1
|
Pitman J, Fayad LM, Ahlawat S. A neuromuscular clinician's guide to magnetic resonance neurography. Muscle Nerve 2025; 71:293-308. [PMID: 39479875 DOI: 10.1002/mus.28283] [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: 01/10/2024] [Revised: 10/04/2024] [Accepted: 10/13/2024] [Indexed: 02/07/2025]
Abstract
Magnetic resonance neurography (MRN) is increasingly used in clinical practice for the evaluation of patients with a wide spectrum of peripheral nerve disorders. This review article discusses the technical aspects of MRN highlighting the core sequences performed for clinical care. A robust, high-resolution, heavily T2-weighted fluid-sensitive sequence performed on a 3.0 Tesla magnet system remains the main workhorse MRN sequence. In specific clinical scenarios, adjunct techniques such as diffusion-weighted imaging can be added to a protocol for disease characterization. In addition, gadolinium-based contrast material can also be administered for the purposes of image optimization (suppress adjacent vascular signal) and disease characterization. Technical modifications to field of view and planes of imaging can be made based on the clinical question and discussion with the radiologist(s). On fluid-sensitive MRN sequences, a normal peripheral nerve exhibits iso- to minimally hyperintense signal relative to skeletal muscle with a predictable trajectory, preserved "fascicular" architecture, and tapered caliber from proximal to distal. Peripheral nerve abnormalities on MRN include alterations in signal, caliber, architecture, diffusion characteristics as well as enhancement and provide information regarding the underlying etiology. Although some MRN findings including nerve hyperintensity and long-segmental enlargement are nonspecific, there are certain diagnoses that can be made with high certainty based on imaging including benign peripheral nerve tumors, high-grade peripheral nerve injury, and intraneural ganglia. The purpose of this article is to familiarize a neuromuscular clinician with fundamentals of MRN acquisition and interpretation to facilitate communication with the neuromuscular radiologist and optimize patient care.
Collapse
Affiliation(s)
- Jenifer Pitman
- Musculoskeletal Imaging Division, The Russell H. Morgan Department of Radiology & Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Laura M Fayad
- Musculoskeletal Imaging Division, The Russell H. Morgan Department of Radiology & Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Orthopaedic Surgery, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Shivani Ahlawat
- Musculoskeletal Imaging Division, The Russell H. Morgan Department of Radiology & Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| |
Collapse
|
2
|
Beste NC, Jende J, Kronlage M, Kurz F, Heiland S, Bendszus M, Meredig H. Automated peripheral nerve segmentation for MR-neurography. Eur Radiol Exp 2024; 8:97. [PMID: 39186183 PMCID: PMC11347527 DOI: 10.1186/s41747-024-00503-8] [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: 12/18/2023] [Accepted: 08/01/2024] [Indexed: 08/27/2024] Open
Abstract
BACKGROUND Magnetic resonance neurography (MRN) is increasingly used as a diagnostic tool for peripheral neuropathies. Quantitative measures enhance MRN interpretation but require nerve segmentation which is time-consuming and error-prone and has not become clinical routine. In this study, we applied neural networks for the automated segmentation of peripheral nerves. METHODS A neural segmentation network was trained to segment the sciatic nerve and its proximal branches on the MRN scans of the right and left upper leg of 35 healthy individuals, resulting in 70 training examples, via 5-fold cross-validation (CV). The model performance was evaluated on an independent test set of one-sided MRN scans of 60 healthy individuals. RESULTS Mean Dice similarity coefficient (DSC) in CV was 0.892 (95% confidence interval [CI]: 0.888-0.897) with a mean Jaccard index (JI) of 0.806 (95% CI: 0.799-0.814) and mean Hausdorff distance (HD) of 2.146 (95% CI: 2.184-2.208). For the independent test set, DSC and JI were lower while HD was higher, with a mean DSC of 0.789 (95% CI: 0.760-0.815), mean JI of 0.672 (95% CI: 0.642-0.699), and mean HD of 2.118 (95% CI: 2.047-2.190). CONCLUSION The deep learning-based segmentation model showed a good performance for the task of nerve segmentation. Future work will focus on extending training data and including individuals with peripheral neuropathies in training to enable advanced peripheral nerve disease characterization. RELEVANCE STATEMENT The results will serve as a baseline to build upon while developing an automated quantitative MRN feature analysis framework for application in routine reading of MRN examinations. KEY POINTS Quantitative measures enhance MRN interpretation, requiring complex and challenging nerve segmentation. We present a deep learning-based segmentation model with good performance. Our results may serve as a baseline for clinical automated quantitative MRN segmentation.
Collapse
Affiliation(s)
- Nedim Christoph Beste
- Institute of Neuroradiology, University Hospital of Heidelberg, Heidelberg, Germany.
| | - Johann Jende
- Institute of Neuroradiology, University Hospital of Heidelberg, Heidelberg, Germany
| | - Moritz Kronlage
- Institute of Neuroradiology, University Hospital of Heidelberg, Heidelberg, Germany
| | - Felix Kurz
- DKFZ German Cancer Research Center, Heidelberg, Germany
| | - Sabine Heiland
- Institute of Neuroradiology, University Hospital of Heidelberg, Heidelberg, Germany
| | - Martin Bendszus
- Institute of Neuroradiology, University Hospital of Heidelberg, Heidelberg, Germany
| | - Hagen Meredig
- Institute of Neuroradiology, University Hospital of Heidelberg, Heidelberg, Germany
| |
Collapse
|
3
|
Yang H, Son NH, Kim D, Chun JH, Kim JS, Oh TK, Lee M, Kim HJ. Assessment of traumatic mandibular nerve using MR neurography sequence: a preliminary study. BMC Oral Health 2024; 24:750. [PMID: 38943102 PMCID: PMC11214249 DOI: 10.1186/s12903-024-04514-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 06/21/2024] [Indexed: 07/01/2024] Open
Abstract
BACKGROUND Iatrogenic mandibular nerve damage resulting from oral surgeries and dental procedures is painful and a formidable challenge for patients and oral surgeons alike, mainly because the absence of objective and quantitative methods for diagnosing nerve damage renders treatment and compensation ambiguous while often leading to medico-legal disputes. The aim of this study was to examine discriminating factors of traumatic mandibular nerve within a specific magnetic resonance imaging (MRI) protocol and to suggest tangible diagnostic criteria for peripheral trigeminal nerve injury. METHODS Twenty-six patients with ipsilateral mandibular nerve trauma underwent T2 Flex water, 3D short tau inversion recovery (STIR), and diffusion-weighted imaging (DWI) acquired by periodically rotating overlapping parallel lines with enhanced reconstruction (PROPELLER) pulse sequences; 26 injured nerves were thus compared with contra-lateral healthy nerves at anatomically corresponding sites. T2 Flex apparent signal to noise ratio (FSNR), T2 Flex apparent nerve-muscle contrast to noise ratio (FNMCNR) 3D STIR apparent signal to noise ratio (SSNR), 3D STIR apparent nerve-muscle contrast to noise ratio (SNMCNR), apparent diffusion coefficient (ADC) and area of cross-sectional nerve (Area) were evaluated. RESULTS Mixed model analysis revealed FSNR and FNMCNR to be the dual discriminators for traumatized mandibular nerve (p < 0.05). Diagnostic performance of both parameters was also determined with area under the receiver operating characteristic curve (AUC for FSNR = 0.712; 95% confidence interval [CI]: 0.5660, 0.8571 / AUC for FNMCNR = 0.7056; 95% confidence interval [CI]: 1.011, 1.112). CONCLUSIONS An increase in FSNR and FNMCNR within our MRI sequence seems to be accurate indicators of the presence of traumatic nerve. This prospective study may serve as a foundation for sophisticated model diagnosing trigeminal nerve trauma within large patient cohorts.
