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Vavasour I, Elliott C, Arnold DL, Gaetano L, Clayton D, Magon S, Bonati U, Bernasconi C, Traboulsee A, Kolind S. Presence of slowly expanding lesions in multiple sclerosis predicts progressive demyelination within lesions and normal-appearing tissue over time. Mult Scler 2025; 31:418-432. [PMID: 39950257 PMCID: PMC11956371 DOI: 10.1177/13524585251316519] [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: 06/26/2024] [Revised: 01/08/2025] [Accepted: 01/13/2025] [Indexed: 04/02/2025]
Abstract
BACKGROUND Multiple sclerosis (MS) slowly expanding lesions (SELs) are defined on magnetic resonance imaging (MRI) as contiguous regions of pre-existing focal non-contrast-enhancing T2 lesions with constant and concentric local expansion on conventional T1-weighted and T2-weighted images. SELs are associated with an increased risk of disability progression. METHODS Myelin-related changes detected using myelin water fraction (MWF) and magnetisation transfer ratio (MTR) in SELs and T2 lesions were measured over 192 weeks in participants with relapsing MS. RESULTS In participants with SELs (SEL+), SELs (MWF: 0.12 ± 0.03, MTR: 33.1 ± 3.6 pu) showed reduced myelin measures at baseline compared to T2 lesions (MWF: 0.13 ± 0.02, MTR: 35.1 ± 2.4 pu). In participants without SELs (SEL-), T2 lesions had higher myelin measures (MWF: 0.15 ± 0.02, MTR: 36.2 ± 2.0 pu) compared to T2 lesions in SEL+. Over 4 years, only SELs showed decreases in MWF (-11.4%). The percentage of abnormal voxels within normal-appearing white matter was higher in SEL+ and increased over time (SEL+ MWF Week 0: 0.56%, Week 192: 0.98%; SEL- MWF Week 0: 0.13%, Week 192: 0.25%). CONCLUSION Our results indicate progressive focal and global demyelination in SEL+ participants and that the presence of SELs might be a biomarker for participants with ongoing diffuse or smouldering inflammation within the whole brain.
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Affiliation(s)
- Irene Vavasour
- The University of British Columbia, Vancouver, BC, Canada
| | | | - Douglas L Arnold
- NeuroRx Research, Montreal, QC, Canada; McGill University, Montreal, QC, Canada
| | | | | | | | | | | | | | - Shannon Kolind
- The University of British Columbia, Vancouver, BC, Canada
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Jaime Garcia D, Clancy U, Arteaga C, Valdés-Hernandez MC, Chappell FM, Jochems ACC, Cheng Y, Zhang J, Thrippleton MJ, Stringer MS, Sleight E, Backhouse EV, Wiseman S, Brown R, Doubal FN, Montagne A, Wardlaw JM. Blood biomarkers of vascular dysfunction in small vessel disease progression: Insights from a longitudinal neuroimaging study. Alzheimers Dement 2025; 21:e70152. [PMID: 40275856 DOI: 10.1002/alz.70152] [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: 10/11/2024] [Revised: 02/26/2025] [Accepted: 02/28/2025] [Indexed: 04/26/2025]
Abstract
INTRODUCTION This study explored the relationship between blood biomarkers of cerebrovascular function and small vessel disease (SVD) neuroimaging markers and cognitive outcomes in highly-phenotyped participants. METHODS We conducted cross-sectional and 1-year longitudinal analyses on 181 patients with mild ischemic stroke, enriched for SVD features. We examined relationships between a panel of 13 blood biomarkers and magnetic resonance imaging (MRI) markers of SVD (structural lesions, diffusion-weighted imaging [DWI]-positive lesions, blood-brain barrier (BBB) permeability, and cerebrovascular reactivity (CVR), and cognition. RESULTS In linear mixed models, vascular endothelial growth factor was significantly associated with incident DWI-positive lesions over 1 year. Intercellular adhesion molecule-1 was linked with lower CVR while platelet-derived growth factor-subunit B and Endothelin-1 were associated with higher CVR. Platelet-Selectin levels were associated with mild cognitive impairment at 1 year. DISCUSSION Our results support the role of endothelial and pericyte dysfunction in SVD burden and progression and suggest that specific biomarkers relate to distinct SVD manifestations. HIGHLIGHTS Small vessel disease (SVD) lacks specific or predictive biomarker signatures. Vascular endothelial growth factor levels were linked to incident lesions detected over 1 year. Circulating intercellular adhesion molecule-1 related to lower cerebrovascular reactivity. Platelet-selectin levels were associated with mild cognitive impairment longitudinally. These findings could help stratify patients at high-risk of rapid-progression SVD.
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Affiliation(s)
- Daniela Jaime Garcia
- Centre for Clinical Brain Sciences, School of Clinical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Una Clancy
- Centre for Clinical Brain Sciences, School of Clinical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK
| | - Carmen Arteaga
- Centre for Clinical Brain Sciences, School of Clinical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK
| | - Maria C Valdés-Hernandez
- Centre for Clinical Brain Sciences, School of Clinical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Francesca M Chappell
- Centre for Clinical Brain Sciences, School of Clinical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK
| | - Angela C C Jochems
- Centre for Clinical Brain Sciences, School of Clinical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Yajun Cheng
- Centre for Clinical Brain Sciences, School of Clinical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Junfang Zhang
- Centre for Clinical Brain Sciences, School of Clinical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK
- Department of Neurology & Institute of Neurology, Ruijin Hospital affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, School of Clinical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging Facility (Royal Infirmary of Edinburgh), University of Edinburgh, Edinburgh, UK
| | - Michael S Stringer
- Centre for Clinical Brain Sciences, School of Clinical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK
- Edinburgh Imaging Facility (Royal Infirmary of Edinburgh), University of Edinburgh, Edinburgh, UK
| | - Emilie Sleight
- Centre for Clinical Brain Sciences, School of Clinical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Ellen V Backhouse
- Centre for Clinical Brain Sciences, School of Clinical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Stewart Wiseman
- Centre for Clinical Brain Sciences, School of Clinical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Rosalind Brown
- Centre for Clinical Brain Sciences, School of Clinical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Fergus N Doubal
- Centre for Clinical Brain Sciences, School of Clinical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK
| | - Axel Montagne
- Centre for Clinical Brain Sciences, School of Clinical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences, School of Clinical Sciences, College of Medicine and Veterinary Medicine, The University of Edinburgh, Edinburgh, UK
- UK Dementia Research Institute at the University of Edinburgh, Edinburgh, UK
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Simegn GL, Song Y, Murali-Manohar S, Zöllner HJ, Davies-Jenkins CW, Simicic D, Hupfeld KE, Gudmundson AT, Muska E, Carter E, Hui SCN, Yedavalli V, Oeltzschner G, Dean DC, Ceritoglu C, Ratnanather JT, Porges E, Edden R. A Water Relaxation Atlas for Age- and Region-Specific Metabolite Concentration Correction at 3 T. NMR IN BIOMEDICINE 2025; 38:e5300. [PMID: 39662516 DOI: 10.1002/nbm.5300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2024] [Revised: 10/25/2024] [Accepted: 11/14/2024] [Indexed: 12/13/2024]
Abstract
Metabolite concentration estimates from magnetic resonance spectroscopy (MRS) are typically quantified using water referencing, correcting for relaxation-time differences between metabolites and water. One common approach is to correct the reference signal for differential relaxation within three tissue compartments (gray matter, white matter, and cerebrospinal fluid) using fixed literature values. However, water relaxation times (T1 and T2) vary between brain locations and with age. MRS studies, even those measuring metabolite levels across the lifespan, often ignore these effects, because of a lack of reference data. The purpose of this study is to develop a water relaxometry atlas and to integrate location- and age-appropriate relaxation values into the MRS analysis workflow. One hundred one volunteers (51 men, 50 women; ~10 male, and 10 female participants per decade from the 20s to 60s) were recruited. T1-weighted MPRAGE images ((1-mm)3 isotropic resolution) were acquired. Whole-brain water T1 and T2 measurements were made with DESPOT ((1.4 mm)3 isotropic resolution) at 3T. T1 and T2 maps were registered to the JHU MNI-SS/EVE atlas using affine and LDDMM transformation. The atlas's 268 parcels were reduced to 130 by combining homologous parcels. Mean T1 and T2 values were calculated for each parcel in each subject. Linear models of T1 and T2 as functions of age were computed, using age - 30 as the predictor. Reference atlases of "age-30-intercept" and age-slope for T1 and T2 were generated. The atlas-based workflow was integrated into Osprey, which co-registers MRS voxels to the atlas and calculates location- and age-appropriate water relaxation parameters for quantification. The water relaxation aging atlas revealed significant regional and tissue differences in water relaxation behavior across adulthood. Using location- and subject-appropriate reference values in the MRS analysis workflow removes a current methodological limitation and is expected to reduce quantification biases associated with water-referenced tissue correction, especially for studies of aging.
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Affiliation(s)
- Gizeaddis Lamesgin Simegn
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Yulu Song
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Saipavitra Murali-Manohar
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Helge J Zöllner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Christopher W Davies-Jenkins
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Dunja Simicic
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Kathleen E Hupfeld
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Aaron T Gudmundson
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Emlyn Muska
- Center for Cognitive Aging and Memory (CAM), McKnight Brain Institute, Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida, USA
| | - Emily Carter
- Center for Cognitive Aging and Memory (CAM), McKnight Brain Institute, Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida, USA
| | - Steve C N Hui
- Developing Brain Institute, Children's National Hospital, Washington, DC, USA
- Department of Radiology, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
- Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Vivek Yedavalli
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Georg Oeltzschner
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
| | - Douglas C Dean
- Waisman Center, University of WI-Madison, Madison, Wisconsin, USA
- Department of Pediatrics, Division of Neonatology and Newborn Nursey, University of WI-Madison, School of Medicine and Public Health, Madison, Wisconsin, USA
- Department of Medical Physics, University of WI-Madison, School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Can Ceritoglu
- Center for Imaging Science and Institute for Computational Medicine, Department of Biomedical Engineering, JHU, Baltimore, Maryland, USA
| | - J Tilak Ratnanather
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Developing Brain Institute, Children's National Hospital, Washington, DC, USA
| | - Eric Porges
- Center for Cognitive Aging and Memory (CAM), McKnight Brain Institute, Department of Clinical and Health Psychology, University of Florida, Gainesville, Florida, USA
| | - Richard Edden
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
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Voorter PHM, Stringer MS, van Dinther M, Kerkhofs D, Dewenter A, Blair GW, Thrippleton MJ, Jaime Garcia D, Chappell FM, Janssen E, Kopczak A, Staals J, Ingrisch M, Duering M, Doubal FN, Dichgans M, van Oostenbrugge RJ, Jansen JFA, Wardlaw JM, Backes WH. Heterogeneity and Penumbra of White Matter Hyperintensities in Small Vessel Diseases Determined by Quantitative MRI. Stroke 2025; 56:128-137. [PMID: 39648904 DOI: 10.1161/strokeaha.124.047910] [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: 05/22/2024] [Revised: 10/11/2024] [Accepted: 11/04/2024] [Indexed: 12/10/2024]
Abstract
BACKGROUND White matter hyperintensities (WMHs) are established structural imaging markers of cerebral small vessel disease. The pathophysiologic condition of brain tissue varies over the core, the vicinity, and the subtypes of WMH and cannot be interpreted from conventional magnetic resonance imaging. We aim to improve our pathophysiologic understanding of WMHs and the adjacently injured normal-appearing white matter in terms of microstructural and microvascular alterations using quantitative magnetic resonance imaging in patients with sporadic and genetic cerebral small vessel disease. METHODS Structural T2-weighted imaging, multishell diffusion imaging, and dynamic contrast-enhanced magnetic resonance imaging were performed at 3T in 44 participants with sporadic cerebral small vessel disease and 32 participants with monogenic cerebral small vessel disease (cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy; 59±12 years, 41 males) between June 2017 and May 2020 as part of the prospective, multicenter (Edinburgh, the United Kingdom; Maastricht, the Netherlands; and Munich, Germany), observational INVESTIGATE-SVDs study (Imaging Neurovascular, Endothelial and Structural Integrity in Preparation to Treat Small Vessel Diseases). The mean diffusivity, free water content, and perfusion (all derived from multishell diffusion imaging), as well as the blood-brain barrier leakage and plasma volume fraction (derived from dynamic contrast-enhanced magnetic resonance imaging), were compared between deep and periventricular WMH types using paired t tests. Additional spatial analyses were performed inside and outside the WMH types to determine the internal heterogeneity and the extent of the penumbras, that is, adjacent white matter at risk for conversion to WMH. RESULTS Periventricular WMH had higher mean diffusivity, higher free water content, and more plasma volume compared with deep WMH (P<0.001, P=0.01, and P<0.001, respectively). No differences were observed in perfusion (P=0.94) and blood-brain barrier leakage (P=0.65) between periventricular and deep WMHs. The spatial analyses inside WMH and the adjacent white matter revealed a gradual gradient in white matter microstructure, free water content, perfusion, and plasma volume but not in blood-brain barrier leakage. CONCLUSIONS We showed different pathophysiological heterogeneity of the 2 WMH types. Periventricular WMHs display more severe damage and fluid accumulation compared with deep WMH, whereas deep WMHs reflect stronger hypoperfusion in the lesion's core. REGISTRATION URL: https://www.isrctn.com; Unique identifier: ISRCTN10514229.
