baseline and follow-up (cohort I: 2.34 years [interquartile range = 2.00-2.55 years], cohort II: 1.05 years [interquartile range = 1.01-1.18 years]) were evaluated for all subjects using Reg, AS, and PropSeg. Reg included an accurate registration of baseline and follow-up straightened cord images, followed by AS-based optimized cord segmentation. A subset of studies was analyzed twice by two observers. STATISTICAL TESTS Linear regression models were used to estimate annualized PCAC, and effect sizes expressed as the ratio between the estimated differences and HC error term (P < 0.05). Reproducibility was assessed by linear mixed-effect models. Annualized PCACs were used for sample size calculations (significance: α = 0.05, power: 1 - β = 0.80). RESULTS Annualized PCACs and related standard errors (SEs) were lower with Reg than with other methods: PCAC in MS patients at 1.5 T was -1.12% (SE = 0.22) with Reg, -1.32% (SE = 0.30) with AS, and -1.40% (SE = 0.33) with PropSeg, while at 3.0 T PCAC was -0.83% (SE = 0.25) with Reg, -0.92% (SE = 0.32) with AS, and -1.18 (SE = 0.53) with PropSeg. This was reflected in larger effect sizes and lower sample sizes. Intra- and inter-observer agreement range was 0.72-0.91 with AS, and it was >0.96 with Reg. DATA CONCLUSION The results support the use of the registration method to measure cervical cord atrophy progression in future MS clinical studies. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 2.
Collapse
Affiliation(s)
- Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Alessandro Meani
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Claudio Gobbi
- Multiple Sclerosis Center, Department of Neurology, Neurocenter of Southern Switzerland (NSI), Lugano, Switzerland.,Faculty of Biomedical Sciences, Università della Svizzera Italiana (USI), Lugano, Switzerland
| | - Antonio Gallo
- Department of Advanced Medical and Surgical Sciences and 3T-MRI Research Centre, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy.,Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| |
Collapse
|
29
|
Kontopodis EE, Papadaki E, Trivizakis E, Maris TG, Simos P, Papadakis GZ, Tsatsakis A, Spandidos DA, Karantanas A, Marias K. Emerging deep learning techniques using magnetic resonance imaging data applied in multiple sclerosis and clinical isolated syndrome patients (Review). Exp Ther Med 2021; 22:1149. [PMID: 34504594 PMCID: PMC8393268 DOI: 10.3892/etm.2021.10583] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 07/29/2021] [Indexed: 12/18/2022] Open
Abstract
Computer-aided diagnosis systems aim to assist clinicians in the early identification of abnormal signs in order to optimize the interpretation of medical images and increase diagnostic precision. Multiple sclerosis (MS) and clinically isolated syndrome (CIS) are chronic inflammatory, demyelinating diseases affecting the central nervous system. Recent advances in deep learning (DL) techniques have led to novel computational paradigms in MS and CIS imaging designed for automatic segmentation and detection of areas of interest and automatic classification of anatomic structures, as well as optimization of neuroimaging protocols. To this end, there are several publications presenting artificial intelligence-based predictive models aiming to increase diagnostic accuracy and to facilitate optimal clinical management in patients diagnosed with MS and/or CIS. The current study presents a thorough review covering DL techniques that have been applied in MS and CIS during recent years, shedding light on their current advances and limitations.
Collapse
Affiliation(s)
- Eleftherios E Kontopodis
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Radiology, Medical School, University of Crete, 70013 Heraklion, Greece
| | - Efrosini Papadaki
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Radiology, Medical School, University of Crete, 70013 Heraklion, Greece
| | - Eleftherios Trivizakis
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Radiology, Medical School, University of Crete, 70013 Heraklion, Greece
| | - Thomas G Maris
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Radiology, Medical School, University of Crete, 70013 Heraklion, Greece
| | - Panagiotis Simos
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Psychiatry and Behavioral Sciences, Medical School, University of Crete, 70013 Heraklion, Greece
| | - Georgios Z Papadakis
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Radiology, Medical School, University of Crete, 70013 Heraklion, Greece
| | - Aristidis Tsatsakis
- Centre of Toxicology Science and Research, Faculty of Medicine, University of Crete, 71003 Heraklion, Greece
| | - Demetrios A Spandidos
- Laboratory of Clinical Virology, Medical School, University of Crete, 71003 Heraklion, Greece
| | - Apostolos Karantanas
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Radiology, Medical School, University of Crete, 70013 Heraklion, Greece
| | - Kostas Marias
- Computational BioMedicine Laboratory, Institute of Computer Science, Foundation for Research and Technology-Hellas, 70013 Heraklion, Greece.,Department of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
| |
Collapse
|
30
|
Cohen-Adad J, Alonso-Ortiz E, Abramovic M, Arneitz C, Atcheson N, Barlow L, Barry RL, Barth M, Battiston M, Büchel C, Budde M, Callot V, Combes AJE, De Leener B, Descoteaux M, de Sousa PL, Dostál M, Doyon J, Dvorak A, Eippert F, Epperson KR, Epperson KS, Freund P, Finsterbusch J, Foias A, Fratini M, Fukunaga I, Gandini Wheeler-Kingshott CAM, Germani G, Gilbert G, Giove F, Gros C, Grussu F, Hagiwara A, Henry PG, Horák T, Hori M, Joers J, Kamiya K, Karbasforoushan H, Keřkovský M, Khatibi A, Kim JW, Kinany N, Kitzler HH, Kolind S, Kong Y, Kudlička P, Kuntke P, Kurniawan ND, Kusmia S, Labounek R, Laganà MM, Laule C, Law CS, Lenglet C, Leutritz T, Liu Y, Llufriu S, Mackey S, Martinez-Heras E, Mattera L, Nestrasil I, O'Grady KP, Papinutto N, Papp D, Pareto D, Parrish TB, Pichiecchio A, Prados F, Rovira À, Ruitenberg MJ, Samson RS, Savini G, Seif M, Seifert AC, Smith AK, Smith SA, Smith ZA, Solana E, Suzuki Y, Tackley G, Tinnermann A, Valošek J, Van De Ville D, Yiannakas MC, Weber Ii KA, Weiskopf N, Wise RG, Wyss PO, Xu J. Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers. Sci Data 2021; 8:219. [PMID: 34400655 PMCID: PMC8368310 DOI: 10.1038/s41597-021-00941-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 03/26/2021] [Indexed: 12/21/2022] Open
Abstract
In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject dataset across 19 centers and a multi-subject dataset across 42 centers (for a total of 260 participants), spanning the three main MRI manufacturers: GE, Philips and Siemens. Both datasets are publicly available via git-annex. Data were analysed using the Spinal Cord Toolbox to produce normative values as well as inter/intra-site and inter/intra-manufacturer statistics. Reproducibility for the spine generic protocol was high across sites and manufacturers, with an average inter-site coefficient of variation of less than 5% for all the metrics. Full documentation and results can be found at https://spine-generic.rtfd.io/ . The datasets and analysis pipeline will help pave the way towards accessible and reproducible quantitative MRI in the spinal cord.
Collapse
Affiliation(s)
- Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.
- Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada.
- Mila - Quebec AI Institute, Montreal, QC, Canada.