Collapse
Affiliation(s)
- Hyunwoo Yang
- Department of Oral and Maxillofacial Surgery, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea
| | - Nak-Hoon Son
- Department of Statistics, Keimyung University, Daegu, Republic of Korea
| | - Dongwook Kim
- Department of Oral and Maxillofacial Surgery, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea
| | - Jae-Hee Chun
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jin Sung Kim
- Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Tae Kyung Oh
- Department of Oral and Maxillofacial Surgery, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea
| | - Minwook Lee
- Department of Radiology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hyung Jun Kim
- Department of Oral and Maxillofacial Surgery, Yonsei University College of Dentistry, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea.
| |
Collapse
|
4
|
Labounek R, Bondy MT, Paulson AL, Bédard S, Abramovic M, Alonso-Ortiz E, Atcheson NT, Barlow LR, Barry RL, Barth M, Battiston M, Büchel C, Budde MD, Callot V, Combes A, De Leener B, Descoteaux M, de Sousa PL, Dostál M, Doyon J, Dvorak AV, 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, Grussu F, Hagiwara A, Henry PG, Horák T, Hori M, Joers JM, Kamiya K, Karbasforoushan H, Keřkovský M, Khatibi A, Kim JW, Kinany N, Kitzler H, Kolind S, Kong Y, Kudlička P, Kuntke P, Kurniawan ND, Kusmia S, Laganà MM, Laule C, Law CSW, Leutritz T, Liu Y, Llufriu S, Mackey S, Martin AR, Martinez-Heras E, Mattera L, 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 GW, Tinnermann A, Valošek J, Van De Ville D, Yiannakas MC, Weber KA, Weiskopf N, Wise RG, Wyss PO, Xu J, Cohen-Adad J, Lenglet C, Nestrašil I. Body size interacts with the structure of the central nervous system: A multi-center in vivo neuroimaging study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.29.591421. [PMID: 38746371 PMCID: PMC11092490 DOI: 10.1101/2024.04.29.591421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controlling for sources of biological variation such as subject's sex and age. However, corrections for body size (i.e. height and weight) are mostly lacking in clinical neuroimaging designs. This study investigates the importance of body size parameters in their relationship with spinal cord (SC) and brain magnetic resonance imaging (MRI) metrics. Data were derived from a cosmopolitan population of 267 healthy human adults (age 30.1±6.6 years old, 125 females). We show that body height correlated strongly or moderately with brain gray matter (GM) volume, cortical GM volume, total cerebellar volume, brainstem volume, and cross-sectional area (CSA) of cervical SC white matter (CSA-WM; 0.44≤r≤0.62). In comparison, age correlated weakly with cortical GM volume, precentral GM volume, and cortical thickness (-0.21≥r≥-0.27). Body weight correlated weakly with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20≥r≥-0.23). Body weight further correlated weakly with the mean diffusivity derived from diffusion tensor imaging (DTI) in SC WM (r=-0.20) and dorsal columns (-0.21), but only in males. CSA-WM correlated strongly or moderately with brain volumes (0.39≤r≤0.64), and weakly with precentral gyrus thickness and DTI-based fractional anisotropy in SC dorsal columns and SC lateral corticospinal tracts (-0.22≥r≥-0.25). Linear mixture of sex and age explained 26±10% of data variance in brain volumetry and SC CSA. The amount of explained variance increased at 33±11% when body height was added into the mixture model. Age itself explained only 2±2% of such variance. In conclusion, body size is a significant biological variable. Along with sex and age, body size should therefore be included as a mandatory variable in the design of clinical neuroimaging studies examining SC and brain structure.
Collapse
Affiliation(s)
- René Labounek
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Monica T. Bondy
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Amy L. Paulson
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Sandrine Bédard
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Mihael Abramovic
- Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Eva Alonso-Ortiz
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Centre de recherche du CHU Sainte-Justine, Université de Montréal, Montreal, QC, Canada
| | - Nicole T Atcheson
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, Australia
| | - Laura R. Barlow
- Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Robert L. Barry
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Harvard-Massachusetts Institute of Technology Health Sciences & Technology, Cambridge, Massachusetts, USA
| | - Markus Barth
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, Australia
- School of Electrical Engineering and Computer Science, The University of Queensland, St Lucia, Australia
| | - Marco Battiston
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK
| | - Christian Büchel
- Department for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Matthew D. Budde
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA
- Clement J. Zablocki Veteran’s Affairs Medical Center, Milwaukee, WI, USA
| | - Virginie Callot
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hopital Universitaire Timone, CEMEREM, Marseille, France
| | - Anna Combes
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK
| | - Benjamin De Leener
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Centre de recherche du CHU Sainte-Justine, Université de Montréal, Montreal, QC, Canada
- Department of Computer Engineering and Software Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science department, Université de Sherbrooke, Sherbrooke, QC, Canada
| | | | - Marek Dostál
- Department of Radiology and Nuclear Medicine, University Hospital Brno and Masaryk University, Czech Republic
- Department of Biophysics, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Julien Doyon
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Adam V. Dvorak
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Falk Eippert
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | | | | | - Patrick Freund
- Spinal Cord Injury Center Balgrist, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Wellcome Trust Centre for Neuroimaging, Queen Square Institute of Neurology, University College London, London, UK
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany
| | - Jürgen Finsterbusch
- Department for 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, Neuroimaging Laboratory, Rome, Italy
| | - Issei Fukunaga
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo 113-8421, Japan
| | - Claudia A. M. Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - GianCarlo Germani
- Advanced Imaging and Artificial Intelligence Center, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Federico Giove
- IRCCS Santa Lucia Foundation, Neuroimaging Laboratory, Rome, Italy
- CREF - Museo storico della fisica e Centro studi e ricerche Enrico Fermi, Rome, Italy
| | - Francesco Grussu
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK
- Vall d’Hebron Institute of Oncology (VHIO), Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo 113-8421, Japan
| | - Pierre-Gilles Henry
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Tomáš Horák
- Faculty of Medicine, Masaryk University, Brno, Czech Republic
- Department of Neurology, University Hospital Brno, Brno, Czech Republic
- Multimodal and Functional Imaging Laboratory, Central European Institute of Technology, Brno, Czech Republic
| | - Masaaki Hori
- Department of Radiology, Juntendo University School of Medicine, 1-2-1, Hongo, Bunkyo, Tokyo 113-8421, Japan
- Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan
| | - James M. Joers
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Kouhei Kamiya
- Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan
| | - Haleh Karbasforoushan
- Department of Neurology, UCSF Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, University Hospital Brno and Masaryk University, Czech Republic
| | - Ali Khatibi
- Centre of Precision Rehabilitation for Spinal Pain (CPR Spine), University of Birmingham, Birmingham, UK
- Centre for Human Brain Health, University of Birmingham, Birmingham, UK
- Institute for Mental Health, University of Birmingham, Birmingham, UK
| | - Joo-won Kim
- Biomedical Engineering and Imaging Institute, Department of Radiology, Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Radiology, Baylor College of Medicine, Houston, Texas, USA
- Department of Psychiatry, Baylor College of Medicine, Houston, Texas, USA
| | - Nawal Kinany
- Neuro-X Institute, Ecole polytechnique fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Switzerland
| | - Hagen Kitzler
- Institute of Diagnostic and Interventional Neuroradiology, Faculty of Medicine and Carl Gustav Carus University Hospital, Technische Universität Dresden, Germany
| | - Shannon Kolind
- Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- Division of Neurology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Yazhuo Kong
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Science, Beijing, 100101, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Petr Kudlička
- Multimodal and Functional Imaging Laboratory, Central European Institute of Technology, Brno, Czech Republic
- First Department of Neurology, St. Anne’s University Hospital and Medical Faculty of Masaryk University, Brno, Czech Republic
| | - Paul Kuntke
- Institute of Diagnostic and Interventional Neuroradiology, Faculty of Medicine and Carl Gustav Carus University Hospital, Technische Universität Dresden, Germany
| | - Nyoman D. Kurniawan
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St Lucia, Australia
| | | | | | - Cornelia Laule
- Department of Radiology, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
- Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, Canada
- International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, Canada
| | | | - Tobias Leutritz
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, China
| | - Sara Llufriu
- Neuroimmunology and Multiple Sclerosis Unit, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic Barcelona, Fundació de Recerca Clínic Barcelona-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, Palo Alto, CA, USA
| | - Allan R. Martin
- Department of Neurological Surgery, University of California, Davis, CA, USA
| | - Eloy Martinez-Heras
- Neuroimmunology and Multiple Sclerosis Unit, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic Barcelona, Fundació de Recerca Clínic Barcelona-IDIBAPS and Universitat de Barcelona. Barcelona, Spain
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d’Hebron, Barcelona, Spain
| | - Loan Mattera
- Fondation Campus Biotech Geneva, Genève, Switzerland
| | - Kristin P. O’Grady
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nico Papinutto
- Department of Neurology, UCSF Weill Institute for Neurosciences, 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
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d’Hebron, Barcelona, Spain
| | - Todd B. Parrish
- Department of Radiology, Northwestern University, Chicago, IL 60611, USA
| | - Anna Pichiecchio
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
- Advanced Imaging and Artificial Intelligence Center, Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
| | - Ferran Prados
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK
- e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
- Centre for Medical Image Computing, University College London, London, UK
| | - Àlex Rovira
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d’Hebron, Barcelona, Spain
| | - Marc J. Ruitenberg
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, St Lucia, Australia
| | - Rebecca S. Samson
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK
| | - Giovanni Savini
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072, Pieve Emanuele (MI), Italy
- Neuroradiology Unit, IRCCS Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089, Rozzano (MI), Italy
| | - Maryam Seif
- Spinal Cord Injury Center Balgrist, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany
| | - Alan C. Seifert
- Biomedical Engineering and Imaging Institute, Department of Radiology, Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, 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 and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN USA
| | - Zachary A. Smith
- Department of Neurosurgery, University of Oklahoma, Oklahoma City, OK, USA
| | - Elisabeth Solana
- Neuroimmunology and Multiple Sclerosis Unit, Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic Barcelona, Fundació de Recerca Clínic Barcelona-IDIBAPS and Universitat de Barcelona. Barcelona, Spain
| | - Yuichi Suzuki
- The University of Tokyo Hospital, Radiology Center, Tokyo, Japan
| | - George W Tackley
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, Wales, UK
| | - Alexandra Tinnermann
- Department for Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - 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, Czech Republic
- Department of Neurology, Faculty of Medicine and Dentistry, Palacký University Olomouc, Olomouc, Czech Republic
| | - Dimitri Van De Ville
- Neuro-X Institute, Ecole polytechnique fédérale de Lausanne, Geneva, Switzerland
- Department of Radiology and Medical Informatics, Faculty of Medicine, University of Geneva, Switzerland
| | - Marios C. Yiannakas
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, UK
| | - Kenneth A. Weber
- Division of Pain Medicine, Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Nikolaus Weiskopf
- Wellcome Trust Centre for Neuroimaging, Queen Square Institute of Neurology, University College London, London, UK
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany
- Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Linnéstraße 5, 04103 Leipzig, Germany
| | - Richard G. Wise
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, Wales, UK
- Department of Neurosciences, Imaging, and Clinical Sciences, ‘G. D’Annunzio’ University of Chieti-Pescara, Chieti, Italy
- Institute for Advanced Biomedical Technologies, ‘G. D’Annunzio’ University of Chieti-Pescara, Chieti, Italy
| | - Patrik O. Wyss
- Department of Radiology, Swiss Paraplegic Centre, Nottwil, Switzerland
| | - Junqian Xu
- Biomedical Engineering and Imaging Institute, Department of Radiology, Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
- Department of Radiology, Baylor College of Medicine, Houston, Texas, USA
- Department of Psychiatry, Baylor College of Medicine, Houston, Texas, USA
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
- Centre de recherche du CHU Sainte-Justine, Université de Montréal, Montreal, QC, Canada
- Mila - Quebec AI Institute, Montreal, QC, Canada
- Functional Neuroimaging Unit, CRIUGM, University of Montreal, Montreal, Canada
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Igor Nestrašil
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| |
Collapse
|
5
|
Chen Y, Baraz J, Xuan SY, Yang X, Castoro R, Xuan Y, Roth AR, Dortch RD, Li J. Multiparametric Quantitative MRI of Peripheral Nerves in the Leg: A Reliability Study. J Magn Reson Imaging 2024; 59:563-574. [PMID: 37191075 PMCID: PMC11188919 DOI: 10.1002/jmri.28778] [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: 03/06/2023] [Revised: 04/30/2023] [Accepted: 05/01/2023] [Indexed: 05/17/2023] Open
Abstract