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Affiliation(s)
- Paulien H M Voorter
- Department of Radiology and Nuclear Medicine (P.H.M.V., J.F.A.J., W.H.B.), Maastricht University Medical Center, the Netherlands
- Mental Health and Neuroscience Research Institute (P.H.M.V., R.J.v.O., J.F.A.J., W.H.B.), Maastricht University, the Netherlands
| | - Michael S Stringer
- Centre for Clinical Brain Sciences, UK Dementia Research Institute at The University of Edinburgh, United Kingdom (M.S.S., G.W.B., M.J.T., D.J.G., F.M.C., F.N.D., J.M.W.)
| | - Maud van Dinther
- Department of Neurology (M.v.D, D.K., J.S., R.J.v.O.), Maastricht University Medical Center, the Netherlands
- Cardiovascular Diseases Research Institute (M.v.D., D.K., J.S., R.J.v.O., W.H.B.), Maastricht University, the Netherlands
| | - Daniëlle Kerkhofs
- Department of Neurology (M.v.D, D.K., J.S., R.J.v.O.), Maastricht University Medical Center, the Netherlands
- Cardiovascular Diseases Research Institute (M.v.D., D.K., J.S., R.J.v.O., W.H.B.), Maastricht University, the Netherlands
| | - Anna Dewenter
- Institute for Stroke and Dementia Research (A.D., A.K., M. Duering, M. Dichgans), Ludwig-Maximilians-University, Munich, Germany
| | - Gordon W Blair
- Centre for Clinical Brain Sciences, UK Dementia Research Institute at The University of Edinburgh, United Kingdom (M.S.S., G.W.B., M.J.T., D.J.G., F.M.C., F.N.D., J.M.W.)
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, UK Dementia Research Institute at The University of Edinburgh, United Kingdom (M.S.S., G.W.B., M.J.T., D.J.G., F.M.C., F.N.D., J.M.W.)
| | - Daniela Jaime Garcia
- Centre for Clinical Brain Sciences, UK Dementia Research Institute at The University of Edinburgh, United Kingdom (M.S.S., G.W.B., M.J.T., D.J.G., F.M.C., F.N.D., J.M.W.)
| | - Francesca M Chappell
- Centre for Clinical Brain Sciences, UK Dementia Research Institute at The University of Edinburgh, United Kingdom (M.S.S., G.W.B., M.J.T., D.J.G., F.M.C., F.N.D., J.M.W.)
| | - Esther Janssen
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands (E.J.)
| | - Anna Kopczak
- Institute for Stroke and Dementia Research (A.D., A.K., M. Duering, M. Dichgans), Ludwig-Maximilians-University, Munich, Germany
| | - Julie Staals
- Department of Radiology and Nuclear Medicine (P.H.M.V., J.F.A.J., W.H.B.), Maastricht University Medical Center, the Netherlands
- Department of Neurology (M.v.D, D.K., J.S., R.J.v.O.), Maastricht University Medical Center, the Netherlands
- Cardiovascular Diseases Research Institute (M.v.D., D.K., J.S., R.J.v.O., W.H.B.), Maastricht University, the Netherlands
| | - Michael Ingrisch
- Department of Radiology (M.I.), Ludwig-Maximilians-University, Munich, Germany
| | - Marco Duering
- Institute for Stroke and Dementia Research (A.D., A.K., M. Duering, M. Dichgans), Ludwig-Maximilians-University, Munich, Germany
- Department of Biomedical Engineering, Faculty of Medicine, Medical Image Analysis Center and Translational Imaging in Neurology, University Hospital Basel and University of Basel, Switzerland (M. Duering)
| | - Fergus N Doubal
- Centre for Clinical Brain Sciences, UK Dementia Research Institute at The University of Edinburgh, United Kingdom (M.S.S., G.W.B., M.J.T., D.J.G., F.M.C., F.N.D., J.M.W.)
| | - Martin Dichgans
- Institute for Stroke and Dementia Research (A.D., A.K., M. Duering, M. Dichgans), Ludwig-Maximilians-University, Munich, Germany
- German Center for Neurodegenerative Diseases, DZNE, Munich, Germany (M. Dichgans)
- Munich Cluster for Systems Neurology, Germany (M. Dichgans)
| | - Robert J van Oostenbrugge
- Department of Radiology and Nuclear Medicine (P.H.M.V., J.F.A.J., W.H.B.), Maastricht University Medical Center, the Netherlands
- Department of Neurology (M.v.D, D.K., J.S., R.J.v.O.), Maastricht University Medical Center, the Netherlands
- Mental Health and Neuroscience Research Institute (P.H.M.V., R.J.v.O., J.F.A.J., W.H.B.), Maastricht University, the Netherlands
- Cardiovascular Diseases Research Institute (M.v.D., D.K., J.S., R.J.v.O., W.H.B.), Maastricht University, the Netherlands
| | - Jacobus F A Jansen
- Mental Health and Neuroscience Research Institute (P.H.M.V., R.J.v.O., J.F.A.J., W.H.B.), Maastricht University, the Netherlands
- Department of Electrical Engineering, Eindhoven University of Technology, the Netherlands (J.F.A.J.)
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences, UK Dementia Research Institute at The University of Edinburgh, United Kingdom (M.S.S., G.W.B., M.J.T., D.J.G., F.M.C., F.N.D., J.M.W.)
| | - Walter H Backes
- Mental Health and Neuroscience Research Institute (P.H.M.V., R.J.v.O., J.F.A.J., W.H.B.), Maastricht University, the Netherlands
- Cardiovascular Diseases Research Institute (M.v.D., D.K., J.S., R.J.v.O., W.H.B.), Maastricht University, the Netherlands
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Zarrini-Monfared Z, Parvaresh M, Mirbagheri MM. T 1 Thermometry for Deep Brain Stimulation Applications: A Comparison between Rapid Gradient Echo Sequences. J Biomed Phys Eng 2024; 14:569-578. [PMID: 39726884 PMCID: PMC11668934 DOI: 10.31661/jbpe.v0i0.2210-1546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 12/15/2022] [Indexed: 12/28/2024]
Abstract
Background T1 thermometry is considered a straight method for the safety monitoring of patients with deep brain stimulation (DBS) electrodes against radiofrequency-induced heating during Magnetic Resonance Imaging (MRI), requiring different sequences and methods. Objective This study aimed to compare two T1 thermometry methods and two low specific absorption rate (SAR) imaging sequences in terms of the output image quality. Material and Methods In this experimental study, a gel phantom was prepared, resembling the brain tissue properties with a copper wire inside. Two types of rapid gradient echo sequences, namely radiofrequency-spoiled and balanced steady-state free precession (bSSFP) sequences, were used. T1 thermometry was performed by either T1-weighted images with a high SAR sequence to increase heating around the wire or T1 mapping methods. Results The balanced steady-state free precession (bSSFP) sequence provided higher image quality in terms of spatial resolution (1×1×1.5 mm3 compared with 1×1×3 mm3) at a shorter acquisition time. The susceptibility artifact was also less pronounced for the bSSFP sequence compared with the radiofrequency-spoiled sequence. A temperature increase, of up to 8 ℃, was estimated using a high SAR sequence. The estimated change in temperature was reduced when using the T1 mapping method. Conclusion Heating induced during MRI of implanted electrodes could be estimated using high-resolution T1 maps obtained from inversion recovery bSSFP sequence. Such a method gives a direct estimation of heating during the imaging sequence, which is highly desirable for safe MRI of DBS patients.
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Affiliation(s)
- Zinat Zarrini-Monfared
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mansour Parvaresh
- Department of Neurosurgery, Hazrat Rasool Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Mehdi Mohammad Mirbagheri
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Department of Physical Medicine and Rehabilitation, Northwestern University, USA
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Nie Y, Lu N, Liao L, Liu Z, Gu A, Huang X, Tie C, Liu H, Huang Z, Xie G. Black-Blood Magnetization Prepared 2 Rapid Acquisition Gradient Echoes: A Fast and Three-Dimensional MR Black-Blood T 1 Mapping Technique for Quantitative Assessment of Atherosclerosis and Venous Thrombosis. J Magn Reson Imaging 2024; 60:1148-1162. [PMID: 38009385 DOI: 10.1002/jmri.29156] [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: 08/28/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 11/28/2023] Open
Abstract
BACKGROUND Blood flow signals may be a confounder in quantifying T1 values of plaque or thrombus and how to realize black-blood T1 mapping remains a challenge task. PURPOSE To develop a fast and three-dimensional black-blood T1 mapping technique for quantitative assessment of atherosclerosis and venous thrombosis. STUDY TYPE Sequence development and optimization via phantoms and volunteers as well as pilot prospective. PHANTOM AND SUBJECTS Numerical simulations, a standard phantom, 8 healthy volunteers (mean age, 22 ± 1 years; 5 males), and 19 patients (mean age, 57 ± 14 years; 13 males) with atherosclerosis or venous thrombosis. FIELD STRENGTH/SEQUENCE 3T/inversion recovery spin-echo sequence (IR-SE), magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE), and black-blood prepared MP2RAGE (BB-MP2RAGE). ASSESSMENT The black-blood preparation (i.e., delay alternating with nutation for tailored excitation, DANTE) was incorporated into MP2RAGE for black-blood T1 mapping. The BB-MP2RAGE was optimized numerically based on the Bloch equation, and then the phantom study was performed to verify the accuracy of T1 mapping by BB-MP2RAGE against IR-SE and MP2RAGE. Preliminary clinical validation was prospectively performed to assess the flow suppression effect and its potential application in plaque and thrombosis identification. STATISTICAL TESTS Pearson correlation test, Bland-Altman analysis, paired t-test, and intraclass correlation coefficient. A P value <0.05 indicates a statistically significant difference. RESULTS Phantom experiments showed comparable accuracy of T1 maps by BB-MP2RAGE with IR-SE and MP2RAGE (all r2 > 0.99); Compared to MP2RAGE, BB-MP2RAGE effectively nulled the blood flow signals, and had a significant improvement in contrast-to-noise ratio between static tissue and blood (250.5 ± 66.6 vs. 91.9 ± 35.9). BB-MP2RAGE can quantify plaque or thrombus T1 relaxation time with blood flow signal suppression. DATA CONCLUSION Accurate T1 mapping with sufficient blood flow suppression was achieved by BB-MP2RAGE. BB-MP2RAGE has the potential to quantitatively characterize atherosclerosis and venous thrombosis. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Yuhui Nie
- School of Biomedical Engineering, The Sixth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Na Lu
- School of Biomedical Engineering, The Sixth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Liping Liao
- Department of Radiology, The First People's Hospital of Qinzhou, Qinzhou, China
| | - Zeping Liu
- School of Biomedical Engineering, The Sixth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Anyan Gu
- School of Biomedical Engineering, The Sixth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Xin Huang
- School of Biomedical Engineering, The Sixth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Changjun Tie
- Paul C. Lauterbur Imaging Center, Shenzhen Institutes Advanced Technology, Shenzhen, Guangdong, China
| | - Hongyan Liu
- School of Biomedical Engineering, The Sixth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Zehe Huang
- Department of Radiology, The First People's Hospital of Qinzhou, Qinzhou, China
| | - Guoxi Xie
- School of Biomedical Engineering, The Sixth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China
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Köhler C, Kuntke P, Sahoo P, Wahl H, Deoni SCL, Gärtner J, Dreha-Kulaczewski S, Kitzler HH. Atlas-based assessment of hypomyelination: Quantitative MRI in Pelizaeus-Merzbacher disease. Hum Brain Mapp 2024; 45:e70014. [PMID: 39230009 PMCID: PMC11372820 DOI: 10.1002/hbm.70014] [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: 02/27/2024] [Revised: 08/07/2024] [Accepted: 08/14/2024] [Indexed: 09/05/2024] Open
Abstract
Pelizaeus-Merzbacher disease (PMD) is a rare childhood hypomyelinating leukodystrophy. Quantification of the pronounced myelin deficit and delineation of subtle myelination processes are of high clinical interest. Quantitative magnetic resonance imaging (qMRI) techniques can provide in vivo insights into myelination status, its spatial distribution, and dynamics during brain maturation. They may serve as potential biomarkers to assess the efficacy of myelin-modulating therapies. However, registration techniques for image quantification and statistical comparison of affected pediatric brains, especially those of low or deviant image tissue contrast, with healthy controls are not yet established. This study aimed first to develop and compare postprocessing pipelines for atlas-based quantification of qMRI data in pediatric patients with PMD and evaluate their registration accuracy. Second, to apply an optimized pipeline to investigate spatial myelin deficiency using myelin water imaging (MWI) data from patients with PMD and healthy controls. This retrospective single-center study included five patients with PMD (mean age, 6 years ± 3.8) who underwent conventional brain MRI and diffusion tensor imaging (DTI), with MWI data available for a subset of patients. Three methods of registering PMD images to a pediatric template were investigated. These were based on (a) T1-weighted (T1w) images, (b) fractional anisotropy (FA) maps, and (c) a combination of T1w, T2-weighted, and FA images in a multimodal approach. Registration accuracy was determined by visual inspection and calculated using the structural similarity index method (SSIM). SSIM values for the registration approaches were compared using a t test. Myelin water fraction (MWF) was quantified from MWI data as an assessment of relative myelination. Mean MWF was obtained from two PMDs (mean age, 3.1 years ± 0.3) within four major white matter (WM) pathways of a pediatric atlas and compared to seven healthy controls (mean age, 3 years ± 0.2) using a Mann-Whitney U test. Our results show that visual registration accuracy estimation and computed SSIM were highest for FA-based registration, followed by multimodal, and T1w-based registration (SSIMFA = 0.67 ± 0.04 vs. SSIMmultimodal = 0.60 ± 0.03 vs. SSIMT1 = 0.40 ± 0.14). Mean MWF of patients with PMD within the WM pathways was significantly lower than in healthy controls MWFPMD = 0.0267 ± 0.021 vs. MWFcontrols = 0.1299 ± 0.039. Specifically, MWF was measurable in brain structures known to be myelinated at birth (brainstem) or postnatally (projection fibers) but was scarcely detectable in other brain regions (commissural and association fibers). Taken together, our results indicate that registration accuracy was highest with an FA-based registration pipeline, providing an alternative to conventional T1w-based registration approaches in the case of hypomyelinating leukodystrophies missing normative intrinsic tissue contrasts. The applied atlas-based analysis of MWF data revealed that the extent of spatial myelin deficiency in patients with PMD was most pronounced in commissural and association and to a lesser degree in brainstem and projection pathways.