| | - Eva Alonso-Ortiz
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Mihael Abramovic
- Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Carina Arneitz
- Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Nicole Atcheson
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Laura Barlow
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Robert L Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Harvard-Massachusetts Institute of Technology Health Sciences & Technology, Cambridge, MA, USA
| | - Markus Barth
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Marco Battiston
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Christian Büchel
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Matthew Budde
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Virginie Callot
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hopital Universitaire Timone, CEMEREM, Marseille, France
| | - Anna J E Combes
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Benjamin De Leener
- Department of Computer and Software Engineering, Polytechnique Montreal, Montreal, Canada
- CHU Sainte-Justine Research Centre, Montreal, QC, Canada
| | - Maxime Descoteaux
- Centre de Recherche CHUS, CIMS, Sherbrooke, Canada
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science department, Université de Sherbrooke, Sherbrooke, Canada
| | | | - Marek Dostál
- UHB - University Hospital Brno and Masaryk University, Department of Radiology and Nuclear Medicine, Brno, Czech Republic
| | - Julien Doyon
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Adam Dvorak
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Falk Eippert
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Karla R Epperson
- Richard M. Lucas Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Kevin S Epperson
- Richard M. Lucas Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Patrick Freund
- Spinal Cord Injury Center Balgrist, University of Zurich, Zurich, Switzerland
| | - Jürgen Finsterbusch
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alexandru Foias
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Michela Fratini
- Institute of Nanotechnology, CNR, Rome, Italy
- IRCCS Santa Lucia Foundation, Rome, Italy
| | - Issei Fukunaga
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Giancarlo Germani
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Federico Giove
- IRCCS Santa Lucia Foundation, Rome, Italy
- CREF - Museo storico della fisica e Centro studi e ricerche Enrico Fermi, Rome, Italy
| | - Charley Gros
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Francesco Grussu
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Pierre-Gilles Henry
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Tomáš Horák
- Multimodal and functional imaging laboratory, Central European Institute of Technology (CEITEC), Brno, Czech Republic
| | - Masaaki Hori
- Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan
| | - James Joers
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Kouhei Kamiya
- Department of Radiology, the University of Tokyo, Tokyo, Japan
| | - Haleh Karbasforoushan
- Interdepartmental Neuroscience Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, USA
| | - Miloš Keřkovský
- UHB - University Hospital Brno and Masaryk University, Department of Radiology and Nuclear Medicine, Brno, Czech Republic
| | - Ali Khatibi
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, College of Life and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, UK
| | - Joo-Won Kim
- BioMedical Engineering and Imaging Institute (BMEII), Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nawal Kinany
- Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Hagen H Kitzler
- Institute of Diagnostic and Interventional Neuroradiology, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Shannon Kolind
- Department of Radiology, University of British Columbia, Vancouver, BC, Canada
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- Department Of Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Yazhuo Kong
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- Wellcome Centre For Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Petr Kudlička
- Multimodal and functional imaging laboratory, Central European Institute of Technology (CEITEC), Brno, Czech Republic
| | - Paul Kuntke
- Institute of Diagnostic and Interventional Neuroradiology, Carl Gustav Carus University Hospital, Technische Universität Dresden, Dresden, Germany
| | - Nyoman D Kurniawan
- Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
| | - Slawomir Kusmia
- CUBRIC, Cardiff University, Wales, UK
- Centre for Medical Image Computing (CMIC), Medical Physics and Biomedical Engineering Department, University College London, London, UK
- Epilepsy Society MRI Unit, Chalfont St Peter, UK
| | - René Labounek
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
- Departments of Neurology and Biomedical Engineering, University Hospital Olomouc, Olomouc, Czech Republic
| | | | - Cornelia Laule
- Departments of Radiology, Pathology & Laboratory Medicine, Physics & Astronomy; International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada
| | - Christine S Law
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Tobias Leutritz
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Tiantan Image Research Center, China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Sara Llufriu
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Sean Mackey
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Eloy Martinez-Heras
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Loan Mattera
- Fondation Campus Biotech Genève, 1202, Geneva, Switzerland
| | - Igor Nestrasil
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Kristin P O'Grady
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nico Papinutto
- UCSF Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Daniel Papp
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Wellcome Centre For Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Deborah Pareto
- Neuroradiology Section, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Todd B Parrish
- Interdepartmental Neuroscience Program, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Anna Pichiecchio
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Ferran Prados
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Centre for Medical Image Computing (CMIC), Medical Physics and Biomedical Engineering Department, University College London, London, UK
- E-health Centre, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Àlex Rovira
- Neuroradiology Section, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Marc J Ruitenberg
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Rebecca S Samson
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Giovanni Savini
- Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy
| | - Maryam Seif
- Spinal Cord Injury Center Balgrist, University of Zurich, Zurich, Switzerland
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Alan C Seifert
- BioMedical Engineering and Imaging Institute (BMEII), Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alex K Smith
- Wellcome Centre For Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Seth A Smith
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zachary A Smith
- University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
| | - Elisabeth Solana
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Y Suzuki
- Department of Radiology, the University of Tokyo, Tokyo, Japan
| | | | - Alexandra Tinnermann
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jan Valošek
- Department of Neurology, Faculty of Medicine and Dentistry, Palacký University and University Hospital Olomouc, Olomouc, Czech Republic
| | - Dimitri Van De Ville
- Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
| | - Marios C Yiannakas
- NMR Research Unit, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Kenneth A Weber Ii
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Richard G Wise
- CUBRIC, Cardiff University, Wales, UK
- Institute for Advanced Biomedical Technologies, Department of Neuroscience, Imaging and Clinical Sciences, "G. D'Annunzio University" of Chieti-Pescara, Chieti-Pescara, Italy
| | - Patrik O Wyss
- Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Junqian Xu
- BioMedical Engineering and Imaging Institute (BMEII), Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| |
Collapse
|
31
|
Lukas C, Bellenberg B, Prados F, Valsasina P, Parmar K, Brouwer I, Pareto D, Rovira À, Sastre-Garriga J, Gandini Wheeler-Kingshott CAM, Kappos L, Rocca MA, Filippi M, Yiannakas M, Barkhof F, Vrenken H. Quantification of Cervical Cord Cross-Sectional Area: Which Acquisition, Vertebra Level, and Analysis Software? A Multicenter Repeatability Study on a Traveling Healthy Volunteer. Front Neurol 2021; 12:693333. [PMID: 34421797 PMCID: PMC8371197 DOI: 10.3389/fneur.2021.693333] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Accepted: 06/14/2021] [Indexed: 11/15/2022] Open
Abstract
Background: Considerable spinal cord (SC) atrophy occurs in multiple sclerosis (MS). While MRI-based techniques for SC cross-sectional area (CSA) quantification have improved over time, there is no common agreement on whether to measure at single vertebral levels or across larger regions and whether upper SC CSA can be reliably measured from brain images. Aim: To compare in a multicenter setting three CSA measurement methods in terms of repeatability at different anatomical levels. To analyze the agreement between measurements performed on the cervical cord and on brain MRI. Method: One healthy volunteer was scanned three times on the same day in six sites (three scanner vendors) using a 3T MRI protocol including sagittal 3D T1-weighted imaging of the brain (covering the upper cervical cord) and of the SC. Images were analyzed using two semiautomated methods [NeuroQLab (NQL) and the Active Surface Model (ASM)] and the fully automated Spinal Cord Toolbox (SCT) on different vertebral levels (C1-C2; C2/3) on SC and brain images and the entire cervical cord (C1-C7) on SC images only. Results: CSA estimates were significantly smaller using SCT compared to NQL and ASM (p < 0.001), regardless of the cord level. Inter-scanner repeatability was best in C1-C7: coefficients of variation for NQL, ASM, and SCT: 0.4, 0.6, and 1.0%, respectively. CSAs estimated in brain MRI were slightly lower than in SC MRI (all p ≤ 0.006 at the C1-C2 level). Despite protocol harmonization between the centers with regard to image resolution and use of high-contrast 3D T1-weighted sequences, the variability of CSA was partly scanner dependent probably due to differences in scanner geometry, coil design, and details of the MRI parameter settings. Conclusion: For CSA quantification, dedicated isotropic SC MRI should be acquired, which yielded best repeatability in the entire cervical cord. In the upper part of the cervical cord, use of brain MRI scans entailed only a minor loss of CSA repeatability compared to SC MRI. Due to systematic differences between scanners and the CSA quantification software, both should be kept constant within a study. The MRI dataset of this study is available publicly to test new analysis approaches.
Collapse
Affiliation(s)
- Carsten Lukas
- Institute of Neuroradiology, St. Josef Hospital, Ruhr-University Bochum, Bochum, Germany
| | - Barbara Bellenberg
- Institute of Neuroradiology, St. Josef Hospital, Ruhr-University Bochum, Bochum, Germany
| | - Ferran Prados
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London, London, United Kingdom
- Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- e-Health Centre, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Katrin Parmar
- Neurological Clinic and Policlinic, Department of Medicine, University Hospital Basel, Basel, Switzerland
| | - Iman Brouwer
- Department of Radiology and Nuclear Medicine, Multiple Sclerosis Center Amsterdam, Amsterdam Neuroscience Amsterdam University Medical Centers (UMC), Vrije Universiteit Medical Center (VUmc), Amsterdam, Netherlands
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Àlex Rovira
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Jaume Sastre-Garriga
- Department of Neurology–Neuroimmunology, Multiple Sclerosis Center of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Claudia A. M. Gandini Wheeler-Kingshott
- Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Department of Brain & Behavioral Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Ludwig Kappos
- Neurological Clinic and Policlinic, Department of Medicine, University Hospital Basel, Basel, Switzerland
- Department of Biomedical Engineering, University of Basel, Allschwig, Switzerland
| | - Maria A. Rocca
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Vita-Salute San Raffaele University, Milan, Italy
- Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Marios Yiannakas
- Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Frederik Barkhof
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London, London, United Kingdom
- Queen Square Multiple Sclerosis Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
- Department of Radiology and Nuclear Medicine, Multiple Sclerosis Center Amsterdam, Amsterdam Neuroscience Amsterdam University Medical Centers (UMC), Vrije Universiteit Medical Center (VUmc), Amsterdam, Netherlands
| | - Hugo Vrenken
- Department of Radiology and Nuclear Medicine, Multiple Sclerosis Center Amsterdam, Amsterdam Neuroscience Amsterdam University Medical Centers (UMC), Vrije Universiteit Medical Center (VUmc), Amsterdam, Netherlands
| |
Collapse
|
32
|
Toufani H, Vard A, Adibi I. A pipeline to quantify spinal cord atrophy with deep learning: Application to differentiation of MS and NMOSD patients. Phys Med 2021; 89:51-62. [PMID: 34352676 DOI: 10.1016/j.ejmp.2021.07.030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/28/2021] [Accepted: 07/21/2021] [Indexed: 01/04/2023] Open
Abstract
PURPOSE Quantitative measurement of various anatomical regions of the brain and spinal cord (SC) in MRI images are used as unique biomarkers to consider progress and effects of demyelinating diseases of the central nervous system. This paper presents a fully-automated image processing pipeline which quantifies the SC volume of MRI images. METHODS In the proposed pipeline, after conducting some pre-processing tasks, a deep convolutional network is utilized to segment the spinal cord cross-sectional area (SCCSA) of each slice. After full segmentation, certain extra slices interpolate between each two adjacent slices using the shape-based interpolation method. Then, a 3D model of the SC is reconstructed, and, by counting the voxels of it, the SC volume is calculated. The performance of the proposed method for the SCCSA segmentation is evaluated on 140 MRI images. Subsequently, to demonstrate the application of the proposed pipeline, we study the differentiations of SC atrophy between 38 Multiple Sclerosis (MS) and 25 Neuromyelitis Optica Spectrum Disorder (NMOSD) patients. RESULTS The experimental results of the SCCSA segmentation indicate that the proposed method, adapted by Mask R-CNN, presented the most satisfactory result with the average Dice coefficient of 0.96. For this method, statistical metrics including sensitivity, specificity, accuracy, and precision are 97.51%, 99.98%, 99.92%, and 98.04% respectively. Moreover, the t-test result (p-value = 0.00089) verified a significant difference between the SC atrophy of MS and NMOSD patients. CONCLUSION The pipeline efficiently quantifies the SC volume of MRI images and can be utilized as an affordable computer-aided tool for diagnostic purposes.