BACKGROUND Patients with polyneuropathies typically have demyelination and/or axonal degeneration in peripheral nerves. Currently, there is a lack of imaging biomarkers to track the changes in these pathologies. PURPOSE To develop and evaluate the reliability of a multiparametric quantitative magnetic resonance imaging (qMRI) method of peripheral nerves in the leg. STUDY TYPE Prospective. SUBJECTS Seventeen healthy volunteers (36.2 ± 13.8 years old, 9 males) with 10 of them scanned twice for test-retest. FIELD STRENGTH/SEQUENCE 3 T, three-dimensional gradient echo and diffusion tensor imaging. ASSESSMENT A qMRI protocol and processing pipeline was established for quantifying the following nerve parameters that are sensitive to myelin and axonal pathologies: magnetization transfer (MT) ratio (MTR), MT saturation index (MTsat), T2 *, T1 , proton density (PD), fractional anisotropy (FA), and mean/axial/radial diffusivities (MD, AD, and RD). The qMRI protocol also measures the volume of nerve fascicles (fVOL) and the fat fraction (FF) of muscles. STATISTICAL TESTS The intersession reproducibility and inter-rater reliability of each qMRI parameter were assessed by Bland-Altman analysis and intraclass correlation coefficient (ICC). Pairwise Pearson correlation analyses were performed to investigate the intrinsic association between qMRI parameters. Distal-to-proximal variations were evaluated by paired t-tests with Bonferroni-Holm multiple comparison corrections. P < 0.05 was considered statistically significant. RESULTS The MTR, MTsat, T2 *, T1 , PD, FA, AD, and fVOL of the sciatic and tibial nerves, and the FF of leg muscles, had an overall good-to-excellent test-retest agreement (ICC varying from 0.78 to 0.99). All the qMRI parameters had good-to-excellent inter-rater reliability (ICC > 0.80). The data demonstrated a pattern of distal-to-proximal changes of an increased nerve MTsat and FA, and a decreased nerve T1 , PD, MD, and RD, as well as a significantly increased muscle FF. DATA CONCLUSION The proposed multiparametric qMRI method of the peripheral nerves is highly reproducible and provided healthy control data which will be used in developing monitoring biomarkers in patients with polyneuropathies. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
- Yongsheng Chen
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Jacob Baraz
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Stephanie Yan Xuan
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Xue Yang
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Ryan Castoro
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Yang Xuan
- Department of Radiology, Wayne State University School of Medicine, Detroit, MI, USA
| | - Alison R. Roth
- Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Richard D. Dortch
- Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Jun Li
- Department of Neurology, Wayne State University School of Medicine, Detroit, MI, USA
- Department of Neurology, Houston Methodist Research Institute, Houston, TX, USA
| |
Collapse
|
6
|
Telleman JA, Sneag DB, Visser LH. The role of imaging in focal neuropathies. HANDBOOK OF CLINICAL NEUROLOGY 2024; 201:19-42. [PMID: 38697740 DOI: 10.1016/b978-0-323-90108-6.00001-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Electrodiagnostic testing (EDX) has been the diagnostic tool of choice in peripheral nerve disease for many years, but in recent years, peripheral nerve imaging has been used ever more frequently in daily clinical practice. Nerve ultrasound and magnetic resonance (MR) neurography are able to visualize nerve structures reliably. These techniques can aid in localizing nerve pathology and can reveal significant anatomical abnormalities underlying nerve pathology that may have been otherwise undetected by EDX. As such, nerve ultrasound and MR neurography can significantly improve diagnostic accuracy and can have a significant effect on treatment strategy. In this chapter, the basic principles and recent developments of these techniques will be discussed, as well as their potential application in several types of peripheral nerve disease, such as carpal tunnel syndrome (CTS), ulnar neuropathy at the elbow (UNE), radial neuropathy, brachial and lumbosacral plexopathy, neuralgic amyotrophy (NA), fibular, tibial, sciatic, femoral neuropathy, meralgia paresthetica, peripheral nerve trauma, tumors, and inflammatory neuropathies.
Collapse
Affiliation(s)
- Johan A Telleman
- Department of Neurology and Clinical Neurophysiology, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands
| | - Darryl B Sneag
- Department of Radiology and Imaging, Hospital for Special Surgery, New York, NY, United States
| | - Leo H Visser
- Department of Neurology and Clinical Neurophysiology, Elisabeth-Tweesteden Hospital, Tilburg, The Netherlands.
| |
Collapse
|
7
|
Zhang X, Zhang F. Peripheral Neuropathy in Diabetes: What Can MRI Do? Diabetes 2023; 72:1060-1069. [PMID: 37471598 DOI: 10.2337/db22-0912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 04/24/2023] [Indexed: 07/22/2023]
Abstract
Diabetes peripheral neuropathy (DPN) is commonly asymptomatic in the early stage. However, once symptoms and obvious defects appear, recovery is not possible. Diagnosis of neuropathy is based on physical examinations, questionnaires, nerve conduction studies, skin biopsies, and so on. However, the diagnosis of DPN is still challenging, and early diagnosis and immediate intervention are very important for prevention of the development and progression of diabetic neuropathy. The advantages of MRI in the diagnosis of DPN are obvious: the peripheral nerve imaging is clear, the lesions can be found intuitively, and the quantitative evaluation of the lesions is the basis for the diagnosis, classification, and follow-up of DPN. With the development of magnetic resonance technology, more and more studies have been conducted on detection of DPN. This article reviews the research field of MRI in DPN.
Collapse
Affiliation(s)
- Xianchen Zhang
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Shandong, China
| | - Fulong Zhang
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Shandong, China
| |
Collapse
|
8
|
Foesleitner O, Knop KC, Lindenau M, Preisner F, Bäumer P, Heiland S, Bendszus M, Kronlage M. Quantitative MR Neurography in Multifocal Motor Neuropathy and Amyotrophic Lateral Sclerosis. Diagnostics (Basel) 2023; 13:diagnostics13071237. [PMID: 37046455 PMCID: PMC10093201 DOI: 10.3390/diagnostics13071237] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 03/19/2023] [Indexed: 03/29/2023] Open
Abstract
Background: The aim of this study was to assess the phenotype of multifocal motor neuropathy (MMN) and amyotrophic lateral sclerosis (ALS) in quantitative MR neurography. Methods: In this prospective study, 22 patients with ALS, 8 patients with MMN, and 10 healthy volunteers were examined with 3T MR neurography, using a high-resolution fat-saturated T2-weighted sequence, diffusion-tensor imaging (DTI), and a multi-echo T2-relaxometry sequence. The quantitative biomarkers fractional anisotropy (FA), radial and axial diffusivity (RD, AD), mean diffusivity (MD), cross-sectional area (CSA), T2-relaxation time, and proton spin density (PSD) were measured in the tibial nerve at the thigh and calf, and in the median, radial, and ulnar nerves at the mid-upper arm. Results: MMN showed a characteristic imaging pattern of decreased FA (p = 0.018), increased RD (p = 0.014), increased CSA (p < 0.001), increased T2-relaxation time (p < 0.001), and increased PSD (p = 0.025) in the upper arm nerves compared to ALS and controls. ALS patients did not differ from controls in any imaging marker, nor were there any group differences in the tibial nerve (p > 0.05). Conclusions: MMN shows a characteristic pattern of quantitative DTI and T2-relaxometry parameters in the upper-arm nerves, primarily indicating demyelination. Peripheral nerve changes in ALS seem to be below the detection level of current state-of-the-art quantitative MR neurography.