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Affiliation(s)
- Caroline Köhler
- Institute of Diagnostic and Interventional Neuroradiology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Paul Kuntke
- Institute of Diagnostic and Interventional Neuroradiology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Prativa Sahoo
- Department of Pediatrics and Adolescent Medicine, University Medical Center Göttingen, Göttingen, Germany
| | - Hannes Wahl
- Institute of Diagnostic and Interventional Neuroradiology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Sean C L Deoni
- Advanced Baby Imaging Lab, School of Engineering, Brown University, Providence, Rhode Island, USA
- Maternal, Newborn, and Child Health Discovery and Tools, Bill and Melinda Gates Foundation, Seattle, Washington, USA
| | - Jutta Gärtner
- Department of Pediatrics and Adolescent Medicine, University Medical Center Göttingen, Göttingen, Germany
| | - Steffi Dreha-Kulaczewski
- Department of Pediatrics and Adolescent Medicine, University Medical Center Göttingen, Göttingen, Germany
| | - Hagen H Kitzler
- Institute of Diagnostic and Interventional Neuroradiology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
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8
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Simegn GL, Gagoski B, Song Y, Dean DC, Hupfeld KE, Murali-Manohar S, Davies-Jenkins CW, Simičić D, Wisnowski J, Yedavalli V, Gudmundson AT, Zöllner HJ, Oeltzschner G, Edden RAE. Comparison of test-retest reproducibility of DESPOT and 3D-QALAS for water T 1 and T 2 mapping. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.15.608081. [PMID: 39229114 PMCID: PMC11370424 DOI: 10.1101/2024.08.15.608081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Purpose Relaxometry, specifically T 1 and T 2 mapping, has become an essential technique for assessing the properties of biological tissues related to various physiological and pathological conditions. Many techniques are being used to estimate T 1 and T 2 relaxation times, ranging from the traditional inversion or saturation recovery and spin-echo sequences to more advanced methods. Choosing the appropriate method for a specific application is critical since the precision and accuracy of T 1 and T 2 measurements are influenced by a variety of factors including the pulse sequence and its parameters, the inherent properties of the tissue being examined, the MRI hardware, and the image reconstruction. The aim of this study is to evaluate and compare the test-retest reproducibility of two advanced MRI relaxometry techniques (Driven Equilibrium Single Pulse Observation of T 1 and T 2, DESPOT, and 3D Quantification using an interleaved Look-Locker acquisition Sequence with a T 2 preparation pulse, QALAS), for T 1 and T 2 mapping in a healthy volunteer cohort. Methods 10 healthy volunteers underwent brain MRI at 1.3 mm3 isotropic resolution, acquiring DESPOT and QALAS data (~11.8 and ~5 minutes duration, including field maps, respectively), test-retest with subject repositioning, on a 3.0 Tesla Philips Ingenia Elition scanner. To reconstruct the T 1 and T 2 maps, we used an equation-based algorithm for DESPOT and a dictionary-based algorithm that incorporates inversion efficiency and B 1 -field inhomogeneity for QALAS. The test-retest reproducibility was assessed using the coefficient of variation (CoV), intraclass correlation coefficient (ICC) and Bland-Altman plots. Results Our results indicate that both the DESPOT and QALAS techniques demonstrate good levels of test-retest reproducibility for T 1 and T 2 mapping across the brain. Higher whole-brain voxel-to-voxel ICCs are observed in QALAS for T 1 (0.84 ± 0.039) and in DESPOT for T 2 (0.897 ± 0.029). The Bland-Altman plots show smaller bias and variability of T 1 estimates for QALAS (mean of -0.02 s, and upper and lower limits of -0.14 and 0.11 s, 95% CI) than for DESPOT (mean of -0.02 s, and limits of -0.31 and 0.27 s). QALAS also showed less variability (mean 1.08 ms, limits -1.88 to 4.04 ms) for T 2 compared to DESPOT (mean of 2.56 ms, and limits -17.29 to 22.41 ms). The within-subject CoVs for QALAS range from 0.6% (T 2 in CSF) to 5.8% (T 2 in GM), while for DESPOT they range from 2.1% (T 2 in CSF) to 6.7% (T 2 in GM). The between-subject CoVs for QALAS range from 2.5% (T 2 in GM) to 12% (T 2 in CSF), and for DESPOT they range from 3.7% (T 2 in WM) to 9.3% (T 2 in CSF). Conclusion Overall, QALAS demonstrated better reproducibility for T 1 and T 2 measurements than DESPOT, in addition to reduced acquisition time.
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Affiliation(s)
- Gizeaddis Lamesgin Simegn
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Borjan Gagoski
- Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children’s Hospital, Boston, Massachusetts, USA
| | - Yulu Song
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Douglas C. Dean
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Kathleen E. Hupfeld
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Saipavitra Murali-Manohar
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Christopher W. Davies-Jenkins
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Dunja Simičić
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Jessica Wisnowski
- Department of Pediatrics, Division of Neurology, Children’s Hospital Los Angeles and the University of Southern California
| | - Vivek Yedavalli
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Aaron T. Gudmundson
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
- The Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA
| | - Helge J. Zöllner
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Georg Oeltzschner
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Richard A. E. Edden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
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9
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Aghaeifar A, Bosch D, Heule R, Williams S, Ehses P, Mauconduit F, Scheffler K. Intra-scan RF power amplifier drift correction. Magn Reson Med 2024; 92:645-659. [PMID: 38469935 DOI: 10.1002/mrm.30078] [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: 09/14/2023] [Revised: 12/18/2023] [Accepted: 02/21/2024] [Indexed: 03/13/2024]
Abstract
PURPOSE The drift in radiofrequency (RF) power amplifiers (RFPAs) is assessed and several contributing factors are investigated. Two approaches for prospective correction of drift are proposed and their effectiveness is evaluated. METHODS RFPA drift assessment encompasses both intra-pulse and inter-pulse drift analyses. Scan protocols with varying flip angle (FA), RF length, and pulse repetition time (TR) are used to gauge the influence of these parameters on drift. Directional couplers (DICOs) monitor the forward waveforms of the RFPA outputs. DICOs data is stored for evaluation, allowing calculation of correction factors to adjust RFPAs' transmit voltage. Two correction methods, predictive and run-time, are employed: predictive correction necessitates a calibration scan, while run-time correction calculates factors during the ongoing scan. RESULTS RFPA drift is indeed influenced by the RF duty-cycle, and in the cases examined with a maximum duty-cycle of 66%, the potential drift is approximately 41% or 15%, depending on the specific RFPA revision. Notably, in low transmit voltage scenarios, FA has minimal impact on RFPA drift. The application of predictive and run-time drift correction techniques effectively reduces the average drift from 10.0% to less than 1%, resulting in enhanced MR signal stability. CONCLUSION Utilizing DICO recordings and implementing a feedback mechanism enable the prospective correction of RFPA drift. Having a calibration scan, predictive correction can be utilized with fewer complexity; for enhanced performance, a run-time approach can be employed.
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Affiliation(s)
- Ali Aghaeifar
- High-Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
| | - Dario Bosch
- High-Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Department of Biomedical Magnetic Resonance, University of Tuebingen, Tuebingen, Germany
| | - Rahel Heule
- High-Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Department of Biomedical Magnetic Resonance, University of Tuebingen, Tuebingen, Germany
- Center for MR Research, University Children's Hospital, Zurich, Switzerland
| | - Sydney Williams
- Imaging Centre of Excellence, University of Glasgow, Glasgow, UK
| | - Philipp Ehses
- MR Physics, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | | | - Klaus Scheffler
- High-Field Magnetic Resonance Center, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany
- Department of Biomedical Magnetic Resonance, University of Tuebingen, Tuebingen, Germany
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10
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Caporale AS, Chiarelli AM, Biondetti E, Villani A, Lipp I, Di Censo D, Tomassini V, Wise RG. Changes of brain parenchyma free water fraction reflect tissue damage and impaired processing speed in multiple sclerosis. Hum Brain Mapp 2024; 45:e26761. [PMID: 38895882 PMCID: PMC11187860 DOI: 10.1002/hbm.26761] [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: 06/30/2023] [Revised: 05/13/2024] [Accepted: 06/02/2024] [Indexed: 06/21/2024] Open
Abstract
Free water fraction (FWF) represents the amount of water per unit volume of brain parenchyma, which is not bound to macromolecules. Its excess in multiple sclerosis (MS) is related to increased tissue loss. The use of mcDESPOT (multicomponent driven single pulse observation of T1 and T2), a 3D imaging method which exploits both the T1 and T2 contrasts, allows FWF to be derived in clinically feasible times. However, this method has not been used to quantify changes of FWF and their potential clinical impact in MS. The aim of this study is to investigate the changes in FWF in MS patients and their relationship with tissue damage and cognition, under the hypothesis that FWF is a proxy of clinically meaningful tissue loss. To this aim, we tested the relationship between FWF, MS lesion burden and information processing speed, evaluated via the Symbol Digit Modalities Test (SDMT). In addition to standard sequences, used for T1- and T2-weighted lesion delineation, the mcDESPOT sequence with 1.7 mm isotropic resolution and a diffusion weighted imaging protocol (b = 0, 1200 s/mm2, 40 diffusion directions) were employed at 3 T. The fractional anisotropy map derived from diffusion data was used to define a subject-specific white matter (WM) atlas. Brain parenchyma segmentation returned masks of gray matter (GM) and WM, and normal-appearing WM (NAWM), in addition to the T1 and T2 lesion masks (T1L and T2L, respectively). Ninety-nine relapsing-remitting MS patients (age = 43.3 ± 9.9 years, disease duration 12.3 ± 7.7 years) were studied, together with twenty-five healthy controls (HC, age = 38.8 ± 11.0 years). FWF was higher in GM and NAWM of MS patients, compared to GM and WM of HC (both p < .001). In MS patients, FWF was the highest in the T1L and GM, followed by T2L and NAWM, respectively. FWF increased significantly with T1L and T2L volume (ρ ranging from 0.40 to 0.58, p < .001). FWF in T2L was strongly related to both T1L volume and the volume ratio T1L/T2L (ρ = 0.73, p < .001). MS patients performed worse than HC in the processing speed test (mean ± SD: 54.1 ± 10.3 for MS, 63.8 ± 10.8 for HC). FWF in GM, T2L, perilesional tissue and NAWM increased with SDMT score reduction (ρ = -0.30, -0.29, -0.33 respectively and r = -.30 for T2L, all with p < .005). A regional analysis, conducted to determine which NAWM regions were of particular importance to explain the relationship between FWF and cognitive impairment, revealed that FWF spatial variance was negatively related to SDMT score in the corpus callosum and the superior longitudinal fasciculus, WM structures known to be associated with cognitive impairment, in addition to the left corticospinal tract, the sagittal stratum, the right anterior limb of internal capsule. In conclusion, we found excess free water in brain parenchyma of MS patients, an alteration that involved not only MS lesions, but also the GM and NAWM, impinging on brain function and negatively associated with cognitive processing speed. We suggest that the FWF metric, derived from noninvasive, rapid MRI acquisitions and bearing good biological interpretability, may prove valuable as an MRI biomarker of tissue damage and associated cognitive impairment in MS.