Collapse
Affiliation(s)
- Hediyeh Toufani
- Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Student Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Alireza Vard
- Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Iman Adibi
- Department of Neurology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Isfahan Neurosciences Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| |
Collapse
|
33
|
Shahrampour S, De Leener B, Alizadeh M, Middleton D, Krisa L, Flanders AE, Faro SH, Cohen-Adad J, Mohamed FB. Atlas-Based Quantification of DTI Measures in a Typically Developing Pediatric Spinal Cord. AJNR Am J Neuroradiol 2021; 42:1727-1734. [PMID: 34326104 DOI: 10.3174/ajnr.a7221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 04/19/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Multi-parametric MRI, provides a variety of biomarkers sensitive to white matter integrity, However, spinal cord MRI data in pediatrics is rare compared to adults. The purpose of this work was 3-fold: 1) to develop a processing pipeline for atlas-based generation of the typically developing pediatric spinal cord WM tracts, 2) to derive atlas-based normative values of the DTI indices for various WM pathways, and 3) to investigate age-related changes in the obtained normative DTI indices along the extracted tracts. MATERIALS AND METHODS DTI scans of 30 typically developing subjects (age range, 6-16 years) were acquired on a 3T MR imaging scanner. The data were registered to the PAM50 template in the Spinal Cord Toolbox. Next, the DTI indices for various WM regions were extracted at a single section centered at the C3 vertebral body in all the 30 subjects. Finally, an ANOVA test was performed to examine the effects of the following: 1) laterality, 2) functionality, and 3) age, with DTI-derived indices in 34 extracted WM regions. RESULTS A postprocessing pipeline was developed and validated to delineate pediatric spinal cord WM tracts. The results of ANOVA on fractional anisotropy values showed no effect for laterality (P = .72) but an effect for functionality (P < .001) when comparing the 30 primary WM labels. There was a significant (P < .05) effect of age and maturity of the left spinothalamic tract on mean diffusivity, radial diffusivity, and axial diffusivity values. CONCLUSIONS The proposed automated pipeline in this study incorporates unique postprocessing steps followed by template registration and quantification of DTI metrics using atlas-based regions. This method eliminates the need for manual ROI analysis of WM tracts and, therefore, increases the accuracy and speed of the measurements.
Collapse
Affiliation(s)
- S Shahrampour
- From the Departments of Radiology (S.S., M.A., D.M., F.B.M.)
| | - B De Leener
- Department of Computer Engineering and Software Engineering (B.D.L.)
| | - M Alizadeh
- From the Departments of Radiology (S.S., M.A., D.M., F.B.M.)
| | - D Middleton
- From the Departments of Radiology (S.S., M.A., D.M., F.B.M.)
| | | | - A E Flanders
- Radiology (A.E.F., S.H.F.), Thomas Jefferson University, Philadelphia, Pennsylvania
| | - S H Faro
- Radiology (A.E.F., S.H.F.), Thomas Jefferson University, Philadelphia, Pennsylvania
| | - J Cohen-Adad
- NeuroPoly Lab (J.C.-A.), Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Quebec, Canada.,Functional Neuroimaging Unit (J.C.-A.), Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Université de Montréal, Montreal, Quebec, Canada
| | - F B Mohamed
- From the Departments of Radiology (S.S., M.A., D.M., F.B.M.)
| |
Collapse
|
34
|
Baucher G, Rasoanandrianina H, Levy S, Pini L, Troude L, Roche PH, Callot V. T1 Mapping for Microstructural Assessment of the Cervical Spinal Cord in the Evaluation of Patients with Degenerative Cervical Myelopathy. AJNR Am J Neuroradiol 2021; 42:1348-1357. [PMID: 33985954 DOI: 10.3174/ajnr.a7157] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 02/07/2021] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Although current radiologic evaluation of degenerative cervical myelopathy by conventional MR imaging accurately demonstrates spondylosis or degenerative disc disease causing spinal cord dysfunction, conventional MR imaging still fails to provide satisfactory anatomic and clinical correlations. In this context, we assessed the potential value of quantitative cervical spinal cord T1 mapping regarding the evaluation of patients with degenerative cervical myelopathy. MATERIALS AND METHODS Twenty patients diagnosed with mild and moderate-to-severe degenerative cervical myelopathy and 10 healthy subjects were enrolled in a multiparametric MR imaging protocol. Cervical spinal cord T1 mapping was performed with the MP2RAGE sequence procedure. Retrieved data were processed and analyzed regarding the global spinal cord and white and anterior gray matter on the basis of the clinical severity and the spinal canal stenosis grading. RESULTS Noncompressed levels in healthy controls demonstrated significantly lower T1 values than noncompressed, mild, moderate, and severe stenotic levels in patients. Concerning the entire spinal cord T1 mapping, patients with moderate-to-severe degenerative cervical myelopathy had higher T1 values compared with healthy controls. Regarding the specific levels, patients with moderate-to-severe degenerative cervical myelopathy demonstrated a T1 value increase at C1, C7, and the level of maximal compression compared with healthy controls. Patients with mild degenerative cervical myelopathy had lower T1 values than those with moderate-to-severe degenerative cervical myelopathy at the level of maximal compression. Analyses of white and anterior gray matter confirmed similar results. Strong negative correlations between individual modified Japanese Orthopaedic Association scores and T1 values were also observed. CONCLUSIONS In this preliminary study, 3D-MP2RAGE T1 mapping demonstrated increased T1 values in the pathology tissue samples, with diffuse medullary alterations in all patients with degenerative cervical myelopathy, especially relevant at C1 (nonstenotic level) and at the maximal compression level. Encouraging correlations observed with the modified Japanese Orthopaedic Association score make this novel approach a potential quantitative biomarker related to clinical severity in degenerative cervical myelopathy. Nevertheless, patients with mild degenerative cervical myelopathy demonstrated nonsignificant results compared with healthy controls and should now be studied in multicenter studies with larger patient populations.
Collapse
Affiliation(s)
- G Baucher
- From the Neurochirurgie adulte (G.B., L.T., P.-H.R.), Assistance Publique-Hôpitaux de Marseille, Hôpital Universitaire Nord, Marseille, France
- Center for Magnetic Resonance in Biology and Medicine (G.B., H.R., L.P., S.L., V.C.), Assistance Publique-Hôpitaux de Marseille, Hôpital Universitaire Timone, Marseille, France
- iLab-Spine International Associated Laboratory (G.B., H.R., S.L., P.-H.R., V.C.), Marseille-Montreal, France-Canada
| | - H Rasoanandrianina
- Center for Magnetic Resonance in Biology and Medicine (G.B., H.R., L.P., S.L., V.C.), Assistance Publique-Hôpitaux de Marseille, Hôpital Universitaire Timone, Marseille, France
- Center for Magnetic Resonance in Biology and Medicine (H.R., L.P., S.L., V.C.), Aix-Marseille Université, Center National de la Recherche Scientifique, Marseille, France
- iLab-Spine International Associated Laboratory (G.B., H.R., S.L., P.-H.R., V.C.), Marseille-Montreal, France-Canada
| | - S Levy
- Center for Magnetic Resonance in Biology and Medicine (G.B., H.R., L.P., S.L., V.C.), Assistance Publique-Hôpitaux de Marseille, Hôpital Universitaire Timone, Marseille, France
- Center for Magnetic Resonance in Biology and Medicine (H.R., L.P., S.L., V.C.), Aix-Marseille Université, Center National de la Recherche Scientifique, Marseille, France
- iLab-Spine International Associated Laboratory (G.B., H.R., S.L., P.-H.R., V.C.), Marseille-Montreal, France-Canada
| | - L Pini
- Center for Magnetic Resonance in Biology and Medicine (G.B., H.R., L.P., S.L., V.C.), Assistance Publique-Hôpitaux de Marseille, Hôpital Universitaire Timone, Marseille, France
- Center for Magnetic Resonance in Biology and Medicine (H.R., L.P., S.L., V.C.), Aix-Marseille Université, Center National de la Recherche Scientifique, Marseille, France
| | - L Troude
- From the Neurochirurgie adulte (G.B., L.T., P.-H.R.), Assistance Publique-Hôpitaux de Marseille, Hôpital Universitaire Nord, Marseille, France
| | - P-H Roche
- From the Neurochirurgie adulte (G.B., L.T., P.-H.R.), Assistance Publique-Hôpitaux de Marseille, Hôpital Universitaire Nord, Marseille, France
- iLab-Spine International Associated Laboratory (G.B., H.R., S.L., P.-H.R., V.C.), Marseille-Montreal, France-Canada
| | - V Callot
- Center for Magnetic Resonance in Biology and Medicine (G.B., H.R., L.P., S.L., V.C.), Assistance Publique-Hôpitaux de Marseille, Hôpital Universitaire Timone, Marseille, France
- Center for Magnetic Resonance in Biology and Medicine (H.R., L.P., S.L., V.C.), Aix-Marseille Université, Center National de la Recherche Scientifique, Marseille, France
- iLab-Spine International Associated Laboratory (G.B., H.R., S.L., P.-H.R., V.C.), Marseille-Montreal, France-Canada
| |
Collapse
|
35
|
Badoual A, Romani L, Unser M. Active Subdivision Surfaces for the Semiautomatic Segmentation of Biomedical Volumes. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2021; 30:5739-5753. [PMID: 34129498 DOI: 10.1109/tip.2021.3087947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
We present a new family of active surfaces for the semiautomatic segmentation of volumetric objects in 3D biomedical images. We represent our deformable model by a subdivision surface encoded by a small set of control points and generated through a geometric refinement process. The subdivision operator confers important properties to the surface such as smoothness, reproduction of desirable shapes and interpolation of the control points. We deform the subdivision surface through the minimization of suitable gradient-based and region-based energy terms that we have designed for that purpose. In addition, we provide an easy way to combine these energies with convolutional neural networks. Our active subdivision surface satisfies the property of multiresolution, which allows us to adopt a coarse-to-fine optimization strategy. This speeds up the computations and decreases its dependence on initialization compared to singleresolution active surfaces. Performance evaluations on both synthetic and real biomedical data show that our active subdivision surface is robust in the presence of noise and outperforms current state-of-the-art methods. In addition, we provide a software that gives full control over the active subdivision surface via an intuitive manipulation of the control points.