Collapse
|
9
|
Foesleitner O, Jäger LB, Schwarz D, Hayes J, Sam G, Wildemann B, Wick W, Bendszus M, Heiland S. Peripheral Nerve Involvement at First Diagnosis of Multiple Sclerosis: A Prospective MR Neurography Study. Invest Radiol 2023; 58:173-179. [PMID: 35976760 DOI: 10.1097/rli.0000000000000915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
OBJECTIVES The aim of this study was to assess peripheral nerve involvement in patients with multiple sclerosis (MS) at first clinical presentation using quantitative magnetic resonance (MR) neurography in correlation with clinical, laboratory, electrophysiological, and central nervous MR imaging data. MATERIALS AND METHODS In this prospective monocentric study, 30 patients first diagnosed with MS according to the McDonald criteria (19 women; mean age, 32.4 ± 8.8 years) and 30 age- and sex-matched healthy volunteers were examined with high-resolution 3 T MR neurography using a dual-echo T2-relaxometry sequence covering the tibial and peroneal nerves from proximal thigh to distal calf. Magnetic resonance biomarkers of T2 relaxation time (T2 app ), proton spin density (PSD), and nerve cross-sectional area (CSA) were correlated with clinical symptoms, intrathecal immunoglobulin (Ig) synthesis, nerve conduction study, and lesion load on brain and spine MR imaging. The diagnostic accuracy of MR biomarkers was assessed using receiver-operating characteristic curves. RESULTS Diffuse nerve changes were detected along the tibial and peroneal nerves in MS patients, who showed decreased PSD ( P < 0.001), increased T2 app ( P < 0.001), and smaller tibial nerve CSA ( P < 0.001) compared with healthy subjects. Tibial PSD was identified as best parameter separating patients from controls (area under the curve = 0.876). Intrathecal IgG and IgM synthesis correlated with PSD values ( r = -0.44, P = 0.016, and r = -0.42, P = 0.022). Contrast-enhancement of brain or spine lesions was related to larger tibial and peroneal CSA ( P < 0.001, P = 0.033). Abnormal electrophysiology correlated with higher tibial and peroneal T2 app ( P < 0.001 and P = 0.033), lower tibial and peroneal PSD ( P = 0.018 and P = 0.002), and smaller peroneal CSA ( P < 0.001). CONCLUSIONS Quantitative MR neurography reveals peripheral nerve changes in patients with initial diagnosis of MS. Correlation of imaging findings with intrathecal immunoglobulin synthesis may indicate a primary coaffection of the peripheral nervous system in MS.
Collapse
Affiliation(s)
| | | | | | | | - Georges Sam
- Neurology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Wolfgang Wick
- Neurology, Heidelberg University Hospital, Heidelberg, Germany
| | | | | |
Collapse
|
10
|
Dorsal Root Ganglia Volume—Normative Values, Correlation with Demographic Determinants and Reliability of Three Different Methods of Volumetry. Diagnostics (Basel) 2022; 12:diagnostics12071570. [PMID: 35885475 PMCID: PMC9323629 DOI: 10.3390/diagnostics12071570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/22/2022] [Accepted: 06/27/2022] [Indexed: 11/17/2022] Open
Abstract
Background: Dorsal root ganglia (DRG) volume assessment by MR-Neurography (MRN) has evolved to an important imaging marker in the diagnostic workup of various peripheral neuropathies and pain syndromes. The aim of this study was (1) to assess normal values of DRG volume and correlations with demographic determinants and (2) to quantify the inter-reader and inter-method reliability of three different methods of DRG volumetry. Methods: Sixty healthy subjects (mean age: 59.1, range 23–79) were examined using a 3D T2-weighted MRN of the lumbosacral plexus at 3 Tesla. Normal values of DRG L3 to S2 were obtained after exact volumetry based on manual 3D segmentation and correlations with demographic variables were assessed. For the assessment of inter-reader and inter-method reliability, DRG volumes in a subset of 25 participants were measured by two independent readers, each applying (1) exact volumetry based on 3D segmentation, (2) axis-corrected, and (3) non-axis-corrected volume estimation. Intraclass correlation coefficients were reported and the Bland–Altman analysis was conducted. Results: Mean DRG volumes ranged from 124.8 mm3 for L3 to 323.3 mm3 for S1 and did not differ between right and left DRG. DRG volume (mean of L3 to S1) correlated with body height (r = 0.42; p = 0.0008) and weight (r = 0.34; p = 0.0087). DRG of men were larger than of women (p = 0.0002); however, no difference remained after correction for body height. Inter-reader reliability was high for all three methods but best for exact volumetry (ICC = 0.99). While axis-corrected estimation was not associated with a relevant bias, non-axis-corrected estimation systematically overestimated DRG volume by on average of 15.55 mm3 (reader 1) or 18.00 mm3 (reader 2) when compared with exact volumetry. Conclusion: The here presented normal values of lumbosacral DRG volume and the correlations with height and weight may be considered in future disease specific studies and possible clinical applications. Exact volumetry was most reliable and should be considered the gold standard. However, the reliability of axis-corrected and non-axis-corrected volume estimation was also high and might still be sufficient, depending on the degree of the required measurement accuracy.
Collapse
|
11
|
Preisner F, Behnisch R, Schwehr V, Godel T, Schwarz D, Foesleitner O, Bäumer P, Heiland S, Bendszus M, Kronlage M. Quantitative MR-Neurography at 3.0T: Inter-Scanner Reproducibility. Front Neurosci 2022; 16:817316. [PMID: 35250457 PMCID: PMC8888927 DOI: 10.3389/fnins.2022.817316] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 01/25/2022] [Indexed: 11/29/2022] Open
Abstract
Background Quantitative MR-neurography (MRN) is increasingly applied, however, the impact of the MR-scanner on the derived parameters is unknown. Here, we used different 3.0T MR scanners and applied comparable MR-sequences in order to quantify the inter-scanner reproducibility of various MRN parameters of the sciatic nerve. Methods Ten healthy volunteers were prospectively examined at three different 3.0T MR scanners and underwent MRN of their sciatic nerve using comparable imaging protocols including diffusion tensor imaging (DTI) and T2 relaxometry. Subsequently, inter-scanner agreement was assessed for seven different parameters by calculating the intraclass correlation coefficients (ICCs) and the standard error of measurement (SEM). Results Assessment of inter-scanner reliability revealed good to excellent agreement for T2 (ICC: 0.846) and the quantitative DTI parameters, such as fractional anisotropy (FA) (ICC: 0.876), whereas moderate agreement was observed for proton spin density (PD) (ICC: 0.51). Analysis of variance identified significant inter-scanner differences for several parameters, such as FA (p < 0.001; p = 0.02), T2 (p < 0.01) and PD (p = 0.02; p < 0.01; p = 0.02). Calculated SEM values were mostly within the range of one standard deviation of the absolute mean values, for example 0.033 for FA, 4.12 ms for T2 and 27.8 for PD. Conclusion This study quantifies the measurement imprecision for peripheral nerve DTI and T2 relaxometry, which is associated with the use of different MR scanners. The here presented values may serve as an orientation of the possible scanner-associated fluctuations of MRN biomarkers, which can occur under similar conditions.