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Affiliation(s)
- Alessandra Stella Caporale
- Department of Neuroscience, Imaging and Clinical Sciences‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
| | - Antonio Maria Chiarelli
- Department of Neuroscience, Imaging and Clinical Sciences‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
| | - Emma Biondetti
- Department of Neuroscience, Imaging and Clinical Sciences‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
| | - Alessandro Villani
- Department of Neuroscience, Imaging and Clinical Sciences‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
| | - Ilona Lipp
- Department of NeurophysicsMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Davide Di Censo
- Department of Neuroscience, Imaging and Clinical Sciences‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
| | - Valentina Tomassini
- Department of Neuroscience, Imaging and Clinical Sciences‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- MS Centre, Department of Clinical Neurology‘SS. Annunziata’ University HospitalChietiItaly
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
| | - Richard Geoffrey Wise
- Department of Neuroscience, Imaging and Clinical Sciences‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Institute for Advanced Biomedical Technologies (ITAB)‘G. d'Annunzio University’ of Chieti‐PescaraChietiItaly
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of PsychologyCardiff UniversityCardiffUK
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11
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Soloukey S, Generowicz B, Warnert E, Springeling G, Schouten J, De Zeeuw C, Dirven C, Vincent A, Kruizinga P. Patient-Specific Vascular Flow Phantom for MRI- and Doppler Ultrasound Imaging. ULTRASOUND IN MEDICINE & BIOLOGY 2024; 50:860-868. [PMID: 38471997 DOI: 10.1016/j.ultrasmedbio.2024.02.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 01/29/2024] [Accepted: 02/16/2024] [Indexed: 03/14/2024]
Abstract
OBJECTIVE Intraoperative Doppler ultrasound imaging of human brain vasculature is an emerging neuro-imaging modality that offers vascular brain mapping with unprecedented spatiotemporal resolution. At present, however, access to the human brain using Doppler Ultrasound is only possible in this intraoperative context, posing a significant challenge for validation of imaging techniques. This challenge necessitates the development of realistic flow phantoms outside of the neurosurgical operating room as external platforms for testing hardware and software. An ideal ultrasound flow phantom should provide reference-like values in standardized topologies such as a slanted pipe, and allow for measurements in structures closely resembling vascular morphology of actual patients. Additionally, the phantom should be compatible with other clinical cerebrovascular imaging modalities. To meet these criteria, we developed and validated a versatile, multimodal MRI- and ultrasound Doppler phantom. METHODS Our approach incorporates the latest advancements in phantom research using tissue-mimicking material and 3D-printing with water-soluble resin to create wall-less patient-specific lumens, compatible for ultrasound and MRI. RESULTS We successfully produced three distinct phantoms: a slanted pipe, a y-shape phantom representing a bifurcating vessel and an arteriovenous malformation (AVM) derived from clinical Digital Subtraction Angiography (DSA)-data of the brain. We present 3D ultrafast power Doppler imaging results from these phantoms, demonstrating their ability to mimic complex flow patterns as observed in the human brain. Furthermore, we showcase the compatibility of our phantom with Magnetic Resonance Imaging (MRI). CONCLUSION We developed an MRI- and Doppler Ultrasound-compatible flow-phantom using customizable, water-soluble resin prints ranging from geometrical forms to patient-specific vasculature.
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Affiliation(s)
- Sadaf Soloukey
- Department of Neuroscience, Erasmus MC, Rotterdam, The Netherlands; Department of Neurosurgery, Erasmus MC, Rotterdam, The Netherlands.
| | | | - Esther Warnert
- Deparment of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands; Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Geert Springeling
- Deparment of Experimental Medical Instrumentation, Erasmus MC, Rotterdam, The Netherlands
| | - Joost Schouten
- Department of Neurosurgery, Erasmus MC, Rotterdam, The Netherlands
| | - Chris De Zeeuw
- Department of Neuroscience, Erasmus MC, Rotterdam, The Netherlands; Netherlands Institute for Neuroscience, Royal Dutch Academy for Arts and Sciences, Amsterdam, Netherlands
| | - Clemens Dirven
- Department of Neurosurgery, Erasmus MC, Rotterdam, The Netherlands
| | - Arnaud Vincent
- Department of Neurosurgery, Erasmus MC, Rotterdam, The Netherlands
| | - Pieter Kruizinga
- Department of Neuroscience, Erasmus MC, Rotterdam, The Netherlands
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12
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van Houdt PJ, Ragunathan S, Berks M, Ahmed Z, Kershaw LE, Gurney-Champion OJ, Tadimalla S, Arvidsson J, Sun Y, Kallehauge J, Dickie B, Lévy S, Bell L, Sourbron S, Thrippleton MJ. Contrast-agent-based perfusion MRI code repository and testing framework: ISMRM Open Science Initiative for Perfusion Imaging (OSIPI). Magn Reson Med 2024; 91:1774-1786. [PMID: 37667526 DOI: 10.1002/mrm.29826] [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: 03/24/2023] [Revised: 06/30/2023] [Accepted: 07/25/2023] [Indexed: 09/06/2023]
Abstract
PURPOSE Software has a substantial impact on quantitative perfusion MRI values. The lack of generally accepted implementations, code sharing and transparent testing reduces reproducibility, hindering the use of perfusion MRI in clinical trials. To address these issues, the ISMRM Open Science Initiative for Perfusion Imaging (OSIPI) aimed to establish a community-led, centralized repository for sharing open-source code for processing contrast-based perfusion imaging, incorporating an open-source testing framework. METHODS A repository was established on the OSIPI GitHub website. Python was chosen as the target software language. Calls for code contributions were made to OSIPI members, the ISMRM Perfusion Study Group, and publicly via OSIPI websites. An automated unit-testing framework was implemented to evaluate the output of code contributions, including visual representation of the results. RESULTS The repository hosts 86 implementations of perfusion processing steps contributed by 12 individuals or teams. These cover all core aspects of DCE- and DSC-MRI processing, including multiple implementations of the same functionality. Tests were developed for 52 implementations, covering five analysis steps. For T1 mapping, signal-to-concentration conversion and population AIF functions, different implementations resulted in near-identical output values. For the five pharmacokinetic models tested (Tofts, extended Tofts-Kety, Patlak, two-compartment exchange, and two-compartment uptake), differences in output parameters were observed between contributions. CONCLUSIONS The OSIPI DCE-DSC code repository represents a novel community-led model for code sharing and testing. The repository facilitates the re-use of existing code and the benchmarking of new code, promoting enhanced reproducibility in quantitative perfusion imaging.
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Affiliation(s)
- Petra J van Houdt
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Michael Berks
- Quantitative Biomedical Imaging Laboratory, Division of Cancer Sciences, The University of Manchester, Manchester, UK
| | - Zaki Ahmed
- Corewell Health William Beaumont University Hospital, Diagnostic Radiology, Royal Oak, USA
| | - Lucy E Kershaw
- Edinburgh Imaging and Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Oliver J Gurney-Champion
- Department of Radiology and Nuclear Medicine, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Sirisha Tadimalla
- Institute of Medical Physics, The University of Sydney, Sydney, Australia
| | - Jonathan Arvidsson
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Yu Sun
- Institute of Medical Physics, The University of Sydney, Sydney, Australia
| | - Jesper Kallehauge
- Aarhus University Hospital, Danish Centre for Particle Therapy, Aarhus, Denmark
- Aarhus University, Department of Clinical Medicine, Aarhus, Denmark
| | - Ben Dickie
- Division of Informatics, Imaging, and Data Science, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, Northern Care Alliance NHS Group, The University of Manchester, Manchester, UK
| | - Simon Lévy
- MR Research Collaborations, Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Laura Bell
- Genentech, Inc, Clinical Imaging Group, South San Francisco, USA
| | - Steven Sourbron
- University of Sheffield, Department of Infection, Immunity and Cardiovascular Disease, Sheffield, UK
| | - Michael J Thrippleton
- University of Edinburgh, Edinburgh Imaging and Centre for Clinical Brain Sciences, Edinburgh, UK
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Huaroc Moquillaza E, Weiss K, Stelter J, Steinhelfer L, Lee YJ, Amthor T, Koken P, Makowski MR, Braren R, Doneva M, Karampinos DC. Accelerated liver water T 1 mapping using single-shot continuous inversion-recovery spiral imaging. NMR IN BIOMEDICINE 2024; 37:e5097. [PMID: 38269568 DOI: 10.1002/nbm.5097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/21/2023] [Accepted: 12/06/2023] [Indexed: 01/26/2024]
Abstract
PURPOSE Liver T1 mapping techniques typically require long breath holds or long scan time in free-breathing, need correction for B 1 + inhomogeneities and process composite (water and fat) signals. The purpose of this work is to accelerate the multi-slice acquisition of liver water selective T1 (wT1) mapping in a single breath hold, improving the k-space sampling efficiency. METHODS The proposed continuous inversion-recovery (IR) Look-Locker methodology combines a single-shot gradient echo spiral readout, Dixon processing and a dictionary-based analysis for liver wT1 mapping at 3 T. The sequence parameters were adapted to obtain short scan times. The influence of fat, B 1 + inhomogeneities and TE on the estimation of T1 was first assessed using simulations. The proposed method was then validated in a phantom and in 10 volunteers, comparing it with MRS and the modified Look-Locker inversion-recovery (MOLLI) method. Finally, the clinical feasibility was investigated by comparing wT1 maps with clinical scans in nine patients. RESULTS The phantom results are in good agreement with MRS. The proposed method encodes the IR-curve for the liver wT1 estimation, is minimally sensitive to B 1 + inhomogeneities and acquires one slice in 1.2 s. The volunteer results confirmed the multi-slice capability of the proposed method, acquiring nine slices in a breath hold of 11 s. The present work shows robustness to B 1 + inhomogeneities (wT 1 , No B 1 + = 1.07 wT 1 , B 1 + - 45.63 , R 2 = 0.99 ) , good repeatability (wT 1 , 2 ° = 1 . 0 wT 1 , 1 ° - 2.14 , R 2 = 0.96 ) and is in better agreement with MRS (wT 1 = 0.92 wT 1 MRS + 103.28 , R 2 = 0.38 ) than is MOLLI (wT 1 MOLLI = 0.76 wT 1 MRS + 254.43 , R 2 = 0.44 ) . The wT1 maps in patients captured diverse lesions, thus showing their clinical feasibility. CONCLUSION A single-shot spiral acquisition can be combined with a continuous IR Look-Locker method to perform rapid repeatable multi-slice liver water T1 mapping at a rate of 1.2 s per slice without a B 1 + map. The proposed method is suitable for nine-slice liver clinical applications acquired in a single breath hold of 11 s.
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Affiliation(s)
- Elizabeth Huaroc Moquillaza
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | | | - Jonathan Stelter
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Lisa Steinhelfer
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | | | | | | | - Marcus R Makowski
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Rickmer Braren
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | | | - Dimitrios C Karampinos
- Department of Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
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14
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Brandejsky V, Dahlqvist OL, Lund E, Lundberg P. Phosphorus-31: A table-top method for 3D B 1-field amplitude and phase measurements. BIOCHIMICA ET BIOPHYSICA ACTA. BIOMEMBRANES 2024; 1866:184307. [PMID: 38408694 DOI: 10.1016/j.bbamem.2024.184307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 01/23/2024] [Accepted: 02/18/2024] [Indexed: 02/28/2024]
Abstract
A novel method of high-spatial-resolution, 3D B1-field distribution measurements is presented. The method is independent of the MR-scanner, and it allows for automated acquisitions of complete maps of all magnetic field vector components for both proton and heteronuclear MR coils of arbitrary geometrical shapes. The advantage of the method proposed here, compared with methods based on measurements with an MR-scanner, is that a complete image of both receive and transmit B1-fields, including the phase of the B1-field, can be acquired. The B1 field maps obtained in this manner can be used for absolute quantification of metabolites in MRS experiments, as well as for intensity compensations in imaging experiments, both of which are important concepts in biological and medical MR applications. Another use might be in coil development and testing. A comparison with B1 field magnitude maps obtained with an MR-scanner was included to validate the accuracy of the proposed method.