Collapse
|
36
|
Zhang X, Li Y, Liu Y, Tang SX, Liu X, Punithakumar K, Shi D. Automatic spinal cord segmentation from axial-view MRI slices using CNN with grayscale regularized active contour propagation. Comput Biol Med 2021; 132:104345. [PMID: 33780869 DOI: 10.1016/j.compbiomed.2021.104345] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 03/09/2021] [Accepted: 03/12/2021] [Indexed: 11/29/2022]
Abstract
Accurate positioning of the responsible segment for patients with cervical spondylotic myelopathy (CSM) is clinically important not only to the surgery but also to reduce the incidence of surgical trauma and complications. Spinal cord segmentation is a crucial step in the positioning procedure. This study proposed a fully automated approach for spinal cord segmentation from 2D axial-view MRI slices of patients with CSM. The proposed method was trained and tested using clinical data from 20 CSM patients (359 images) acquired by the Peking University Third Hospital, with ground truth labeled by professional radiologists. The accuracy of the proposed method was evaluated using quantitative measures, the reliability metric as well as visual assessment. The proposed method yielded a Dice coefficient of 87.0%, Hausdorff distance of 9.7 mm, root-mean-square error of 5.9 mm. Higher conformance with ground truth was observed for the proposed method in comparison to the state-of-the-art algorithms. The results are also statistically significant with p-values calculated between state-of-the-art methods and the proposed methods.
Collapse
Affiliation(s)
- Xiaoran Zhang
- School of Automation, Beijing Institute of Technology, Beijing, 100081, China; Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA, 90095-1594, USA.
| | - Yan Li
- Department of Orthopaedics, Peking University Third Hospital and the Engineering Research Center of Bone and Joint Precision Medicine, Ministry of Education, Beijing, China.
| | - Yicun Liu
- School of Automation, Beijing Institute of Technology, Beijing, 100081, China.
| | - Shu-Xia Tang
- Department of Mechanical Engineering, Texas Tech University, Lubbock, TX, 79409, USA.
| | - Xiaoguang Liu
- Department of Orthopaedics, Peking University Third Hospital and the Engineering Research Center of Bone and Joint Precision Medicine, Ministry of Education, Beijing, China.
| | - Kumaradevan Punithakumar
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, 8440, Canada.
| | - Dawei Shi
- School of Automation, Beijing Institute of Technology, Beijing, 100081, China.
| |
Collapse
|
37
|
Ost K, Jacobs WB, Evaniew N, Cohen-Adad J, Anderson D, Cadotte DW. Spinal Cord Morphology in Degenerative Cervical Myelopathy Patients; Assessing Key Morphological Characteristics Using Machine Vision Tools. J Clin Med 2021; 10:jcm10040892. [PMID: 33672259 PMCID: PMC7926672 DOI: 10.3390/jcm10040892] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 02/16/2021] [Accepted: 02/18/2021] [Indexed: 11/29/2022] Open
Abstract
Despite Degenerative Cervical Myelopathy (DCM) being the most common form of spinal cord injury, effective methods to evaluate patients for its presence and severity are only starting to appear. Evaluation of patient images, while fast, is often unreliable; the pathology of DCM is complex, and clinicians often have difficulty predicting patient prognosis. Automated tools, such as the Spinal Cord Toolbox (SCT), show promise, but remain in the early stages of development. To evaluate the current state of an SCT automated process, we applied it to MR imaging records from 328 DCM patients, using the modified Japanese Orthopedic Associate scale as a measure of DCM severity. We found that the metrics extracted from these automated methods are insufficient to reliably predict disease severity. Such automated processes showed potential, however, by highlighting trends and barriers which future analyses could, with time, overcome. This, paired with findings from other studies with similar processes, suggests that additional non-imaging metrics could be added to achieve diagnostically relevant predictions. Although modeling techniques such as these are still in their infancy, future models of DCM severity could greatly improve automated clinical diagnosis, communications with patients, and patient outcomes.
Collapse
Affiliation(s)
- Kalum Ost
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada;
| | - W. Bradley Jacobs
- Department of Clinical Neurosciences, Section of Neurosurgery, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada;
- Combined Orthopedic and Neurosurgery Spine Program, University of Calgary, Calgary, AB T2N 1N4, Canada;
| | - Nathan Evaniew
- Combined Orthopedic and Neurosurgery Spine Program, University of Calgary, Calgary, AB T2N 1N4, Canada;
- Section of Orthopaedic Surgery, Department of Surgery, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montrèal, Montrèal, QC H3T 1J4, Canada;
- Functional Neuroimaging Unit, CRIUGM, Universitè de Montrèal, Montrèal, QC H3T 1J4, Canada
- Mila-Quebec AI Institute, Montrèal, QC T2N 1N4, Canada
| | - David Anderson
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada;
| | - David W. Cadotte
- Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada;
- Combined Orthopedic and Neurosurgery Spine Program, University of Calgary, Calgary, AB T2N 1N4, Canada;
- Section of Orthopaedic Surgery, Department of Surgery, University of Calgary, Calgary, AB T2N 1N4, Canada
- Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada;
- Correspondence: ; Tel.: +403-944-3490
| |
Collapse
|
38
|
Ouellette R, Treaba CA, Granberg T, Herranz E, Barletta V, Mehndiratta A, De Leener B, Tauhid S, Yousuf F, Dupont SM, Klawiter EC, Sloane JA, Bakshi R, Cohen-Adad J, Mainero C. 7 T imaging reveals a gradient in spinal cord lesion distribution in multiple sclerosis. Brain 2021; 143:2973-2987. [PMID: 32935834 DOI: 10.1093/brain/awaa249] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 06/03/2020] [Accepted: 06/29/2020] [Indexed: 12/11/2022] Open
Abstract
We used 7 T MRI to: (i) characterize the grey and white matter pathology in the cervical spinal cord of patients with early relapsing-remitting and secondary progressive multiple sclerosis; (ii) assess the spinal cord lesion spatial distribution and the hypothesis of an outside-in pathological process possibly driven by CSF-mediated immune cytotoxic factors; and (iii) evaluate the association of spinal cord pathology with brain burden and its contribution to neurological disability. We prospectively recruited 20 relapsing-remitting, 15 secondary progressive multiple sclerosis participants and 11 age-matched healthy control subjects to undergo 7 T imaging of the cervical spinal cord and brain as well as conventional 3 T brain acquisition. Cervical spinal cord imaging at 7 T was used to segment grey and white matter, including lesions therein. Brain imaging at 7 T was used to segment cortical and white matter lesions and 3 T imaging for cortical thickness estimation. Cervical spinal cord lesions were mapped voxel-wise as a function of distance from the inner central canal CSF pool to the outer subpial surface. Similarly, brain white matter lesions were mapped voxel-wise as a function of distance from the ventricular system. Subjects with relapsing-remitting multiple sclerosis showed a greater predominance of spinal cord lesions nearer the outer subpial surface compared to secondary progressive cases. Inversely, secondary progressive participants presented with more centrally located lesions. Within the brain, there was a strong gradient of lesion formation nearest the ventricular system that was most evident in participants with secondary progressive multiple sclerosis. Lesion fractions within the spinal cord grey and white matter were related to the lesion fraction in cerebral white matter. Cortical thinning was the primary determinant of the Expanded Disability Status Scale, white matter lesion fractions in the spinal cord and brain of the 9-Hole Peg Test and cortical thickness and spinal cord grey matter cross-sectional area of the Timed 25-Foot Walk. Spinal cord lesions were localized nearest the subpial surfaces for those with relapsing-remitting and the central canal CSF surface in progressive disease, possibly implying CSF-mediated pathogenic mechanisms in lesion development that may differ between multiple sclerosis subtypes. These findings show that spinal cord lesions involve both grey and white matter from the early multiple sclerosis stages and occur mostly independent from brain pathology. Despite the prevalence of cervical spinal cord lesions and atrophy, brain pathology seems more strongly related to physical disability as measured by the Expanded Disability Status Scale.