Collapse
Affiliation(s)
- Fabian Preisner
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Rouven Behnisch
- Institute of Medical Biometry and Informatics, Heidelberg University, Heidelberg, Germany
| | - Véronique Schwehr
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Tim Godel
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Daniel Schwarz
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Olivia Foesleitner
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | | | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Moritz Kronlage
- Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
- *Correspondence: Moritz Kronlage,
| |
Collapse
|
12
|
Evans MC, Wade C, Hohenschurz-Schmidt D, Lally P, Ugwudike A, Shah K, Bangerter N, Sharp DJ, Rice ASC. Magnetic Resonance Imaging as a Biomarker in Diabetic and HIV-Associated Peripheral Neuropathy: A Systematic Review-Based Narrative. Front Neurosci 2021; 15:727311. [PMID: 34621152 PMCID: PMC8490874 DOI: 10.3389/fnins.2021.727311] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 08/17/2021] [Indexed: 12/18/2022] Open
Abstract
Background: Peripheral neuropathy can be caused by diabetes mellitus and HIV infection, and often leaves patients with treatment-resistant neuropathic pain. To better treat this condition, we need greater understanding of the pathogenesis, as well as objective biomarkers to predict treatment response. Magnetic resonance imaging (MRI) has a firm place as a biomarker for diseases of the central nervous system (CNS), but until recently has had little role for disease of the peripheral nervous system. Objectives: To review the current state-of-the-art of peripheral nerve MRI in diabetic and HIV symmetrical polyneuropathy. We used systematic literature search methods to identify all studies currently published, using this as a basis for a narrative review to discuss major findings in the literature. We also assessed risk of bias, as well as technical aspects of MRI and statistical analysis. Methods: Protocol was pre-registered on NIHR PROSPERO database. MEDLINE, Web of Science and EMBASE databases were searched from 1946 to 15th August 2020 for all studies investigating either diabetic or HIV neuropathy and MRI, focusing exclusively on studies investigating symmetrical polyneuropathy. The NIH quality assessment tool for observational and cross-sectional cohort studies was used for risk of bias assessment. Results: The search resulted in 18 papers eligible for review, 18 for diabetic neuropathy and 0 for HIV neuropathy. Risk of bias assessment demonstrated that studies generally lacked explicit sample size justifications, and some may be underpowered. Whilst most studies made efforts to balance groups for confounding variables (age, gender, BMI, disease duration), there was lack of consistency between studies. Overall, the literature provides convincing evidence that DPN is associated with larger nerve cross sectional area, T2-weighted hyperintense and hypointense lesions, evidence of nerve oedema on Dixon imaging, decreased fractional anisotropy and increased apparent diffusion coefficient compared with controls. Analysis to date is largely restricted to the sciatic nerve or its branches. Conclusions: There is emerging evidence that various structural MR metrics may be useful as biomarkers in diabetic polyneuropathy, and areas for future direction are discussed. Expanding this technique to other forms of peripheral neuropathy, including HIV neuropathy, would be of value. Systematic Review Registration: (identifier: CRD 42020167322) https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=167322.
Collapse
Affiliation(s)
- Matthew C. Evans
- Pain Research, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
- Department of Brain Sciences, Care Research and Technology Centre, UK Dementia Research Institute, London, United Kingdom
| | - Charles Wade
- Department of Brain Sciences, Care Research and Technology Centre, UK Dementia Research Institute, London, United Kingdom
| | - David Hohenschurz-Schmidt
- Pain Research, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Pete Lally
- Department of Brain Sciences, Care Research and Technology Centre, UK Dementia Research Institute, London, United Kingdom
- Royal School of Mines, Imperial College London, London, United Kingdom
| | - Albert Ugwudike
- Royal School of Mines, Imperial College London, London, United Kingdom
| | - Kamal Shah
- Royal School of Mines, Imperial College London, London, United Kingdom
| | - Neal Bangerter
- Royal School of Mines, Imperial College London, London, United Kingdom
| | - David J. Sharp
- Department of Brain Sciences, Care Research and Technology Centre, UK Dementia Research Institute, London, United Kingdom
| | - Andrew S. C. Rice
- Pain Research, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, United Kingdom
| |
Collapse
|
13
|
Magnetization Transfer Ratio of Peripheral Nerve and Skeletal Muscle : Correlation with Demographic Variables in Healthy Volunteers. Clin Neuroradiol 2021; 32:557-564. [PMID: 34374786 PMCID: PMC9187530 DOI: 10.1007/s00062-021-01067-5] [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: 03/17/2021] [Accepted: 07/06/2021] [Indexed: 11/27/2022]
Abstract
Purpose To assess the correlation of peripheral nerve and skeletal muscle magnetization transfer ratio (MTR) with demographic variables. Methods In this study 59 healthy adults evenly distributed across 6 decades (mean age 50.5 years ±17.1, 29 women) underwent magnetization transfer imaging and high-resolution T2-weighted imaging of the sciatic nerve at 3 T. Mean sciatic nerve MTR as well as MTR of biceps femoris and vastus lateralis muscles were calculated based on manual segmentation on six representative slices. Correlations of MTR with age, body height, body weight, and body mass index (BMI) were expressed by Pearson coefficients. Best predictors for nerve and muscle MTR were determined using a multiple linear regression model with forward variable selection and fivefold cross-validation. Results Sciatic nerve MTR showed significant negative correlations with age (r = −0.47, p < 0.001), BMI (r = −0.44, p < 0.001), and body weight (r = −0.36, p = 0.006) but not with body height (p = 0.55). The multiple linear regression model determined age and BMI as best predictors for nerve MTR (R2 = 0.40). The MTR values were different between nerve and muscle tissue (p < 0.0001), but similar between muscles. Muscle MTR was associated with BMI (r = −0.46, p < 0.001 and r = −0.40, p = 0.002) and body weight (r = −0.36, p = 0.005 and r = −0.28, p = 0.035). The BMI was selected as best predictor for mean muscle MTR in the multiple linear regression model (R2 = 0.26). Conclusion Peripheral nerve MTR decreases with higher age and BMI. Studies that assess peripheral nerve MTR should consider age and BMI effects. Skeletal muscle MTR is primarily associated with BMI but overall less dependent on demographic variables. Supplementary Information The online version of this article (10.1007/s00062-021-01067-5) contains supplementary material, which is available to authorized users.
Collapse
|
14
|
Preisner F, Behnisch R, Foesleitner O, Schwarz D, Wehrstein M, Meredig H, Friedmann-Bette B, Heiland S, Bendszus M, Kronlage M. Reliability and reproducibility of sciatic nerve magnetization transfer imaging and T2 relaxometry. Eur Radiol 2021; 31:9120-9130. [PMID: 34104997 PMCID: PMC8589742 DOI: 10.1007/s00330-021-08072-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 04/08/2021] [Accepted: 05/11/2021] [Indexed: 12/19/2022]
Abstract
Objectives To assess the interreader and test-retest reliability of magnetization transfer imaging (MTI) and T2 relaxometry in sciatic nerve MR neurography (MRN). Materials and methods In this prospective study, 21 healthy volunteers were examined three times on separate days by a standardized MRN protocol at 3 Tesla, consisting of an MTI sequence, a multi-echo T2 relaxometry sequence, and a high-resolution T2-weighted sequence. Magnetization transfer ratio (MTR), T2 relaxation time, and proton spin density (PSD) of the sciatic nerve were assessed by two independent observers, and both interreader and test-retest reliability for all readout parameters were reported by intraclass correlation coefficients (ICCs) and standard error of measurement (SEM). Results For the sciatic nerve, overall mean ± standard deviation MTR was 26.75 ± 3.5%, T2 was 64.54 ± 8.2 ms, and PSD was 340.93 ± 78.8. ICCs ranged between 0.81 (MTR) and 0.94 (PSD) for interreader reliability and between 0.75 (MTR) and 0.94 (PSD) for test-retest reliability. SEM for interreader reliability was 1.7% for MTR, 2.67 ms for T2, and 21.3 for PSD. SEM for test-retest reliability was 1.7% for MTR, 2.66 ms for T2, and 20.1 for PSD. Conclusions MTI and T2 relaxometry of the sciatic nerve are reliable and reproducible. The values of measurement imprecision reported here may serve as a guide for correct interpretation of quantitative MRN biomarkers in future studies. Key Points • Magnetization transfer imaging (MTI) and T2 relaxometry of the sciatic nerve are reliable and reproducible. • The imprecision that is unavoidably associated with different scans or different readers can be estimated by the here presented SEM values for the biomarkers T2, PSD, and MTR. • These values may serve as a guide for correct interpretation of quantitative MRN biomarkers in future studies and possible clinical applications. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-08072-9.