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Affiliation(s)
- V Brandejsky
- Dept of Radiation Physics, and Depth of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - O Leinhard Dahlqvist
- Dept of Radiation Physics, and Depth of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden
| | - E Lund
- Dept of Radiation Physics, and Depth of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - P Lundberg
- Dept of Radiation Physics, and Depth of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
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15
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Jochems ACC, Muñoz Maniega S, Clancy U, Arteaga C, Jaime Garcia D, Chappell FM, Hewins W, Locherty R, Backhouse EV, Barclay G, Jardine C, McIntyre D, Gerrish I, Kampaite A, Sakka E, Valdés Hernández M, Wiseman S, Bastin ME, Stringer MS, Thrippleton MJ, Doubal FN, Wardlaw JM. Magnetic Resonance Imaging Tissue Signatures Associated With White Matter Changes Due to Sporadic Cerebral Small Vessel Disease Indicate That White Matter Hyperintensities Can Regress. J Am Heart Assoc 2024; 13:e032259. [PMID: 38293936 PMCID: PMC11056146 DOI: 10.1161/jaha.123.032259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 12/21/2023] [Indexed: 02/01/2024]
Abstract
BACKGROUND White matter hyperintensities (WMHs) might regress and progress contemporaneously, but we know little about underlying mechanisms. We examined WMH change and underlying quantitative magnetic resonance imaging tissue measures over 1 year in patients with minor ischemic stroke with sporadic cerebral small vessel disease. METHODS AND RESULTS We defined areas of stable normal-appearing white matter, stable WMHs, progressing and regressing WMHs based on baseline and 1-year brain magnetic resonance imaging. In these areas we assessed tissue characteristics with quantitative T1, fractional anisotropy (FA), mean diffusivity (MD), and neurite orientation dispersion and density imaging (baseline only). We compared tissue signatures cross-sectionally between areas, and longitudinally within each area. WMH change masks were available for N=197. Participants' mean age was 65.61 years (SD, 11.10), 59% had a lacunar infarct, and 68% were men. FA and MD were available for N=195, quantitative T1 for N=182, and neurite orientation dispersion and density imaging for N=174. Cross-sectionally, all 4 tissue classes differed for FA, MD, T1, and Neurite Density Index. Longitudinally, in regressing WMHs, FA increased with little change in MD and T1 (difference estimate, 0.011 [95% CI, 0.006-0.017]; -0.002 [95% CI, -0.008 to 0.003] and -0.003 [95% CI, -0.009 to 0.004]); in progressing and stable WMHs, FA decreased (-0.022 [95% CI, -0.027 to -0.017] and -0.009 [95% CI, -0.011 to -0.006]), whereas MD and T1 increased (progressing WMHs, 0.057 [95% CI, 0.050-0.063], 0.058 [95% CI, 0.050 -0.066]; stable WMHs, 0.054 [95% CI, 0.045-0.063], 0.049 [95% CI, 0.039-0.058]); and in stable normal-appearing white matter, MD increased (0.004 [95% CI, 0.003-0.005]), whereas FA and T1 slightly decreased and increased (-0.002 [95% CI, -0.004 to -0.000] and 0.005 [95% CI, 0.001-0.009]). CONCLUSIONS Quantitative magnetic resonance imaging shows that WMHs that regress have less abnormal microstructure at baseline than stable WMHs and follow trajectories indicating tissue improvement compared with stable and progressing WMHs.
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Affiliation(s)
- Angela C. C. Jochems
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Susana Muñoz Maniega
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Una Clancy
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Carmen Arteaga
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Daniela Jaime Garcia
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Francesca M. Chappell
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Will Hewins
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Rachel Locherty
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Ellen V. Backhouse
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Gayle Barclay
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
| | - Charlotte Jardine
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
| | - Donna McIntyre
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
| | - Iona Gerrish
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
| | - Agniete Kampaite
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
| | - Eleni Sakka
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
| | - Maria Valdés Hernández
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Stewart Wiseman
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Mark E. Bastin
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
| | - Michael S. Stringer
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Michael J. Thrippleton
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
| | - Fergus N. Doubal
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
| | - Joanna M. Wardlaw
- Centre for Clinical Brain SciencesUniversity of EdinburghEdinburghUnited Kingdom
- UK Dementia Research Institute at the University of EdinburghEdinburghUnited Kingdom
- Edinburgh Imaging Facility, Royal Infirmary of EdinburghEdinburghUnited Kingdom
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16
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Gangadin SS, Mandl RCW, de Witte LD, van Haren NEM, Schutte MJL, Begemann MJH, Kahn RS, Sommer IEC. Lower fractional anisotropy without evidence for neuro-inflammation in patients with early-phase schizophrenia spectrum disorders. Schizophr Res 2024; 264:557-566. [PMID: 36577563 DOI: 10.1016/j.schres.2022.12.009] [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: 06/09/2022] [Revised: 12/05/2022] [Accepted: 12/07/2022] [Indexed: 12/28/2022]
Abstract
Various lines of research suggest immune dysregulation as a potential therapeutic target for negative and cognitive symptoms in schizophrenia spectrum disorders (SSD). Immune dysregulation would lead to higher extracellular free-water (EFW) in cerebral white matter (WM), which may partially underlie the frequently reported lower fractional anisotropy (FA) in SSD. We aim to investigate differences in EFW concentrations - a presumed proxy for neuro-inflammation - between early-phase SSD patients (n = 55) and healthy controls (HC; n = 37), and to explore immunological and cognitive correlates. To increase specificity for EFW, we study several complementary magnetic resonance imaging contrasts that are sensitive to EFW. FA, mean diffusivity (MD), magnetization transfer ratio (MTR), myelin water fraction (MWF) and quantitative T1 and T2 were calculated from diffusion-weighted imaging (DWI), magnetization transfer imaging (MTI) and multicomponent driven equilibrium single-pulse observation of T1/T2 (mcDESPOT). For each measure, WM skeletons were constructed with tract-based spatial statistics. Multivariate SSD-HC comparisons with WM skeletons and their average values (i.e. global WM) were not statistically significant. In voxel-wise analyses, FA was significantly lower in SSD in the genu of the corpus callosum and in the left superior longitudinal fasciculus (p < 0.04). Global WM measures did not correlate with immunological markers (i.e. IL1-RA, IL-6, IL-8, IL-10 and CRP) or cognition in HC and SSD after corrections for multiple comparisons. We confirmed lower FA in early-phase SSD patients. However, nonFA measures did not provide additional evidence for immune dysregulation or for higher EFW as the primary mechanism underlying the reported lower FA values in SSD.
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Affiliation(s)
- Shiral S Gangadin
- Section Cognitive Neuroscience, Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
| | - René C W Mandl
- Section Cognitive Neuroscience, Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Lot D de Witte
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
| | - Neeltje E M van Haren
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus University Medical Center-Sophia Children's Hospital, Rotterdam, the Netherlands.
| | - Maya J L Schutte
- Section Cognitive Neuroscience, Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
| | - Marieke J H Begemann
- Section Cognitive Neuroscience, Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
| | - René S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
| | - Iris E C Sommer
- Section Cognitive Neuroscience, Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
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17
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Jang A, Han PK, Ma C, El Fakhri G, Wang N, Samsonov A, Liu F. B 1 inhomogeneity-corrected T 1 mapping and quantitative magnetization transfer imaging via simultaneously estimating Bloch-Siegert shift and magnetization transfer effects. Magn Reson Med 2023; 90:1859-1873. [PMID: 37427533 PMCID: PMC10528411 DOI: 10.1002/mrm.29778] [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: 02/13/2023] [Revised: 05/10/2023] [Accepted: 06/06/2023] [Indexed: 07/11/2023]
Abstract
PURPOSE To introduce a method of inducing Bloch-Siegert shift and magnetization Transfer Simultaneously (BTS) and demonstrate its utilization for measuring binary spin-bath model parameters free pool spin-lattice relaxation (T 1 F $$ {T}_1^{\mathrm{F}} $$ ), macromolecular fraction (f $$ f $$ ), magnetization exchange rate (k F $$ {k}_{\mathrm{F}} $$ ) and local transmit field (B 1 + $$ {B}_1^{+} $$ ). THEORY AND METHODS Bloch-Siegert shift and magnetization transfer is simultaneously induced through the application of off-resonance irradiation in between excitation and acquisition of an RF-spoiled gradient-echo scheme. Applying the binary spin-bath model, an analytical signal equation is derived and verified through Bloch simulations. Monte Carlo simulations were performed to analyze the method's performance. The estimation of the binary spin-bath parameters withB 1 + $$ {B}_1^{+} $$ compensation was further investigated through experiments, both ex vivo and in vivo. RESULTS Comparing BTS with existing methods, simulations showed that existing methods can significantly biasT 1 $$ {T}_1 $$ estimation when not accounting for transmitB 1 $$ {B}_1 $$ heterogeneity and MT effects that are present. Phantom experiments further showed that the degree of this bias increases with increasing macromolecular proton fraction. Multi-parameter fit results from an in vivo brain study generated values in agreement with previous literature. Based on these studies, we confirmed that BTS is a robust method for estimating the binary spin-bath parameters in macromolecule-rich environments, even in the presence ofB 1 + $$ {B}_1^{+} $$ inhomogeneity. CONCLUSION A method of estimating Bloch-Siegert shift and magnetization transfer effect has been developed and validated. Both simulations and experiments confirmed that BTS can estimate spin-bath parameters (T 1 F $$ {T}_1^{\mathrm{F}} $$ ,f $$ f $$ ,k F $$ {k}_{\mathrm{F}} $$ ) that are free fromB 1 + $$ {B}_1^{+} $$ bias.
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Affiliation(s)
- Albert Jang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Paul K Han
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Chao Ma
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Massachusetts General Hospital, Boston, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Nian Wang
- Indiana University, Indianapolis, Indiana, United States
| | | | - Fang Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
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18
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Skorska MN, Thurston LT, Biasin JM, Devenyi GA, Zucker KJ, Chakravarty MM, Lai MC, VanderLaan DP. Cortical Structure Differences in Relation to Age, Sexual Attractions, and Gender Dysphoria in Adolescents: An Examination of Mean Diffusivity and T1 Relaxation Time. Brain Sci 2023; 13:963. [PMID: 37371441 PMCID: PMC10296103 DOI: 10.3390/brainsci13060963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 06/08/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023] Open
Abstract
Recent research found that the combination of masculine gender identity and gynephilia was associated with cortical T1 relaxation time, which is considered to reflect gray matter density. We hypothesized that mean diffusivity (MD), a diffusion tensor imaging metric that reflects the degree to which water movement is free versus constrained, in combination with T1 relaxation time would provide further insight regarding cortical tissue characteristics. MD and T1 relaxation time were measured in 76 cortical regions in 15 adolescents assigned female at birth who experience gender dysphoria (GD AFAB) and were not receiving hormone therapy, 17 cisgender girls, and 14 cisgender boys (ages 12-17 years). Sexual orientation was represented by the degree of androphilia-gynephilia and the strength of sexual attraction. In multivariate analyses, cortical T1 relaxation time showed a weak but statistically significant positive association with MD across the cortex, suggesting that macromolecule-rich cortical tissue also tends to show water movement that is somewhat more constrained. In further multivariate analyses, in several left frontal, parietal, and temporal regions, the combination of shorter T1 relaxation time and faster MD was associated with older age and greater gynephilia in GD AFAB individuals and cisgender boys and with stronger attractions in cisgender boys only. Thus, for these cortical regions in these groups, older age, gynephilia, and stronger attractions (cisgender boys only) were associated with macromolecule-rich tissue in which water movement was freer-a pattern that some prior research suggests is associated with greater cell density and size. Overall, this study indicates that investigating T1 relaxation time and MD together can further inform how cortical gray matter tissue characteristics relate to age and psychosexuality.
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Affiliation(s)
- Malvina N. Skorska
- Child & Youth Psychiatry, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada; (M.N.S.)
| | - Lindsey T. Thurston
- Department of Psychology, University of Toronto Mississauga, Mississauga, ON L5L 1C6, Canada
| | - Jessica M. Biasin
- Department of Psychology, University of Toronto Mississauga, Mississauga, ON L5L 1C6, Canada
| | - Gabriel A. Devenyi
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, QC H4H 1R3, Canada (M.M.C.)
- Department of Psychiatry, McGill University, Montreal, QC H3A 1A1, Canada
| | - Kenneth J. Zucker
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
| | - M. Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, QC H4H 1R3, Canada (M.M.C.)
- Department of Psychiatry, McGill University, Montreal, QC H3A 1A1, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal, QC H3A 2B4, Canada
| | - Meng-Chuan Lai
- Child & Youth Psychiatry, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada; (M.N.S.)
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON M5T 1R8, Canada
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health, Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada
- Department of Psychiatry and Autism Research Unit, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge CB2 8AH, UK
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei 100229, Taiwan
| | - Doug P. VanderLaan
- Child & Youth Psychiatry, Centre for Addiction and Mental Health, Toronto, ON M6J 1H4, Canada; (M.N.S.)