Collapse
Affiliation(s)
- Russell Ouellette
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Radiology, Karolinska University Hospital, Stockholm, Sweden
| | - Constantina A Treaba
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Tobias Granberg
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Radiology, Karolinska University Hospital, Stockholm, Sweden.,Harvard Medical School, Boston, MA, USA
| | - Elena Herranz
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Valeria Barletta
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Ambica Mehndiratta
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Benjamin De Leener
- Department of Computer Engineering and Software Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Shahamat Tauhid
- Harvard Medical School, Boston, MA, USA.,Brigham and Women's Hospital, Boston, MA, USA
| | - Fawad Yousuf
- Harvard Medical School, Boston, MA, USA.,Brigham and Women's Hospital, Boston, MA, USA
| | - Sarah M Dupont
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Eric C Klawiter
- Harvard Medical School, Boston, MA, USA.,Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Jacob A Sloane
- Harvard Medical School, Boston, MA, USA.,Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Rohit Bakshi
- Harvard Medical School, Boston, MA, USA.,Brigham and Women's Hospital, Boston, MA, USA
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Caterina Mainero
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA.,Harvard Medical School, Boston, MA, USA
| |
Collapse
|
39
|
Irimia A, Van Horn JD. Mapping the rest of the human connectome: Atlasing the spinal cord and peripheral nervous system. Neuroimage 2021; 225:117478. [PMID: 33160086 PMCID: PMC8485987 DOI: 10.1016/j.neuroimage.2020.117478] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 09/15/2020] [Accepted: 10/13/2020] [Indexed: 12/13/2022] Open
Abstract
The emergence of diffusion, structural, and functional neuroimaging methods has enabled major multi-site efforts to map the human connectome, which has heretofore been defined as containing all neural connections in the central nervous system (CNS). However, these efforts are not structured to examine the richness and complexity of the peripheral nervous system (PNS), which arguably forms the (neglected) rest of the connectome. Despite increasing interest in an atlas of the spinal cord (SC) and PNS which is simultaneously stereotactic, interactive, electronically dissectible, scalable, population-based and deformable, little attention has thus far been devoted to this task of critical importance. Nevertheless, the atlasing of these complete neural structures is essential for neurosurgical planning, neurological localization, and for mapping those components of the human connectome located outside of the CNS. Here we recommend a modification to the definition of the human connectome to include the SC and PNS, and argue for the creation of an inclusive atlas to complement current efforts to map the brain's human connectome, to enhance clinical education, and to assist progress in neuroscience research. In addition to providing a critical overview of existing neuroimaging techniques, image processing methodologies and algorithmic advances which can be combined for the creation of a full connectome atlas, we outline a blueprint for ultimately mapping the entire human nervous system and, thereby, for filling a critical gap in our scientific knowledge of neural connectivity.
Collapse
Affiliation(s)
- Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, 3715 McClintock Avenue, Los Angeles CA 90089, United States; Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, 1042 Downey Way, Los Angeles, CA 90089, United States.
| | - John Darrell Van Horn
- Department of Psychology, University of Virginia, 485 McCormick Road, Gilmer Hall, Room 102, Charlottesville, Virginia 22903, United States; School of Data Science, University of Virginia, Dell 1, Charlottesville, Virginia 22903, United States.
| |
Collapse
|
40
|
Dvorak AV, Ljungberg E, Vavasour IM, Lee LE, Abel S, Li DKB, Traboulsee A, MacKay AL, Kolind SH. Comparison of multi echo T 2 relaxation and steady state approaches for myelin imaging in the central nervous system. Sci Rep 2021; 11:1369. [PMID: 33446710 PMCID: PMC7809349 DOI: 10.1038/s41598-020-80585-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 12/22/2020] [Indexed: 12/11/2022] Open
Abstract
The traditional approach for measuring myelin-associated water with quantitative magnetic resonance imaging (MRI) uses multi-echo T2 relaxation data to calculate the myelin water fraction (MWF). A fundamentally different approach, abbreviated “mcDESPOT”, uses a more efficient steady-state acquisition to generate an equivalent metric (fM). Although previous studies have demonstrated inherent instability and bias in the complex mcDESPOT analysis procedure, fM has often been used as a surrogate for MWF. We produced and compared multivariate atlases of MWF and fM in healthy human brain and cervical spinal cord (available online) and compared their ability to detect multiple sclerosis pathology. A significant bias was found in all regions (p < 10–5), albeit reversed for spinal cord (fM-MWF = − 3.4%) compared to brain (+ 6.2%). MWF and fM followed an approximately linear relationship for regions with MWF < ~ 10%. For MWF > ~ 10%, the relationship broke down and fM no longer increased in tandem with MWF. For multiple sclerosis patients, MWF and fM Z score maps showed overlapping areas of low Z score and similar trends between patients and brain regions, although those of fM generally had greater spatial extent and magnitude of severity. These results will guide future choice of myelin-sensitive quantitative MRI and improve interpretation of studies using either myelin imaging approach.
Collapse
Affiliation(s)
- Adam V Dvorak
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada. .,International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada.
| | - Emil Ljungberg
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Irene M Vavasour
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Lisa Eunyoung Lee
- Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Shawna Abel
- Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - David K B Li
- Radiology, University of British Columbia, Vancouver, BC, Canada.,Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Anthony Traboulsee
- Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Alex L MacKay
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Shannon H Kolind
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada.,Radiology, University of British Columbia, Vancouver, BC, Canada.,Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| |
Collapse
|
41
|
Bautin P, Cohen-Adad J. Minimum detectable spinal cord atrophy with automatic segmentation: Investigations using an open-access dataset of healthy participants. NEUROIMAGE: CLINICAL 2021; 32:102849. [PMID: 34624638 PMCID: PMC8503570 DOI: 10.1016/j.nicl.2021.102849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 09/07/2021] [Accepted: 09/28/2021] [Indexed: 11/20/2022] Open
Abstract
Evaluate the robustness of an automated analysis pipeline for detecting SC atrophy. Simulate spinal cord atrophy and scan-rescan variability. Fully automated analysis method available on an open access database. Evaluation of sample size and inter/intra-subject variability for T1w and T2w images.
Spinal cord atrophy is a well-known biomarker in multiple sclerosis (MS) and other diseases. It is measured by segmenting the spinal cord on an MRI image and computing the average cross-sectional area (CSA) over a few slices. Introduced about 25 years ago, this procedure is highly sensitive to the quality of the segmentation and is prone to rater-bias. Recently, fully-automated spinal cord segmentation methods, which remove the rater-bias and enable the automated analysis of large populations, have been introduced. A lingering question related to these automated methods is: How reliable are they at detecting atrophy? In this study, we evaluated the precision and accuracy of automated atrophy measurements by simulating scan-rescan experiments. Spinal cord MRI data from the open-access spine-generic project were used. The dataset aggregates 42 sites worldwide and consists of 260 healthy subjects and includes T1w and T2w contrasts. To simulate atrophy, each volume was globally rescaled at various scaling factors. Moreover, to simulate patient repositioning, random rigid transformations were applied. Using the DeepSeg algorithm from the Spinal Cord Toolbox, the spinal cord was segmented and vertebral levels were identified. Then, the average CSA between C3-C5 vertebral levels was computed for each Monte Carlo sample, allowing us to derive measures of atrophy, intra/inter-subject variability, and sample-size calculations. The minimum sample size required to detect an atrophy of 2% between unpaired study arms, commonly seen in MS studies, was 467 +/− 13.9 using T1w and 467 +/− 3.2 using T2w images. The minimum sample size to detect a longitudinal atrophy (between paired study arms) of 0.8% was 60 +/− 25.1 using T1w and 10 +/− 1.2 using T2w images. At the intra-subject level, the estimated CSA, observed in this study, showed good precision compared to other studies with COVs (across Monte Carlo transformations) of 0.8% for T1w and 0.6% for T2w images. While these sample sizes seem small, we would like to stress that these results correspond to a “best case” scenario, in that the dataset used here was of particularly good quality and the model for simulating atrophy does not encompass all the variability met in real-life datasets. The simulated atrophy and scan-rescan variability may over-simplify the biological reality. The proposed framework is open-source and available at https://csa-atrophy.readthedocs.io/.
Collapse
Affiliation(s)
- Paul Bautin
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada; Mila - Quebec AI Institute, Montreal, QC, Canada.
| |
Collapse
|
42
|
Dekker I, Schoonheim MM, Venkatraghavan V, Eijlers AJC, Brouwer I, Bron EE, Klein S, Wattjes MP, Wink AM, Geurts JJG, Uitdehaag BMJ, Oxtoby NP, Alexander DC, Vrenken H, Killestein J, Barkhof F, Wottschel V. The sequence of structural, functional and cognitive changes in multiple sclerosis. Neuroimage Clin 2020; 29:102550. [PMID: 33418173 PMCID: PMC7804841 DOI: 10.1016/j.nicl.2020.102550] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 12/09/2020] [Accepted: 12/20/2020] [Indexed: 12/25/2022]
Abstract
BACKGROUND As disease progression remains poorly understood in multiple sclerosis (MS), we aim to investigate the sequence in which different disease milestones occur using a novel data-driven approach. METHODS We analysed a cohort of 295 relapse-onset MS patients and 96 healthy controls, and considered 28 features, capturing information on T2-lesion load, regional brain and spinal cord volumes, resting-state functional centrality ("hubness"), microstructural tissue integrity of major white matter (WM) tracts and performance on multiple cognitive tests. We used a discriminative event-based model to estimate the sequence of biomarker abnormality in MS progression in general, as well as specific models for worsening physical disability and cognitive impairment. RESULTS We demonstrated that grey matter (GM) atrophy of the cerebellum, thalamus, and changes in corticospinal tracts are early events in MS pathology, whereas other WM tracts as well as the cognitive domains of working memory, attention, and executive function are consistently late events. The models for disability and cognition show early functional changes of the default-mode network and earlier changes in spinal cord volume compared to the general MS population. Overall, GM atrophy seems crucial due to its early involvement in the disease course, whereas WM tract integrity appears to be affected relatively late despite the early onset of WM lesions. CONCLUSION Data-driven modelling revealed the relative occurrence of both imaging and non-imaging events as MS progresses, providing insights into disease propagation mechanisms, and allowing fine-grained staging of patients for monitoring purposes.