Collapse
Affiliation(s)
- Fabian Preisner
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Rouven Behnisch
- Institute of Medical Biometry and Informatics, University of Heidelberg, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany
| | - Olivia Foesleitner
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Daniel Schwarz
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Michaela Wehrstein
- Department of Sports Medicine (Internal Medicine VII), Medical Clinic, Heidelberg University Hospital, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany
| | - Hagen Meredig
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Birgit Friedmann-Bette
- Department of Sports Medicine (Internal Medicine VII), Medical Clinic, Heidelberg University Hospital, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany
| | - Sabine Heiland
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Martin Bendszus
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany
| | - Moritz Kronlage
- Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany.
| |
Collapse
|
15
|
Advances in imaging technologies for the assessment of peripheral neuropathies in rheumatoid arthritis. Rheumatol Int 2021; 41:519-528. [PMID: 33427917 DOI: 10.1007/s00296-020-04780-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 12/26/2020] [Indexed: 12/22/2022]
Abstract
Peripheral neuropathy in patients with rheumatoid arthritis is associated with a maladaptive autoimmune response that may cause chronic pain and disability. Nerve conduction studies are the routine method performed when rheumatologists presume its presence. However, this approach is invasive, may not reveal subtle malfunctions in the early stages of the disease, and does not expose abnormalities in structures surrounding the nerves and muscles, limiting the possibility of a timely diagnosis. This work aims to present a narrative review of new technologies for the clinical assessment of peripheral neuropathy in Rheumatoid Arthritis. Through a bibliographic search carried out in five repositories, from 1990 to 2020, we identified three technologies that could detect peripheral nerve lesions and perform quantitative evaluations: (1) magnetic resonance neurography, (2) functional magnetic resonance imaging, and (3) high-resolution ultrasonography of peripheral nerves. We found these tools can overcome the main constraints imposed by the previous electrophysiologic methods, enabling early diagnosis.
Collapse
|
16
|
Halm S, Fairhurst PG, Tschanz S, Wieland FAM, Djonov V, Krause F. Effect of Lateral Sliding Calcaneus Osteotomy on Tarsal Tunnel Pressure. FOOT & ANKLE ORTHOPAEDICS 2020; 5:2473011420931015. [PMID: 35097388 PMCID: PMC8697189 DOI: 10.1177/2473011420931015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Background: Lateral sliding calcaneus osteotomies are common procedures to correct hindfoot varus deformities. Shifting the calcaneal tuberosity laterally (lateralization) can lead to tarsal tunnel pressure increase and tibial nerve palsy. The purpose of this cadaveric biomechanical study was to investigate the correlation of lateralization and pressure increase underneath the flexor retinaculum. Methods: The pressure in the tarsal tunnel of 12 Thiel-fixated human cadaveric lower legs was measured in different foot positions and varying degrees of calcaneal lateralization. Results: The mean pressure increased from plantarflexion (PF) to neutral position (NP) and from NP to hindfoot dorsiflexion (DF), and with increasing amounts of lateralization of the calcaneal tuberosity. The mean baseline pressure in PF was 1.5, in NP 2.2, and in DF 6.5 mmHg and increased to 8.1 in PF, 18.4 in NP, and 33.1 mmHg with 12 mm of lateralization. The release of the flexor retinaculum significantly lowered the pressure. Conclusion: Increasing pressures were found in the tarsal tunnel with increasing lateralization of the tuberosity and with both dorsiflexion and plantarflexion of the ankle. Clinical Relevance: A pre-emptive release of the flexor retinaculum for a lateralization of the calcaneal tuberosity of more than 8 mm should be considered, especially if specific patient risk factors are present. No tibial nerve palsy should be expected with 4 mm of lateralization.
Collapse
Affiliation(s)
- Sebastian Halm
- Institute of Anatomy, University of Bern, Bern, Switzerland
| | - Paul G. Fairhurst
- Department of Orthopaedic Surgery and Traumatology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Stefan Tschanz
- Institute of Anatomy, University of Bern, Bern, Switzerland
| | | | | | - Fabian Krause
- Department of Orthopaedic Surgery and Traumatology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| |
Collapse
|
17
|
Sollmann N, Weidlich D, Klupp E, Cervantes B, Ganter C, Zimmer C, Rummeny EJ, Baum T, Kirschke JS, Karampinos DC. T2 mapping of the distal sciatic nerve in healthy subjects and patients suffering from lumbar disc herniation with nerve compression. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2020; 33:713-724. [PMID: 32048099 PMCID: PMC7502059 DOI: 10.1007/s10334-020-00832-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 12/22/2019] [Accepted: 01/28/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVE To measure T2 values for magnetic resonance neurography (MRN) of the healthy distal sciatic nerve and compare those to T2 changes in patients with nerve compression. MATERIALS AND METHODS Twenty-one healthy subjects and five patients with sciatica due to disc herniation underwent MRN using a T2-prepared turbo spin echo (TSE) sequence of the distal sciatic nerve bilaterally. Six and one of those healthy subjects further underwent a commonly used multi-echo spin-echo (MESE) sequence and magnetic resonance spectroscopy (MRS), respectively. RESULTS T2 values derived from the T2-prepared TSE sequence were 44.6 ± 3.0 ms (left) and 44.5 ± 2.6 ms (right) in healthy subjects and showed good inter-reader reliability. In patients, T2 values of 61.5 ± 6.2 ms (affected side) versus 43.3 ± 2.4 ms (unaffected side) were obtained. T2 values of MRS were in good agreement with measurements from the T2-prepared TSE, but not with those of the MESE sequence. DISCUSSION A T2-prepared TSE sequence enables precise determination of T2 values of the distal sciatic nerve in agreement with MRS. A MESE sequence tends to overestimate nerve T2 compared to T2 from MRS due to the influence of residual fat on T2 quantification. Our approach may enable to quantitatively assess direct nerve affection related to nerve compression.
Collapse
Affiliation(s)
- Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany. .,TUM-Neuroimaging Center, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.