- Department of Psychology, University of Toronto Mississauga, Mississauga, ON L5L 1C6, Canada
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19
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Negi PS, Mehta SB, Jena A, Rana P. K trans Calculation Using Reference Method Corrected Native T 10 for Breast Cancer Diagnosis. J Med Phys 2023; 48:19-25. [PMID: 37342602 PMCID: PMC10277302 DOI: 10.4103/jmp.jmp_90_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 12/12/2022] [Accepted: 12/21/2022] [Indexed: 06/23/2023] Open
Abstract
Purpose The objective of the study is to use multiple tube phantoms to generate correction factor at different spatial locations for each breast coil cuff to correct the native T10 value in the corresponding spatial location of the breast lesion. The corrected T10 value was used to compute Ktrans and analyze its diagnostic accuracy in the classification of target condition, i.e., breast tumors into malignant and benign. Materials and Methods Both in vitro phantom study (external reference) and patient's studies were acquired on simultaneous positron emission tomography/magnetic resonance imaging (PET/MRI) Biograph molecular magnetic resonance (mMR) system using 4 channel mMR breast coil. The spatial correction factors derived using multiple tube phantom were used for a retrospective analysis of dynamic contrast-enhanced (DCE) MRI data of 39 patients with a mean age of 50 years (31-77 years) having 51 enhancing breast lesions. Results Corrected and non-corrected receiver operating characteristic (ROC) curve analysis revealed a mean Ktrans value of 0.64 min-1 and 0.60 min-1, respectively. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy for non-corrected data were 86.21%, 81.82%, 86.20%, 81.81%, and 84.31%, respectively, and for corrected data were 93.10%, 86.36%, 90%, 90.47%, and 90.20% respectively. The area under curve (AUC) of corrected data was improved to 0.959 (95% confidence interval [CI] 0.862-0.994) from 0.824 (95% CI 0.694-0.918) of non-corrected data, and for NPV, it was improved to 90.47% from 81.81%, respectively. Conclusion T10 values were normalized using multiple tube phantom which was used for computation of Ktrans. We found significant improvement in the diagnostic accuracy of corrected Ktrans values that results in better characterization of breast lesions.
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Affiliation(s)
- Pradeep Singh Negi
- PET Suite (Indraprastha Apollo Hospitals and House of Diagnostics), Department of Molecular Imaging and Nuclear Medicine, Indraprastha Apollo Hospitals, New Delhi, India
- Department of Physics, Vivekananda Global University, Jaipur, Rajasthan, India
| | - Shashi Bhushan Mehta
- PET Suite (Indraprastha Apollo Hospitals and House of Diagnostics), Department of Molecular Imaging and Nuclear Medicine, Indraprastha Apollo Hospitals, New Delhi, India
- Department of Physics, Vivekananda Global University, Jaipur, Rajasthan, India
| | - Amarnath Jena
- PET Suite (Indraprastha Apollo Hospitals and House of Diagnostics), Department of Molecular Imaging and Nuclear Medicine, Indraprastha Apollo Hospitals, New Delhi, India
- Department of Physics, Vivekananda Global University, Jaipur, Rajasthan, India
| | - Prerana Rana
- PET Suite (Indraprastha Apollo Hospitals and House of Diagnostics), Department of Molecular Imaging and Nuclear Medicine, Indraprastha Apollo Hospitals, New Delhi, India
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20
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Li Z, Mathew M, Syed AB, Feng L, Brunsing R, Pauly JM, Vasanawala SS. Rapid fat-water separated T 1 mapping using a single-shot radial inversion-recovery spoiled gradient recalled pulse sequence. NMR IN BIOMEDICINE 2022; 35:e4803. [PMID: 35891586 DOI: 10.1002/nbm.4803] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 07/21/2022] [Accepted: 07/23/2022] [Indexed: 05/04/2023]
Abstract
T1 mapping is increasingly used in clinical practice and research studies. With limited scan time, existing techniques often have limited spatial resolution, contrast resolution and slice coverage. High fat concentrations yield complex errors in Look-Locker T1 methods. In this study, a dual-echo 2D radial inversion-recovery T1 (DEradIR-T1) technique was developed for fast fat-water separated T1 mapping. The DEradIR-T1 technique was tested in phantoms, 5 volunteers and 28 patients using a 3 T clinical MRI scanner. In our study, simulations were performed to analyze the composite (fat + water) and water-only T1 under different echo times (TE). In standardized phantoms, an inversion-recovery spin echo (IR-SE) sequence with and without fat saturation pulses served as a T1 reference. Parameter mapping with DEradIR-T1 was also assessed in vivo, and values were compared with modified Look-Locker inversion recovery (MOLLI). Bland-Altman analysis and two-tailed paired t-tests were used to compare the parameter maps from DEradIR-T1 with the references. Simulations of the composite and water-only T1 under different TE values and levels of fat matched the in vivo studies. T1 maps from DEradIR-T1 on a NIST phantom (Pcomp = 0.97) and a Calimetrix fat-water phantom (Pwater = 0.56) matched with the references. In vivo T1 was compared with that of MOLLI: R comp 2 = 0.77 ; R water 2 = 0.72 . In this work, intravoxel fat is found to have a variable, echo-time-dependent effect on measured T1 values, and this effect may be mitigated using the proposed DRradIR-T1.
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Affiliation(s)
- Zhitao Li
- Department of Radiology, Stanford University, Stanford, California, USA
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
| | - Manoj Mathew
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Ali B Syed
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ryan Brunsing
- Department of Radiology, Stanford University, Stanford, California, USA
| | - John M Pauly
- Department of Electrical Engineering, Stanford University, Stanford, California, USA
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21
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Tomer O, Barazany D, Baratz Z, Tsarfaty G, Assaf Y. In vivo measurements of lamination patterns in the human cortex. Hum Brain Mapp 2022; 43:2861-2868. [PMID: 35274794 PMCID: PMC9120563 DOI: 10.1002/hbm.25821] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 02/06/2022] [Accepted: 02/16/2022] [Indexed: 11/22/2022] Open
Abstract
The laminar composition of the cerebral cortex is tightly connected to the development and connectivity of the brain, as well as to function and pathology. Although most of the research on the cortical layers is done with the aid of ex vivo histology, there have been recent attempts to use magnetic resonance imaging (MRI) with potential in vivo applications. However, the high-resolution MRI technology and protocols required for such studies are neither common nor practical. In this article, we present a clinically feasible method for assessing the laminar properties of the human cortex using standard pulse sequence available on any common MRI scanner. Using a series of low-resolution inversion recovery (IR) MRI scans allows us to calculate multiple T1 relaxation time constants for each voxel. Based on the whole-brain T1 -distribution, we identify six different gray matter T1 populations and their variation across the cortex. Based on this, we show age-related differences in these population and demonstrate that this method is able to capture the difference in laminar composition across varying brain areas. We also provide comparison to ex vivo high-resolution MRI scans. We show that this method is feasible for the estimation of layer variability across large population cohorts, which can lead to research into the links between the cortical layers and function, behavior and pathologies that was heretofore unexplorable.
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Affiliation(s)
- Omri Tomer
- Sagol School of NeuroscienceTel Aviv UniversityTel AvivIsrael
| | - Daniel Barazany
- The Strauss Center for Computational NeuroimagingTel Aviv UniversityTel AvivIsrael
| | - Zvi Baratz
- Sagol School of NeuroscienceTel Aviv UniversityTel AvivIsrael
| | - Galia Tsarfaty
- Division of Diagnostic Imaging, Sheba Medical Center, Tel‐Hashomer, Affiliated to the Faculty of MedicineTel Aviv UniversityTel AvivIsrael
| | - Yaniv Assaf
- Sagol School of NeuroscienceTel Aviv UniversityTel AvivIsrael
- The Strauss Center for Computational NeuroimagingTel Aviv UniversityTel AvivIsrael
- School of Neurobiology, Biochemistry and Biophysics, Faculty of Life ScienceTel Aviv UniversityTel AvivIsrael
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22
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Hwang KP, Fujita S. Synthetic MR: Physical principles, clinical implementation, and new developments. Med Phys 2022; 49:4861-4874. [PMID: 35535442 DOI: 10.1002/mp.15686] [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: 09/30/2021] [Revised: 04/05/2022] [Accepted: 04/08/2022] [Indexed: 11/07/2022] Open
Abstract
Current clinical MR imaging practices rely on the qualitative assessment of images for diagnosis and treatment planning. While contrast in MR images is dependent on the spin parameters of the imaged tissue, pixel values on MR images are relative and are not scaled to represent any tissue properties. Synthetic MR is a fully featured imaging workflow consisting of efficient multiparameter mapping acquisition, synthetic image generation, and volume quantitation of brain tissues. As the application becomes more widely available on multiple vendors and scanner platforms, it has also gained widespread adoption as clinicians begin to recognize the benefits of rapid quantitation. This review will provide details about the sequence with a focus on the physical principles behind its relaxometry mechanisms. It will present an overview of the products in their current form and some potential issues when implementing it in the clinic. It will conclude by highlighting some recent advances of the technique, including a 3D mapping method and its associated applications. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Ken-Pin Hwang
- Department of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, 77030
| | - Shohei Fujita
- Department of Radiology, Graduate School of Medicine, The University of Tokyo.,Department of Radiology, Juntendo University, Tokyo, Japan
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23
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Feng L, Ma D, Liu F. Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends. NMR IN BIOMEDICINE 2022; 35:e4416. [PMID: 33063400 PMCID: PMC8046845 DOI: 10.1002/nbm.4416] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 08/25/2020] [Accepted: 09/09/2020] [Indexed: 05/08/2023]
Abstract
Quantitative mapping of MR tissue parameters such as the spin-lattice relaxation time (T1 ), the spin-spin relaxation time (T2 ), and the spin-lattice relaxation in the rotating frame (T1ρ ), referred to as MR relaxometry in general, has demonstrated improved assessment in a wide range of clinical applications. Compared with conventional contrast-weighted (eg T1 -, T2 -, or T1ρ -weighted) MRI, MR relaxometry provides increased sensitivity to pathologies and delivers important information that can be more specific to tissue composition and microenvironment. The rise of deep learning in the past several years has been revolutionizing many aspects of MRI research, including image reconstruction, image analysis, and disease diagnosis and prognosis. Although deep learning has also shown great potential for MR relaxometry and quantitative MRI in general, this research direction has been much less explored to date. The goal of this paper is to discuss the applications of deep learning for rapid MR relaxometry and to review emerging deep-learning-based techniques that can be applied to improve MR relaxometry in terms of imaging speed, image quality, and quantification robustness. The paper is comprised of an introduction and four more sections. Section 2 describes a summary of the imaging models of quantitative MR relaxometry. In Section 3, we review existing "classical" methods for accelerating MR relaxometry, including state-of-the-art spatiotemporal acceleration techniques, model-based reconstruction methods, and efficient parameter generation approaches. Section 4 then presents how deep learning can be used to improve MR relaxometry and how it is linked to conventional techniques. The final section concludes the review by discussing the promise and existing challenges of deep learning for rapid MR relaxometry and potential solutions to address these challenges.
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Affiliation(s)
- Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Dan Ma
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Fang Liu
- Department of Radiology, Massachusetts General Hospital, Harvard University, Boston, Massachusetts
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24
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Ye Y, Lyu J, Hu Y, Zhang Z, Xu J, Zhang W. MULTI-parametric MR imaging with fLEXible design (MULTIPLEX). Magn Reson Med 2022; 87:658-673. [PMID: 34464011 DOI: 10.1002/mrm.28999] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 08/17/2021] [Accepted: 08/18/2021] [Indexed: 11/10/2022]
Abstract
PURPOSE To introduce a gradient echo (GRE) -based method, namely MULTIPLEX, for single-scan 3D multi-parametric MRI with high resolution, signal-to-noise ratio (SNR), accuracy, efficiency, and acquisition flexibility. THEORY With a comprehensive design with dual-repetition time (TR), dual flip angle (FA), multi-echo, and optional flow modulation features, the MULTIPLEX signals contain information on radiofrequency (RF) B1t fields, proton density, T1 , susceptibility and blood flows, facilitating multiple qualitative images and parametric maps. METHODS MULTIPLEX was evaluated on system phantom and human brains, via visual inspection for image contrasts and quality or quantitative evaluation via simulation, phantom scans and literature comparison. Region-of-interest (ROI) analysis was performed on parametric maps of the system phantom and brain scans, extracting the mean and SD of the T1 , T2∗ , proton density (PD), and/or quantitative susceptibility mapping (QSM) values for comparison with reference values or literature. RESULTS One MULTIPLEX scan offers multiple sets of images, including but not limited to: composited PDW/T1 W/ T2∗ W, aT1 W, SWI, MRA (optional), B1t map, T1 map, T2∗ / R2∗ maps, PD map, and QSM. The quantitative error of phantom T1 , T2∗ and PD mapping were <5%, and those in brain scans were in good agreement with literature. MULTIPLEX scan times for high resolution (0.68 × 0.68 × 2 mm3 ) whole brain coverage were about 7.5 min, while processing times were <1 min. With flow modulation, additional MRA images can be obtained without affecting the quality or accuracy of other images. CONCLUSION The proposed MUTLIPLEX method possesses great potential for multi-parametric MR imaging.