Collapse
Affiliation(s)
- Iris Dekker
- Amsterdam UMC, Location VUmc, Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands; Neurology, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Vikram Venkatraghavan
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology & Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Anand J C Eijlers
- Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Iman Brouwer
- Amsterdam UMC, Location VUmc, Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Esther E Bron
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology & Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology & Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Mike P Wattjes
- Dept. of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Alle Meije Wink
- Amsterdam UMC, Location VUmc, Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Jeroen J G Geurts
- Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Bernard M J Uitdehaag
- Neurology, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Neil P Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK
| | - Hugo Vrenken
- Amsterdam UMC, Location VUmc, Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Joep Killestein
- Neurology, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Amsterdam UMC, Location VUmc, Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands; Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK; Institute of Neurology, UCL, London, UK
| | - Viktor Wottschel
- Amsterdam UMC, Location VUmc, Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands.
| |
Collapse
|
43
|
Rasoanandrianina H, Demortière S, Trabelsi A, Ranjeva JP, Girard O, Duhamel G, Guye M, Pelletier J, Audoin B, Callot V. Sensitivity of the Inhomogeneous Magnetization Transfer Imaging Technique to Spinal Cord Damage in Multiple Sclerosis. AJNR Am J Neuroradiol 2020; 41:929-937. [PMID: 32414903 DOI: 10.3174/ajnr.a6554] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 03/12/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND AND PURPOSE The inhomogeneous magnetization transfer technique has demonstrated high specificity for myelin, and has shown sensitivity to multiple sclerosis-related impairment in brain tissue. Our aim was to investigate its sensitivity to spinal cord impairment in MS relative to more established MR imaging techniques (volumetry, magnetization transfer, DTI). MATERIALS AND METHODS Anatomic images covering the cervical spinal cord from the C1 to C6 levels and DTI, magnetization transfer/inhomogeneous magnetization transfer images at the C2/C5 levels were acquired in 19 patients with MS and 19 paired healthy controls. Anatomic images were segmented in spinal cord GM and WM, both manually and using the AMU40 atlases. MS lesions were manually delineated. MR metrics were analyzed within normal-appearing and lesion regions in anterolateral and posterolateral WM and compared using Wilcoxon rank tests and z scores. Correlations between MR metrics and clinical scores in patients with MS were evaluated using the Spearman rank correlation. RESULTS AMU40-based C1-to-C6 GM/WM automatic segmentations in patients with MS were evaluated relative to manual delineation. Mean Dice coefficients were 0.75/0.89, respectively. All MR metrics (WM/GM cross-sectional areas, normal-appearing and lesion diffusivities, and magnetization transfer/inhomogeneous magnetization transfer ratios) were observed altered in patients compared with controls (P < .05). Additionally, the absolute inhomogeneous magnetization transfer ratio z scores were significantly higher than those of the other MR metrics (P < .0001), suggesting a higher inhomogeneous magnetization transfer sensitivity toward spinal cord impairment in MS. Significant correlations with the Expanded Disability Status Scale (ρ = -0.73/P = .02, ρ = -0.81/P = .004) and the total Medical Research Council scale (ρ = 0.80/P = .009, ρ = -0.74/P = .02) were observed for inhomogeneous magnetization transfer and magnetization transfer ratio z scores, respectively, in normal-appearing WM regions, while weaker and nonsignificant correlations were obtained for DTI metrics. CONCLUSIONS With inhomogeneous magnetization transfer being highly sensitive to spinal cord damage in MS compared with conventional magnetization transfer and DTI, it could generate great clinical interest for longitudinal follow-up and potential remyelinating clinical trials. In line with other advanced myelin techniques with which it could be compared, it opens perspectives for multicentric investigations.
Collapse
Affiliation(s)
- H Rasoanandrianina
- From the Center for Magnetic Resonance in Biology and Medicine (H.R., S.D., A.T., J.P.R., O.G., G.D., M.G., J.P., B.A., V.C.), Centre National de la Recherche Scientifique, Aix-Marseille Université, Marseille, France.,Centre d'Exploration Métabolique par Résonance Magnétique (H.R., S.D., A.T., J.P.R., O.G., G.D., M.G., J.P., B.A., V.C.), Assistance Publique-Hopitaux de Marseille, Hôpital Universitaire Timone, Marseille, France.,Laboratoire de Biomécanique Appliquée, Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Reseaux, Aix-Marseille Université; iLab-Spine International Associated Laboratory (H.R., J.P.R., V.C.), Marseille-Montreal, France-Canada
| | - S Demortière
- From the Center for Magnetic Resonance in Biology and Medicine (H.R., S.D., A.T., J.P.R., O.G., G.D., M.G., J.P., B.A., V.C.), Centre National de la Recherche Scientifique, Aix-Marseille Université, Marseille, France.,Centre d'Exploration Métabolique par Résonance Magnétique (H.R., S.D., A.T., J.P.R., O.G., G.D., M.G., J.P., B.A., V.C.), Assistance Publique-Hopitaux de Marseille, Hôpital Universitaire Timone, Marseille, France.,Department of Neurology (S.D., J.P., B.A.), Centre Hospitalier Universitaire Timone, Assistance Publique-Hopitaux de Marseille, Marseille, France
| | - A Trabelsi
- From the Center for Magnetic Resonance in Biology and Medicine (H.R., S.D., A.T., J.P.R., O.G., G.D., M.G., J.P., B.A., V.C.), Centre National de la Recherche Scientifique, Aix-Marseille Université, Marseille, France.,Centre d'Exploration Métabolique par Résonance Magnétique (H.R., S.D., A.T., J.P.R., O.G., G.D., M.G., J.P., B.A., V.C.), Assistance Publique-Hopitaux de Marseille, Hôpital Universitaire Timone, Marseille, France
| | - J P Ranjeva
- From the Center for Magnetic Resonance in Biology and Medicine (H.R., S.D., A.T., J.P.R., O.G., G.D., M.G., J.P., B.A., V.C.), Centre National de la Recherche Scientifique, Aix-Marseille Université, Marseille, France.,Centre d'Exploration Métabolique par Résonance Magnétique (H.R., S.D., A.T., J.P.R., O.G., G.D., M.G., J.P., B.A., V.C.), Assistance Publique-Hopitaux de Marseille, Hôpital Universitaire Timone, Marseille, France.,Laboratoire de Biomécanique Appliquée, Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Reseaux, Aix-Marseille Université; iLab-Spine International Associated Laboratory (H.R., J.P.R., V.C.), Marseille-Montreal, France-Canada
| | - O Girard
- From the Center for Magnetic Resonance in Biology and Medicine (H.R., S.D., A.T., J.P.R., O.G., G.D., M.G., J.P., B.A., V.C.), Centre National de la Recherche Scientifique, Aix-Marseille Université, Marseille, France.,Centre d'Exploration Métabolique par Résonance Magnétique (H.R., S.D., A.T., J.P.R., O.G., G.D., M.G., J.P., B.A., V.C.), Assistance Publique-Hopitaux de Marseille, Hôpital Universitaire Timone, Marseille, France
| | - G Duhamel
- From the Center for Magnetic Resonance in Biology and Medicine (H.R., S.D., A.T., J.P.R., O.G., G.D., M.G., J.P., B.A., V.C.), Centre National de la Recherche Scientifique, Aix-Marseille Université, Marseille, France.,Centre d'Exploration Métabolique par Résonance Magnétique (H.R., S.D., A.T., J.P.R., O.G., G.D., M.G., J.P., B.A., V.C.), Assistance Publique-Hopitaux de Marseille, Hôpital Universitaire Timone, Marseille, France
| | - M Guye
- From the Center for Magnetic Resonance in Biology and Medicine (H.R., S.D., A.T., J.P.R., O.G., G.D., M.G., J.P., B.A., V.C.), Centre National de la Recherche Scientifique, Aix-Marseille Université, Marseille, France.,Centre d'Exploration Métabolique par Résonance Magnétique (H.R., S.D., A.T., J.P.R., O.G., G.D., M.G., J.P., B.A., V.C.), Assistance Publique-Hopitaux de Marseille, Hôpital Universitaire Timone, Marseille, France
| | - J Pelletier
- From the Center for Magnetic Resonance in Biology and Medicine (H.R., S.D., A.T., J.P.R., O.G., G.D., M.G., J.P., B.A., V.C.), Centre National de la Recherche Scientifique, Aix-Marseille Université, Marseille, France.,Centre d'Exploration Métabolique par Résonance Magnétique (H.R., S.D., A.T., J.P.R., O.G., G.D., M.G., J.P., B.A., V.C.), Assistance Publique-Hopitaux de Marseille, Hôpital Universitaire Timone, Marseille, France.,Department of Neurology (S.D., J.P., B.A.), Centre Hospitalier Universitaire Timone, Assistance Publique-Hopitaux de Marseille, Marseille, France
| | - B Audoin
- From the Center for Magnetic Resonance in Biology and Medicine (H.R., S.D., A.T., J.P.R., O.G., G.D., M.G., J.P., B.A., V.C.), Centre National de la Recherche Scientifique, Aix-Marseille Université, Marseille, France.,Centre d'Exploration Métabolique par Résonance Magnétique (H.R., S.D., A.T., J.P.R., O.G., G.D., M.G., J.P., B.A., V.C.), Assistance Publique-Hopitaux de Marseille, Hôpital Universitaire Timone, Marseille, France.,Department of Neurology (S.D., J.P., B.A.), Centre Hospitalier Universitaire Timone, Assistance Publique-Hopitaux de Marseille, Marseille, France
| | - V Callot
- From the Center for Magnetic Resonance in Biology and Medicine (H.R., S.D., A.T., J.P.R., O.G., G.D., M.G., J.P., B.A., V.C.), Centre National de la Recherche Scientifique, Aix-Marseille Université, Marseille, France .,Centre d'Exploration Métabolique par Résonance Magnétique (H.R., S.D., A.T., J.P.R., O.G., G.D., M.G., J.P., B.A., V.C.), Assistance Publique-Hopitaux de Marseille, Hôpital Universitaire Timone, Marseille, France.,Laboratoire de Biomécanique Appliquée, Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Reseaux, Aix-Marseille Université; iLab-Spine International Associated Laboratory (H.R., J.P.R., V.C.), Marseille-Montreal, France-Canada
| |
Collapse
|
44
|
Wu M, Wang C, Lin P, Chao T. Technical Note: Optimization of quantitative susceptibility mapping by streaking artifact detection. Med Phys 2020; 47:5715-5722. [DOI: 10.1002/mp.14460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 07/22/2020] [Accepted: 07/22/2020] [Indexed: 11/10/2022] Open
Affiliation(s)
- Ming‐Long Wu
- Department of Computer Science and Information Engineering National Cheng Kung University No. 1, University Road Tainan 70101 Taiwan
- Institute of Medical Informatics National Cheng Kung University No. 1, University Road Tainan 70101 Taiwan
| | - Chun‐Kun Wang
- Institute of Medical Informatics National Cheng Kung University No. 1, University Road Tainan 70101 Taiwan
| | - Po‐Yu Lin
- Department of Computer Science and Information Engineering National Cheng Kung University No. 1, University Road Tainan 70101 Taiwan
| | - Tzu‐Cheng Chao
- Department of Radiology Mayo Clinic 200 First St. SW Rochester MN 55905 USA
| |
Collapse
|
45
|
Meinhold W, Martinez DE, Oshinski J, Hu AP, Ueda J. A Direct Drive Parallel Plane Piezoelectric Needle Positioning Robot for MRI Guided Intraspinal Injection. IEEE Trans Biomed Eng 2020; 68:807-814. [PMID: 32870782 DOI: 10.1109/tbme.2020.3020926] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Recent developments in the field of cellular therapeutics have indicated the potential of stem cell injections directly to the spinal cord. Injections require either open surgery or a Magnetic Resonance Imaging (MRI) guided injection. Needle positioning during MRI imaging is a significant hurdle to direct spinal injection, as the small target region and interlaminar space require high positioning accuracy. OBJECTIVE To improve both the procedure time and positioning accuracy, an MRI guided robotic needle positioning system is developed. METHODS The robot uses linear piezoelectric motors to directly drive a parallel plane positioning mechanism. Feedback is provided through MRI during the orientation procedure. Both accuracy and repeatability of the robot are characterized. RESULTS This system is found to be capable of repeatability below 51 μm. Needle endpoint error is limited by imaging modality, but is validated to 156 μm. CONCLUSION The reported robot and MRI image feedback system is capable of repeatable and accurate needle guide positioning. SIGNIFICANCE This high accuracy will result in a significant improvement to the workflow of spinal injection procedures.