| | - Dominik Weidlich
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Elisabeth Klupp
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Barbara Cervantes
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Carl Ganter
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.,TUM-Neuroimaging Center, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Ernst J Rummeny
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Jan S Kirschke
- Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.,TUM-Neuroimaging Center, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Dimitrios C Karampinos
- Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| |
Collapse
|
18
|
MR Neurography: Normative Values in Correlation to Demographic Determinants in Children and Adolescents. Clin Neuroradiol 2019; 30:671-677. [DOI: 10.1007/s00062-019-00834-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2019] [Accepted: 08/19/2019] [Indexed: 01/30/2023]
|
19
|
Kronlage M, Knop KC, Schwarz D, Godel T, Heiland S, Bendszus M, Bäumer P. Amyotrophic Lateral Sclerosis versus Multifocal Motor Neuropathy: Utility of MR Neurography. Radiology 2019; 292:149-156. [PMID: 31063079 DOI: 10.1148/radiol.2019182538] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background Differential diagnosis between amyotrophic lateral sclerosis (ALS) and multifocal motor neuropathy (MMN) relies on clinical examination and electrophysiological criteria. Peripheral nerve imaging might assist this differential diagnosis. Purpose To assess diagnostic accuracy of MR neurography in the differential diagnosis of ALS and MMN. Materials and Methods This prospective study was conducted between December 2015 and April 2017. Study participants with ALS or MMN underwent MR neurography of the lumbosacral plexus, midthigh, proximal calf, and midupper arm of the clinically more affected side using high-resolution T2-weighted sequences. Matched healthy study participants who underwent MR neurography served as a control group. Two blinded readers independently rated fascicular lesions and muscle denervation signs on a five-point scale and made an image-only diagnosis, which was compared with the clinical diagnosis to assess diagnostic accuracy (reported for ALS vs non-ALS and MMN vs non-MMN). The Kruskal-Wallis test was used to compare readers' scoring results. Results Twenty-two participants with ALS (12 men and 10 women; mean age ± standard deviation, 62.3 years ± 9.0), eight participants with MMN (seven men and one woman; mean age, 57.6 years ± 18.6), and 15 healthy participants (seven men and eight women; mean age, 59.1 years ± 10.9) were enrolled in this study. Nerves of participants with ALS either appeared normal or showed T2-weighted hyperintensities without fascicular enlargement (reader 1, 22 of 22 participants; reader 2, 21 of 22 participants). In contrast, nerves in MMN were characterized by fascicular swellings (reader 1, six of eight participants; reader 2, seven of eight participants). Muscle denervation signs were more prominent in ALS than in MMN. Inter-rater reliability for blinded diagnosis was κ of 0.82. By consensus, the sensitivity to diagnose ALS (vs MMN and healthy control participants) was 19 of 22 (86% [95% confidence interval {CI}: 67%, 95%]). The corresponding specificity was 23 of 23 (100% [95% CI: 86%, 100%]). The sensitivity to diagnose MMN (vs ALS and healthy control participants) was seven of eight (88% [95% CI: 53%, 99%]). The corresponding specificity was 37 of 37 (100% [95% CI: 91%, 100%]). Conclusion MR neurography is an accurate method for assisting in the differential diagnosis of amyotrophic lateral sclerosis and multifocal motor neuropathy. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Andreisek in this issue.
Collapse
Affiliation(s)
- Moritz Kronlage
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (M.K., D.S., T.G., S.H., M.B., P.B.); Neurologie Neuer Wall, Hamburg, Germany (K.C.K.); Department of Radiology, German Cancer Research Center, Heidelberg, Germany (P.B.); and dia.log, Altoetting Center for Radiology, Altoetting, Germany (P.B.)
| | - Karl Christian Knop
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (M.K., D.S., T.G., S.H., M.B., P.B.); Neurologie Neuer Wall, Hamburg, Germany (K.C.K.); Department of Radiology, German Cancer Research Center, Heidelberg, Germany (P.B.); and dia.log, Altoetting Center for Radiology, Altoetting, Germany (P.B.)
| | - Daniel Schwarz
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (M.K., D.S., T.G., S.H., M.B., P.B.); Neurologie Neuer Wall, Hamburg, Germany (K.C.K.); Department of Radiology, German Cancer Research Center, Heidelberg, Germany (P.B.); and dia.log, Altoetting Center for Radiology, Altoetting, Germany (P.B.)
| | - Tim Godel
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (M.K., D.S., T.G., S.H., M.B., P.B.); Neurologie Neuer Wall, Hamburg, Germany (K.C.K.); Department of Radiology, German Cancer Research Center, Heidelberg, Germany (P.B.); and dia.log, Altoetting Center for Radiology, Altoetting, Germany (P.B.)
| | - Sabine Heiland
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (M.K., D.S., T.G., S.H., M.B., P.B.); Neurologie Neuer Wall, Hamburg, Germany (K.C.K.); Department of Radiology, German Cancer Research Center, Heidelberg, Germany (P.B.); and dia.log, Altoetting Center for Radiology, Altoetting, Germany (P.B.)
| | - Martin Bendszus
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (M.K., D.S., T.G., S.H., M.B., P.B.); Neurologie Neuer Wall, Hamburg, Germany (K.C.K.); Department of Radiology, German Cancer Research Center, Heidelberg, Germany (P.B.); and dia.log, Altoetting Center for Radiology, Altoetting, Germany (P.B.)
| | - Philipp Bäumer
- From the Department of Neuroradiology, Heidelberg University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany (M.K., D.S., T.G., S.H., M.B., P.B.); Neurologie Neuer Wall, Hamburg, Germany (K.C.K.); Department of Radiology, German Cancer Research Center, Heidelberg, Germany (P.B.); and dia.log, Altoetting Center for Radiology, Altoetting, Germany (P.B.)
| |
Collapse
|
20
|
Prevalence of fascicular hyperintensities in peripheral nerves of healthy individuals with regard to cerebral white matter lesions. Eur Radiol 2019; 29:3480-3487. [DOI: 10.1007/s00330-019-06145-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Revised: 02/21/2019] [Accepted: 03/08/2019] [Indexed: 12/12/2022]
|
21
|
Balsiger F, Steindel C, Arn M, Wagner B, Grunder L, El-Koussy M, Valenzuela W, Reyes M, Scheidegger O. Segmentation of Peripheral Nerves From Magnetic Resonance Neurography: A Fully-Automatic, Deep Learning-Based Approach. Front Neurol 2018; 9:777. [PMID: 30283397 PMCID: PMC6156270 DOI: 10.3389/fneur.2018.00777] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2018] [Accepted: 08/27/2018] [Indexed: 01/05/2023] Open
Abstract
Diagnosis of peripheral neuropathies relies on neurological examinations, electrodiagnostic studies, and since recently magnetic resonance neurography (MRN). The aim of this study was to develop and evaluate a fully-automatic segmentation method of peripheral nerves of the thigh. T2-weighted sequences without fat suppression acquired on a 3 T MR scanner were retrospectively analyzed in 10 healthy volunteers and 42 patients suffering from clinically and electrophysiologically diagnosed sciatic neuropathy. A fully-convolutional neural network was developed to segment the MRN images into peripheral nerve and background tissues. The performance of the method was compared to manual inter-rater segmentation variability. The proposed method yielded Dice coefficients of 0.859 ± 0.061 and 0.719 ± 0.128, Hausdorff distances of 13.9 ± 26.6 and 12.4 ± 12.1 mm, and volumetric similarities of 0.930 ± 0.054 and 0.897 ± 0.109, for the healthy volunteer and patient cohorts, respectively. The complete segmentation process requires less than one second, which is a significant decrease to manual segmentation with an average duration of 19 ± 8 min. Considering cross-sectional area or signal intensity of the segmented nerves, focal and extended lesions might be detected. Such analyses could be used as biomarker for lesion burden, or serve as volume of interest for further quantitative MRN techniques. We demonstrated that fully-automatic segmentation of healthy and neuropathic sciatic nerves can be performed from standard MRN images with good accuracy and in a clinically feasible time.
Collapse
Affiliation(s)
- Fabian Balsiger
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
| | - Carolin Steindel
- Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Mirjam Arn
- Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Benedikt Wagner
- Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Lorenz Grunder
- Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Marwan El-Koussy
- Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Waldo Valenzuela
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland.,Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Mauricio Reyes
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
| | - Olivier Scheidegger
- Support Center for Advanced Neuroimaging, Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.,Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| |
Collapse
|