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Affiliation(s)
| | | | - Yichen Hu
- UIH America, Inc., Houston, Texas, USA
| | | | - Jian Xu
- UIH America, Inc., Houston, Texas, USA
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25
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Kolind S, Abel S, Taylor C, Tam R, Laule C, Li DK, Garren H, Gaetano L, Bernasconi C, Clayton D, Vavasour I, Traboulsee A. Myelin water imaging in relapsing multiple sclerosis treated with ocrelizumab and interferon beta-1a. NEUROIMAGE: CLINICAL 2022; 35:103109. [PMID: 35878575 PMCID: PMC9421448 DOI: 10.1016/j.nicl.2022.103109] [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: 04/22/2022] [Revised: 06/27/2022] [Accepted: 07/10/2022] [Indexed: 11/26/2022] Open
Abstract
2-Year change in MS myelin water fraction favored ocrelizumab over interferon. Matched healthy controls showed no change in myelin water fraction over 2 years. Ocrelizumab appears to protect against demyelination in MS white matter and lesions.
Background Myelin water imaging is a magnetic resonance imaging (MRI) technique that quantifies myelin damage and repair in multiple sclerosis (MS) via the myelin water fraction (MWF). Objective In this substudy of a phase 3 therapeutic trial, OPERA II, MWF was assessed in relapsing MS participants assigned to interferon beta-1a (IFNb-1a) or ocrelizumab (OCR) during a two-year double-blind period (DBP) followed by a two-year open label extension (OLE) with ocrelizumab treatment. Methods MWF in normal appearing white matter (NAWM), including both whole brain NAWM and 5 white matter structures, and chronic lesions, was assessed in 29 OCR and 26 IFNb-1a treated participants at weeks 0, 24, 48 and 96 (DBP), and weeks 144 and 192 (OLE), and in white matter for 23 healthy control participants at weeks 0, 48 and 96. Results Linear mixed-effects models of data from baseline to week 96 showed a difference in the change in MWF over time favouring ocrelizumab in all NAWM regions. At week 192, lesion MWF was lower for participants originally randomised to IFNb-1a compared to those originally randomised to OCR. Controls showed no change in MWF over 96 weeks in any region. Conclusion Ocrelizumab appears to protect against demyelination in MS NAWM and chronic lesions and may allow for a more permissive micro environment for remyelination to occur in focal and diffusely damaged tissue.
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Using quantitative MRI to study brain responses to immune challenge with interferon-α. Brain Behav Immun Health 2021; 18:100376. [PMID: 34746879 PMCID: PMC8554453 DOI: 10.1016/j.bbih.2021.100376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 10/18/2021] [Indexed: 11/20/2022] Open
Abstract
Inflammatory processes in the Central Nervous System (CNS) have been proposed to mediate the association between peripheral inflammation and the development of psychiatric disorders, but we currently lack sensitive measures of CNS inflammation for human studies in vivo. Here we used quantitative MRI (qMRI) to explore the in vivo central response to a peripheral immune challenge in healthy humans, and we assessed whether changes in quantitative relaxometry MRI parameters were associated with changes in peripheral inflammation. Quantitative relaxation times (T1 & T2) and Proton Density (PD) were measured in n = 6 healthy males (mean age = 30.5 ± 6.8 years) in two MRI assessments collected before and 24 hours after a subcutaneous injection of the proinflammatory cytokine and immune activator, interferon-alpha (IFN-α). Serum levels of immune markers and markers of blood-brain barrier integrity (S100B) were also measured before and after the injection. Region of interest and histogram-based analyses (optimized for the small sample size) showed a statistically significant increase of both T1 (t(5) = 3.78, p = 0.013) and PD (t(5) = 2.91, p = 0.033) parameters in the bilateral hippocampus after IFN-α administration. T1 peak values in bilateral hippocampus were positively correlated with serum Tumour Necrosis Factor-alpha levels at 24 h after the injection, when this cytokine peaked (Spearman's rho = 0.67, p = 0.018) and negatively correlated with S100B levels (Spearman's rho = -0.826, p = 0.001). Our data suggest that peripheral administration of IFN-α produces acute changes in brain qMRI which might indicate a brain immune response. This is supported by the association of such changes with low-grade peripheral inflammation.
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Moya-Sáez E, Peña-Nogales Ó, Luis-García RD, Alberola-López C. A deep learning approach for synthetic MRI based on two routine sequences and training with synthetic data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 210:106371. [PMID: 34525411 DOI: 10.1016/j.cmpb.2021.106371] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 08/18/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Synthetic magnetic resonance imaging (MRI) is a low cost procedure that serves as a bridge between qualitative and quantitative MRI. However, the proposed methods require very specific sequences or private protocols which have scarcely found integration in clinical scanners. We propose a learning-based approach to compute T1, T2, and PD parametric maps from only a pair of T1- and T2-weighted images customarily acquired in the clinical routine. METHODS Our approach is based on a convolutional neural network (CNN) trained with synthetic data; specifically, a synthetic dataset with 120 volumes was constructed from the anatomical brain model of the BrainWeb tool and served as the training set. The CNN learns an end-to-end mapping function to transform the input T1- and T2-weighted images to their underlying T1, T2, and PD parametric maps. Then, conventional weighted images unseen by the network are analytically synthesized from the parametric maps. The network can be fine tuned with a small database of actual weighted images and maps for better performance. RESULTS This approach is able to accurately compute parametric maps from synthetic brain data achieving normalized squared error values predominantly below 1%. It also yields realistic parametric maps from actual MR brain acquisitions with T1, T2, and PD values in the range of the literature and with correlation values above 0.95 compared to the T1 and T2 maps obtained from relaxometry sequences. Further, the synthesized weighted images are visually realistic; the mean square error values are always below 9% and the structural similarity index is usually above 0.90. Network fine tuning with actual maps improves performance, while training exclusively with a small database of actual maps shows a performance degradation. CONCLUSIONS These results show that our approach is able to provide realistic parametric maps and weighted images out of a CNN that (a) is trained with a synthetic dataset and (b) needs only two inputs, which are in turn obtained from a common full-brain acquisition that takes less than 8 min of scan time. Although a fine tuning with actual maps improves performance, synthetic data is crucial to reach acceptable performance levels. Hence, we show the utility of our approach for both quantitative MRI in clinical viable times and for the synthesis of additional weighted images to those actually acquired.
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Affiliation(s)
- Elisa Moya-Sáez
- Laboratorio de Procesado de Imagen, Universidad de Valladolid, Valladolid, Spain. http://www.lpi.tel.uva.es
| | - Óscar Peña-Nogales
- Laboratorio de Procesado de Imagen, Universidad de Valladolid, Valladolid, Spain
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Droby A, Thaler A, Giladi N, Hutchison RM, Mirelman A, Ben Bashat D, Artzi M. Whole brain and deep gray matter structure segmentation: Quantitative comparison between MPRAGE and MP2RAGE sequences. PLoS One 2021; 16:e0254597. [PMID: 34358242 PMCID: PMC8345829 DOI: 10.1371/journal.pone.0254597] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 06/29/2021] [Indexed: 11/29/2022] Open
Abstract
Objective T1-weighted MRI images are commonly used for volumetric assessment of brain structures. Magnetization prepared 2 rapid gradient echo (MP2RAGE) sequence offers superior gray (GM) and white matter (WM) contrast. This study aimed to quantitatively assess the agreement of whole brain tissue and deep GM (DGM) volumes obtained from MP2RAGE compared to the widely used MP-RAGE sequence. Methods Twenty-nine healthy participants were included in this study. All subjects underwent a 3T MRI scan acquiring high-resolution 3D MP-RAGE and MP2RAGE images. Twelve participants were re-scanned after one year. The whole brain, as well as DGM segmentation, was performed using CAT12, volBrain, and FSL-FAST automatic segmentation tools based on the acquired images. Finally, contrast-to-noise ratio between WM and GM (CNRWG), the agreement between the obtained tissue volumes, as well as scan-rescan variability of both sequences were explored. Results Significantly higher CNRWG was detected in MP2RAGE vs. MP-RAGE (Mean ± SD = 0.97 ± 0.04 vs. 0.8 ± 0.1 respectively; p<0.0001). Significantly higher total brain GM, and lower cerebrospinal fluid volumes were obtained from MP2RAGE vs. MP-RAGE based on all segmentation methods (p<0.05 in all cases). Whole-brain voxel-wise comparisons revealed higher GM tissue probability in the thalamus, putamen, caudate, lingual gyrus, and precentral gyrus based on MP2RAGE compared with MP-RAGE. Moreover, significantly higher WM probability was observed in the cerebellum, corpus callosum, and frontal-and-temporal regions in MP2RAGE vs. MP-RAGE. Finally, MP2RAGE showed a higher mean percentage of change in total brain GM compared to MP-RAGE. On the other hand, MP-RAGE demonstrated a higher overtime percentage of change in WM and DGM volumes compared to MP2RAGE. Conclusions Due to its higher CNR, MP2RAGE resulted in reproducible brain tissue segmentation, and thus is a recommended method for volumetric imaging biomarkers for the monitoring of neurological diseases.
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Affiliation(s)
- Amgad Droby
- Laboratory for Early Markers of Neurodegeneration (LEMON), Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- * E-mail:
| | - Avner Thaler
- Laboratory for Early Markers of Neurodegeneration (LEMON), Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Nir Giladi
- Laboratory for Early Markers of Neurodegeneration (LEMON), Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | | | - Anat Mirelman
- Laboratory for Early Markers of Neurodegeneration (LEMON), Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Dafna Ben Bashat
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Moran Artzi
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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Puddu C, Rao M, Xu X, Deppe MH, Collier G, Maunder A, Chan HF, De Zanche N, Robb F, Wild JM. An asymmetrical whole-body birdcage RF coil without RF shield for hyperpolarized 129 Xe lung MR imaging at 1.5 T. Magn Reson Med 2021; 86:3373-3381. [PMID: 34268802 DOI: 10.1002/mrm.28915] [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: 12/23/2020] [Revised: 06/15/2021] [Accepted: 06/15/2021] [Indexed: 11/10/2022]
Abstract
PURPOSE This study describes the development and testing of an asymmetrical xenon-129 (129 Xe) birdcage radiofrequency (RF) coil for 129 Xe lung ventilation imaging at 1.5 Tesla, which allows proton (1 H) system body coil transmit-receive functionality. METHODS The 129 Xe RF coil is a whole-body asymmetrical elliptical birdcage constructed without an outer RF shield to enable 1 H imaging. B 1 + field homogeneity and flip angle mapping of the 129 Xe birdcage RF coil and 1 H system body RF coil with the 129 Xe RF coil in situ were evaluated in the MR scanner. The functionality of the 129 Xe birdcage RF coil was demonstrated through hyperpolarized 129 Xe lung ventilation imaging with the birdcage in both transceiver configuration and transmit-only configuration when combined with an 8-channel 129 Xe receive-only RF coil array. The functionality of 1 H system body coil with the 129 Xe RF coil in situ was demonstrated by acquiring coregistered 1 H lung anatomical MR images. RESULTS The asymmetrical birdcage produced a homogeneous B 1 + field (±10%) in agreement with electromagnetic simulations. Simulations indicated an optimal detuning configuration with 4 diodes. The obtained g-factor of 1.4 for acceleration factor of R = 2 indicates optimal array configuration. Coregistered 1 H anatomical images from the system body coil along with 129 Xe lung images demonstrated concurrent and compatible arrangement of the RF coils. CONCLUSION A large asymmetrical birdcage for homogenous B 1 + transmission with high sensitivity reception for 129 Xe lung MRI at 1.5 Tesla has been demonstrated. The unshielded asymmetrical birdcage design enables 1 H structural lung MR imaging in the same exam.