Collapse
|
46
|
Grussu F, Battiston M, Veraart J, Schneider T, Cohen-Adad J, Shepherd TM, Alexander DC, Fieremans E, Novikov DS, Gandini Wheeler-Kingshott CAM. Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising. Neuroimage 2020; 217:116884. [PMID: 32360689 PMCID: PMC7378937 DOI: 10.1016/j.neuroimage.2020.116884] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 03/18/2020] [Accepted: 04/23/2020] [Indexed: 12/11/2022] Open
Abstract
Multi-parametric quantitative MRI (qMRI) of the spinal cord is a promising non-invasive tool to probe early microstructural damage in neurological disorders. It is usually performed in vivo by combining acquisitions with multiple signal readouts, which exhibit different thermal noise levels, geometrical distortions and susceptibility to physiological noise. This ultimately hinders joint multi-contrast modelling and makes the geometric correspondence of parametric maps challenging. We propose an approach to overcome these limitations, by implementing state-of-the-art microstructural MRI of the spinal cord with a unified signal readout in vivo (i.e. with matched spatial encoding parameters across a range of imaging contrasts). We base our acquisition on single-shot echo planar imaging with reduced field-of-view, and obtain data from two different vendors (vendor 1: Philips Achieva; vendor 2: Siemens Prisma). Importantly, the unified acquisition allows us to compare signal and noise across contrasts, thus enabling overall quality enhancement via multi-contrast image denoising methods. As a proof-of-concept, here we provide a demonstration with one such method, known as Marchenko-Pastur (MP) Principal Component Analysis (PCA) denoising. MP-PCA is a singular value (SV) decomposition truncation approach that relies on redundant acquisitions, i.e. such that the number of measurements is large compared to the number of components that are maintained in the truncated SV decomposition. Here we used in vivo and synthetic data to test whether a unified readout enables more efficient MP-PCA denoising of less redundant acquisitions, since these can be denoised jointly with more redundant ones. We demonstrate that a unified readout provides robust multi-parametric maps, including diffusion and kurtosis tensors from diffusion MRI, myelin metrics from two-pool magnetisation transfer, and T1 and T2 from relaxometry. Moreover, we show that MP-PCA improves the quality of our multi-contrast acquisitions, since it reduces the coefficient of variation (i.e. variability) by up to 17% for mean kurtosis, 8% for bound pool fraction (myelin-sensitive), and 13% for T1, while enabling more efficient denoising of modalities limited in redundancy (e.g. relaxometry). In conclusion, multi-parametric spinal cord qMRI with unified readout is feasible and provides robust microstructural metrics with matched resolution and distortions, whose quality benefits from multi-contrast denoising methods such as MP-PCA.
Collapse
Affiliation(s)
- Francesco Grussu
- Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK; Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
| | - Marco Battiston
- Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Jelle Veraart
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | | | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, Canada
| | - Timothy M Shepherd
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | - Claudia A M Gandini Wheeler-Kingshott
- Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK; Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| |
Collapse
|
47
|
Geldschläger O, Bosch D, Avdievich NI, Henning A. Ultrahigh-resolution quantitative spinal cord MRI at 9.4T. Magn Reson Med 2020; 85:1013-1027. [PMID: 32789980 DOI: 10.1002/mrm.28455] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 07/06/2020] [Accepted: 07/10/2020] [Indexed: 11/08/2022]
Abstract
PURPOSE To present the results of the first human spinal cord in vivo MRI scans at 9.4T. METHODS A human brain coil was used to image the human spinal cord at 9.4T. All anatomical images were acquired with a T2 *-weighted gradient-echo sequence. A comparison of the influence of four different B0 shimming routines on the image quality was performed. Intrinsic signal-to-noise-ratio maps were determined using a pseudo-multiple replica approach. Measurements with different echo times were compared and processed to one multiecho data image combination image. Based on the multiecho acquisitions, T2 *-relaxation time maps were calculated. Algorithmic spinal cord detection and gray matter/white matter segmentation were tested. RESULTS An echo time between 9 and 13.8 ms compromised best between gray matter/white matter contrast and image quality. A maximum in-plane resolution of 0.15 × 0.15 mm2 was achieved for anatomical images. These images offered excellent image quality and made small structures of the spinal cord visible. The scanner vendor implemented B0 shimming routine performed best during this work. Intrinsic signal-to-noise-ratio values of between 6600 and 8060 at the upper cervical spinal cord were achieved. Detection and segmentation worked reliably. An average T2 *-time of 24.88 ms ± 6.68 ms for gray matter and 19.37 ms ± 8.66 ms for white matter was calculated. CONCLUSION The proposed human brain coil can be used to image the spinal cord. The maximum in-plane resolution in this work was higher compared with the 7T results from the literature. The 9.4T acquisitions made the small structures of the spinal cord clearly visible.
Collapse
Affiliation(s)
- Ole Geldschläger
- High-Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Dario Bosch
- High-Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Biomedical Magnetic Resonance, University Hospital Tübingen, Tübingen, Germany
| | - Nikolai I Avdievich
- High-Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Anke Henning
- High-Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.,Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| |
Collapse
|
48
|
Sabaghian S, Dehghani H, Batouli SAH, Khatibi A, Oghabian MA. Fully automatic 3D segmentation of the thoracolumbar spinal cord and the vertebral canal from T2-weighted MRI using K-means clustering algorithm. Spinal Cord 2020; 58:811-820. [PMID: 32132652 DOI: 10.1038/s41393-020-0429-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Revised: 01/26/2020] [Accepted: 01/27/2020] [Indexed: 11/09/2022]
Abstract
STUDY DESIGN Method development. OBJECTIVES To develop a reliable protocol for automatic segmentation of Thoracolumbar spinal cord using MRI based on K-means clustering algorithm in 3D images. SETTING University-based laboratory, Tehran, Iran. METHODS T2 structural volumes acquired from the spinal cord of 20 uninjured volunteers on a 3T MR scanner. We proposed an automatic method for spinal cord segmentation based on the K-means clustering algorithm in 3D images and compare our results with two available segmentation methods (PropSeg, DeepSeg) implemented in the Spinal Cord Toolbox. Dice and Hausdorff were used to compare the results of our method (K-Seg) with the manual segmentation, PropSeg, and DeepSeg. RESULTS The accuracy of our automatic segmentation method for T2-weighted images was significantly better or similar to the SCT methods, in terms of 3D DC (p < 0.001). The 3D DCs were respectively (0.81 ± 0.04) and Hausdorff Distance (12.3 ± 2.48) by the K-Seg method in contrary to other SCT methods for T2-weighted images. CONCLUSIONS The output with similar protocols showed that K-Seg results match the manual segmentation better than the other methods especially on the thoracolumbar levels in the spinal cord due to the low image contrast as a result of poor SNR in these areas.
Collapse
Affiliation(s)
- Sahar Sabaghian
- Department of Software, School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
- Neuro Imaging and Analysis Group (NIAG), Research Center for Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences, Tehran, Iran
| | - Hamed Dehghani
- Neuro Imaging and Analysis Group (NIAG), Research Center for Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences, Tehran, Iran
- Department of Medical Physics and Biomedical Engineering, Faculty of Advanced Technologies in Medicine, Tehran University of Medical Science, Tehran, Iran
| | - Seyed Amir Hossein Batouli
- Neuro Imaging and Analysis Group (NIAG), Research Center for Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences, Tehran, Iran
- Department of Neuroscience and Addiction studies, School of Advanced Technologies in Medicine, Tehran University of Medical Science, Tehran, Iran
| | - Ali Khatibi
- Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, Birmingham, UK
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK
| | - Mohammad Ali Oghabian
- Neuro Imaging and Analysis Group (NIAG), Research Center for Molecular and Cellular Imaging (RCMCI), Tehran University of Medical Sciences, Tehran, Iran.
- Department of Medical Physics and Biomedical Engineering, Faculty of Advanced Technologies in Medicine, Tehran University of Medical Science, Tehran, Iran.
| |
Collapse
|
49
|
Bonacchi R, Pagani E, Meani A, Cacciaguerra L, Preziosa P, De Meo E, Filippi M, Rocca MA. Clinical Relevance of Multiparametric MRI Assessment of Cervical Cord Damage in Multiple Sclerosis. Radiology 2020; 296:605-615. [PMID: 32573387 DOI: 10.1148/radiol.2020200430] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Background In multiple sclerosis (MS), knowledge about how spinal cord abnormalities translate into clinical manifestations is incomplete. Comprehensive, multiparametric MRI studies are useful in this perspective, but studies for the spinal cord are lacking. Purpose To identify MRI features of cervical spinal cord damage that could help predict disability and disease course in MS by using a comprehensive, multiparametric MRI approach. Materials and Methods In this retrospective hypothesis-driven analysis of longitudinally acquired data between June 2017 and April 2019, 120 patients with MS (58 with relapsing-remitting MS [RRMS] and 62 with progressive MS [PMS]) and 30 age- and sex-matched healthy control participants underwent 3.0-T MRI of the brain and cervical spinal cord. Cervical spinal cord MRI was performed with three-dimensional (3D) T1-weighted, T2-weighted, and diffusion-weighted imaging; sagittal two-dimensional (2D) short inversion time inversion-recovery imaging; and axial 2D phase-sensitive inversion-recovery imaging at the C2-C3 level. Brain MRI was performed with 3D T1-weighted, fluid-attenuated inversion-recovery and T2-weighted sequences. Associations between MRI variables and disability were explored with age-, sex- and phenotype-adjusted linear models. Results In patients with MS, multivariable analysis identified phenotype, cervical spinal cord gray matter (GM) cross-sectional area (CSA), lateral funiculi fractional anisotropy (FA), and brain GM volume as independent predictors of Expanded Disability Status Scale (EDSS) score (R2 = 0.86). The independent predictors of EDSS score in RRMS were lateral funiculi FA, normalized brain volume, and cervical spinal cord GM T2 lesion volume (R2 = 0.51). The independent predictors of EDSS score in PMS were cervical spinal cord GM CSA and brain GM volume (R2 = 0.44). Logistic regression analysis identified cervical spinal cord GM CSA and T2 lesion volume as independent predictors of phenotype (area under the receiver operating characteristic curve = 0.95). An optimal cervical spinal cord GM CSA cut-off value of 11.1 mm2 was found to enable accurate differentiation of patients with PMS, having values below the threshold, from those with RRMS (sensitivity = 90% [56 of 62], specificity = 91% [53 of 58]). Conclusion Cervical spinal cord MRI involvement has a central role in explaining disability in multiple sclerosis (MS): Lesion-induced damage in the lateral funiculi and gray matter (GM) in relapsing-remitting MS and GM atrophy in patients with progressive MS are the most relevant variables. Cervical spinal cord GM atrophy is an accurate predictor of progressive phenotype. Cervical spinal cord GM lesions may subsequently cause GM atrophy, which may contribute to evolution to PMS. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Zivadinov and Bergsland in this issue.
Collapse
Affiliation(s)
- Raffaello Bonacchi
- From the Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience (R.B., E.P., A.M., L.C., P.P., E.D.M., M.F., M.A.R.), Neurology Unit (R.B., L.C., P.P., E.D.M., M.F., M.A.R.), and Neurophysiology Unit (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan 20132, Italy; and Vita-Salute San Raffaele University, Milan, Italy (R.B., L.C., E.D.M., M.F.)
| | - Elisabetta Pagani
- From the Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience (R.B., E.P., A.M., L.C., P.P., E.D.M., M.F., M.A.R.), Neurology Unit (R.B., L.C., P.P., E.D.M., M.F., M.A.R.), and Neurophysiology Unit (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan 20132, Italy; and Vita-Salute San Raffaele University, Milan, Italy (R.B., L.C., E.D.M., M.F.)
| | - Alessandro Meani
- From the Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience (R.B., E.P., A.M., L.C., P.P., E.D.M., M.F., M.A.R.), Neurology Unit (R.B., L.C., P.P., E.D.M., M.F., M.A.R.), and Neurophysiology Unit (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan 20132, Italy; and Vita-Salute San Raffaele University, Milan, Italy (R.B., L.C., E.D.M., M.F.)
| | - Laura Cacciaguerra
- From the Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience (R.B., E.P., A.M., L.C., P.P., E.D.M., M.F., M.A.R.), Neurology Unit (R.B., L.C., P.P., E.D.M., M.F., M.A.R.), and Neurophysiology Unit (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan 20132, Italy; and Vita-Salute San Raffaele University, Milan, Italy (R.B., L.C., E.D.M., M.F.)
| | - Paolo Preziosa
- From the Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience (R.B., E.P., A.M., L.C., P.P., E.D.M., M.F., M.A.R.), Neurology Unit (R.B., L.C., P.P., E.D.M., M.F., M.A.R.), and Neurophysiology Unit (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan 20132, Italy; and Vita-Salute San Raffaele University, Milan, Italy (R.B., L.C., E.D.M., M.F.)
| | - Ermelinda De Meo
- From the Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience (R.B., E.P., A.M., L.C., P.P., E.D.M., M.F., M.A.R.), Neurology Unit (R.B., L.C., P.P., E.D.M., M.F., M.A.R.), and Neurophysiology Unit (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan 20132, Italy; and Vita-Salute San Raffaele University, Milan, Italy (R.B., L.C., E.D.M., M.F.)
| | - Massimo Filippi
- From the Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience (R.B., E.P., A.M., L.C., P.P., E.D.M., M.F., M.A.R.), Neurology Unit (R.B., L.C., P.P., E.D.M., M.F., M.A.R.), and Neurophysiology Unit (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan 20132, Italy; and Vita-Salute San Raffaele University, Milan, Italy (R.B., L.C., E.D.M., M.F.)
| | - Maria A Rocca
- From the Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience (R.B., E.P., A.M., L.C., P.P., E.D.M., M.F., M.A.R.), Neurology Unit (R.B., L.C., P.P., E.D.M., M.F., M.A.R.), and Neurophysiology Unit (M.F.), IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan 20132, Italy; and Vita-Salute San Raffaele University, Milan, Italy (R.B., L.C., E.D.M., M.F.)
| |
Collapse
|
50
|
Solstrand Dahlberg L, Viessmann O, Linnman C. Heritability of cervical spinal cord structure. Neurol Genet 2020; 6:e401. [PMID: 32185240 PMCID: PMC7061306 DOI: 10.1212/nxg.0000000000000401] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 01/13/2020] [Indexed: 12/04/2022]
Abstract
OBJECTIVE Measures of spinal cord structure can be a useful phenotype to track disease severity and development; this observational study measures the hereditability of cervical spinal cord anatomy and its correlates in healthy human beings. METHODS Twin data from the Human Connectome Project were analyzed with semiautomated spinal cord segmentation, evaluating test-retest reliability and broad-sense heritability with an AE model. Relationships between spinal cord metrics, general physical measures, regional brain structural measures, and motor function were assessed. RESULTS We found that the spinal cord C2 cross-sectional area (CSA), left-right width (LRW), and anterior-posterior width (APW) are highly heritable (85%-91%). All measures were highly correlated with the brain volume, and CSA only was positively correlated with thalamic volumes (p = 0.005) but negatively correlated with the occipital cortex area (p = 0.001). LRW was correlated with the participant's height (p = 0.00027). The subjects' sex significantly influenced these metrics. Analyses of a test-retest data set confirmed validity of the approach. CONCLUSIONS This study provides the evidence of genetic influence on spinal cord structure. MRI metrics of cervical spinal cord anatomy are robust and not easily influenced by nonpathological environmental factors, providing a useful metric for monitoring normal development and progression of neurodegenerative disorders affecting the spinal cord, including-but not limited to-spinal cord injury and MS.
Collapse
Affiliation(s)
- Linda Solstrand Dahlberg
- Department of Anesthesiology, Perioperative and Pain Medicine (L.S.D., C.L.), Boston Children's Hospital, Harvard Medical School, MA; Departments of Psychiatry and Radiology (L.S.D., C.L.), Massachusetts General Hospital, Harvard Medical School; Department of Neurology and Neurosurgery (L.S.D.), Montreal Neurological Institute, McGill University, Canada; Athinoula A. Martinos Center for Biomedical Imaging (O.V.), Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, Boston; and Spaulding Neuroimaging Lab (C.L.), Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA
| | - Olivia Viessmann
- Department of Anesthesiology, Perioperative and Pain Medicine (L.S.D., C.L.), Boston Children's Hospital, Harvard Medical School, MA; Departments of Psychiatry and Radiology (L.S.D., C.L.), Massachusetts General Hospital, Harvard Medical School; Department of Neurology and Neurosurgery (L.S.D.), Montreal Neurological Institute, McGill University, Canada; Athinoula A. Martinos Center for Biomedical Imaging (O.V.), Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, Boston; and Spaulding Neuroimaging Lab (C.L.), Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA
| | - Clas Linnman
- Department of Anesthesiology, Perioperative and Pain Medicine (L.S.D., C.L.), Boston Children's Hospital, Harvard Medical School, MA; Departments of Psychiatry and Radiology (L.S.D., C.L.), Massachusetts General Hospital, Harvard Medical School; Department of Neurology and Neurosurgery (L.S.D.), Montreal Neurological Institute, McGill University, Canada; Athinoula A. Martinos Center for Biomedical Imaging (O.V.), Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, Boston; and Spaulding Neuroimaging Lab (C.L.), Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA
| |
Collapse
|