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Affiliation(s)
- Claudio Puddu
- POLARIS, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Madhwesha Rao
- POLARIS, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Xiaojun Xu
- POLARIS, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Martin H Deppe
- POLARIS, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Guilhem Collier
- POLARIS, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Adam Maunder
- POLARIS, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Ho-Fung Chan
- POLARIS, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
| | - Nicola De Zanche
- Department of Medical Physics, Cross Cancer Institute and University of Alberta, Alberta, Canada
| | - Fraser Robb
- POLARIS, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.,GE Healthcare, Aurora, Ohio, USA
| | - Jim M Wild
- POLARIS, Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom
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Imaging neurovascular, endothelial and structural integrity in preparation to treat small vessel diseases. The INVESTIGATE-SVDs study protocol. Part of the SVDs@Target project. CEREBRAL CIRCULATION - COGNITION AND BEHAVIOR 2021; 2:100020. [PMID: 36324725 PMCID: PMC9616332 DOI: 10.1016/j.cccb.2021.100020] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 04/25/2021] [Accepted: 06/20/2021] [Indexed: 12/30/2022]
Abstract
Background Sporadic cerebral small vessel disease (SVD) and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) share clinical and neuroimaging features and possibly vascular dysfunction(s). However few studies have included both conditions, assessed more than one vascular dysfunction simultaneously, or included more than one centre. The INVESTIGATE-SVDs study will assess several cerebrovascular dysfunctions with MRI in participants with sporadic SVD or CADASIL at three European centres. Methods We will recruit participants with sporadic SVDs (ischaemic stroke or vascular cognitive impairment) and CADASIL in Edinburgh, Maastricht and Munich. We will perform detailed clinical and neuropsychological phenotyping of the participants, and neuroimaging including structural MRI, cerebrovascular reactivity MRI (CVR: using carbon dioxide challenge), phase contrast MRI (arterial, venous and CSF flow and pulsatility), dynamic contrast-enhanced MRI (blood brain barrier (BBB) leakage) and multishell diffusion imaging. Participants will measure their blood pressure (BP) and its variability over seven days using a telemetric device. Discussion INVESTIGATE-SVDs will assess the relationships of BBB integrity, CVR, pulsatility and CSF flow in sporadic SVD and CADASIL using a multisite, multimodal MRI protocol. We aim to establish associations between these measures of vascular function, risk factors particularly BP and its variability, and brain parenchymal lesions in these two SVD phenotypes. Additionally we will test feasibility of complex multisite MRI, provide reliable intermediary outcome measures and sample size estimates for future trials.
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Key Words
- BBB, blood brain barrier
- BOLD, blood oxygen level dependent
- BP, blood pressure
- BPv, blood pressure variability
- Blood-brain barrier permeability
- CADASIL
- CADASIL, cerebral autosomal dominant arteriopathy with leukoencephalopathy and subcortical infarcts
- CBF, cerebral blood flow
- CERAD+, consortium to establish a disease registry for Alzheimer's disease plus battery
- CO2, carbon dioxide
- CSF, cerebrospinal fluid
- CVR, cerebrovascular reactivity
- Cerebral small vessel disease
- Cerebrovascular reactivity
- DCE, dynamic contrast enhanced
- EtCO2, end-tidal carbon dioxide
- GM, grey matter
- MMSE, mini-mental state examination
- MRI
- MoCA, Montreal cognitive exam
- NIHSS, national institute for health stroke scale
- PI, pulsatility index
- PVS, perivascular space
- RSSI, recent small subcortical infarct
- SVDs, small vessel diseases
- WM, white matter
- WMH, white matter hyperintensity
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Kinh Do R, Reyngold M, Paudyal R, Oh JH, Konar AS, LoCastro E, Goodman KA, Shukla-Dave A. Diffusion-Weighted and Dynamic Contrast-Enhanced MRI Derived Imaging Metrics for Stereotactic Body Radiotherapy of Pancreatic Ductal Adenocarcinoma: Preliminary Findings. ACTA ACUST UNITED AC 2021; 6:261-271. [PMID: 32548304 PMCID: PMC7289241 DOI: 10.18383/j.tom.2020.00015] [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] [Indexed: 01/26/2023]
Abstract
We aimed to assess longitudinal changes in quantitative imaging metric values obtained from diffusion-weighted (DW-) and dynamic contrast-enhanced magnetic resonance imaging (DCE)-MRI at pre-treatment (TX[0]), immediately after the first fraction of stereotactic body radiotherapy (D1-TX[1]), and 6 weeks post-TX (Post-TX[2]) in patients with pancreatic ductal adenocarcinoma. Ten enrolled patients (n = 10) underwent DW- and DCE-MRI examinations on a 3.0 T scanner. The apparent diffusion coefficient, ADC (mm2/s), was derived from DW imaging data using a monoexponential model. The tissue relaxation rate, R 1t, time-course data were fitted with a shutter-speed model, which provides estimates of the volume transfer constant, K trans (min-1), extravascular extracellular volume fraction, ve , and mean lifetime of intracellular water protons, τ i (seconds). Wilcoxon rank-sum test compared the mean values, standard deviation, skewness, kurtosis, and relative percentage (r, %) changes (Δ) in ADC, K trans, ve , and τ i values between the magnetic resonance examinations. rADCΔ2-0 values were significantly greater than rADCΔ1-0 values (P = .009). rK trans Δ2-0 values were significantly lower than rK trans Δ1-0 values (P = .048). rve Δ2-1 and rveΔ2-0 values were significantly different (P = .016). rτ i Δ2-1 values were significantly lower than rτ i Δ2-0 values (P = .008). For group comparison, the pre-TX mean and kurtosis of ADC (P = .18 and P = .14), skewness and kurtosis of K trans values (P = .14 for both) showed a leaning toward significant difference between patients who experienced local control (n = 2) and failed early (n = 4). DW- and DCE-MRI-derived quantitative metrics could be useful biomarkers to evaluate longitudinal changes to stereotactic body radiotherapy in patients with pancreatic ductal adenocarcinoma.
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Affiliation(s)
| | | | - Ramesh Paudyal
- Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY; and
| | - Jung Hun Oh
- Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY; and
| | | | - Eve LoCastro
- Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY; and
| | - Karyn A Goodman
- Tisch Cancer Institute at Mount Sinai Hospital, New York, NY
| | - Amita Shukla-Dave
- Departments of Radiology.,Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY; and
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Fritz FJ, Poser BA, Roebroeck A. MESMERISED: Super-accelerating T 1 relaxometry and diffusion MRI with STEAM at 7 T for quantitative multi-contrast and diffusion imaging. Neuroimage 2021; 239:118285. [PMID: 34147632 DOI: 10.1016/j.neuroimage.2021.118285] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 06/14/2021] [Accepted: 06/16/2021] [Indexed: 12/17/2022] Open
Abstract
There is an increasing interest in quantitative imaging of T1, T2 and diffusion contrast in the brain due to greater robustness against bias fields and artifacts, as well as better biophysical interpretability in terms of microstructure. However, acquisition time constraints are a challenge, particularly when multiple quantitative contrasts are desired and when extensive sampling of diffusion directions, high b-values or long diffusion times are needed for multi-compartment microstructure modeling. Although ultra-high fields of 7 T and above have desirable properties for many MR modalities, the shortening T2 and the high specific absorption rate (SAR) of inversion and refocusing pulses bring great challenges to quantitative T1, T2 and diffusion imaging. Here, we present the MESMERISED sequence (Multiplexed Echo Shifted Multiband Excited and Recalled Imaging of STEAM Encoded Diffusion). MESMERISED removes the dead time in Stimulated Echo Acquisition Mode (STEAM) imaging by an echo-shifting mechanism. The echo-shift (ES) factor is independent of multiband (MB) acceleration and allows for very high multiplicative (ESxMB) acceleration factors, particularly under moderate and long mixing times. This results in super-acceleration and high time efficiency at 7 T for quantitative T1 and diffusion imaging, while also retaining the capacity to perform quantitative T2 and B1 mapping. We demonstrate the super-acceleration of MESMERISED for whole-brain T1 relaxometry with total acceleration factors up to 36 at 1.8 mm isotropic resolution, and up to 54 at 1.25 mm resolution qT1 imaging, corresponding to a 6x and 9x speedup, respectively, compared to MB-only accelerated acquisitions. We then demonstrate highly efficient diffusion MRI with high b-values and long diffusion times in two separate cases. First, we show that super-accelerated multi-shell diffusion acquisitions with 370 whole-brain diffusion volumes over 8 b-value shells up to b = 7000 s/mm2 can be generated at 2 mm isotropic in under 8 minutes, a data rate of almost a volume per second, or at 1.8 mm isotropic in under 11 minutes, achieving up to 3.4x speedup compared to MB-only. A comparison of b = 7000 s/mm2 MESMERISED against standard MB pulsed gradient spin echo (PGSE) diffusion imaging shows 70% higher SNR efficiency and greater effectiveness in supporting complex diffusion signal modeling. Second, we demonstrate time-efficient sampling of different diffusion times with 1.8 mm isotropic diffusion data acquired at four diffusion times up to 290 ms, which supports both Diffusion Tensor Imaging (DTI) and Diffusion Kurtosis Imaging (DKI) at each diffusion time. Finally, we demonstrate how adding quantitative T2 and B1+ mapping to super-accelerated qT1 and diffusion imaging enables efficient quantitative multi-contrast mapping with the same MESMERISED sequence and the same readout train. MESMERISED extends possibilities to efficiently probe T1, T2 and diffusion contrast for multi-component modeling of tissue microstructure.
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Affiliation(s)
- F J Fritz
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands; Institut für Systemische Neurowissenschaften, Zentrum für Experimentelle Medizin, Universitätklinikum Hamburg-Eppendorf (UKE), Hamburg, Deutschland
| | - B A Poser
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - A Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands.
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Liu F, Kijowski R, El Fakhri G, Feng L. Magnetic resonance parameter mapping using model-guided self-supervised deep learning. Magn Reson Med 2021; 85:3211-3226. [PMID: 33464652 PMCID: PMC9185837 DOI: 10.1002/mrm.28659] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 11/15/2020] [Accepted: 12/07/2020] [Indexed: 12/25/2022]
Abstract
PURPOSE To develop a model-guided self-supervised deep learning MRI reconstruction framework called reference-free latent map extraction (RELAX) for rapid quantitative MR parameter mapping. METHODS Two physical models are incorporated for network training in RELAX, including the inherent MR imaging model and a quantitative model that is used to fit parameters in quantitative MRI. By enforcing these physical model constraints, RELAX eliminates the need for full sampled reference data sets that are required in standard supervised learning. Meanwhile, RELAX also enables direct reconstruction of corresponding MR parameter maps from undersampled k-space. Generic sparsity constraints used in conventional iterative reconstruction, such as the total variation constraint, can be additionally included in the RELAX framework to improve reconstruction quality. The performance of RELAX was tested for accelerated T1 and T2 mapping in both simulated and actually acquired MRI data sets and was compared with supervised learning and conventional constrained reconstruction for suppressing noise and/or undersampling-induced artifacts. RESULTS In the simulated data sets, RELAX generated good T1 /T2 maps in the presence of noise and/or undersampling artifacts, comparable to artifact/noise-free ground truth. The inclusion of a spatial total variation constraint helps improve image quality. For the in vivo T1 /T2 mapping data sets, RELAX achieved superior reconstruction quality compared with conventional iterative reconstruction, and similar reconstruction performance to supervised deep learning reconstruction. CONCLUSION This work has demonstrated the initial feasibility of rapid quantitative MR parameter mapping based on self-supervised deep learning. The RELAX framework may also be further extended to other quantitative MRI applications by incorporating corresponding quantitative imaging models.
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Affiliation(s)
- Fang Liu
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Richard Kijowski
- Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Georges El Fakhri
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Li Feng
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, USA
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Zhu Z, Lebel RM, Bliesener Y, Acharya J, Frayne R, Nayak KS. Sparse precontrast T 1 mapping for high-resolution whole-brain DCE-MRI. Magn Reson Med 2021; 86:2234-2249. [PMID: 34036658 PMCID: PMC8362109 DOI: 10.1002/mrm.28849] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2021] [Revised: 04/26/2021] [Accepted: 04/28/2021] [Indexed: 12/18/2022]
Abstract
PURPOSE To develop and evaluate an efficient precontrast T1 mapping technique suitable for quantitative high-resolution whole-brain dynamic contrast-enhanced-magnetic resonance imaging (DCE-MRI). METHODS Variable flip angle (VFA) T1 mapping was considered that provides 1 × 1 × 2 mm3 resolution to match a recent high-resolution whole-brain DCE-MRI protocol. Seven FAs were logarithmically spaced from 1.5° to 15°. T1 and M0 maps were estimated using model-based reconstruction. This approach was evaluated using an anatomically realistic brain tumor digital reference object (DRO) with noise-mimicking 3T neuroimaging and fully sampled data acquired from one healthy volunteer. Methods were also applied on fourfold prospectively undersampled VFA data from 13 patients with high-grade gliomas. RESULTS T1 -mapping precision decreased with undersampling factor R, althoughwhereas bias remained small before a critical R. In the noiseless DRO, T1 bias was <25 ms in white matter (WM) and <11 ms in brain tumor (BT). T1 standard deviation (SD) was <119.5 ms in WM (coefficient of variation [COV] ~11.0%) and <253.2 ms in BT (COV ~12.7%). In the noisy DRO, T1 bias was <50 ms in WM and <30 ms in BT. For R ≤ 10, T1 SD was <107.1 ms in WM (COV ~9.9%) and <240.9 ms in BT (COV ~12.1%). In the healthy subject, T1 bias was <30 ms for R ≤ 16. At R = 4, T1 SD was 171.4 ms (COV ~13.0%). In the prospective brain tumor study, T1 values were consistent with literature values in WM and BT. CONCLUSION High-resolution whole-brain VFA T1 mapping is feasible with sparse sampling, supporting its use for quantitative DCE-MRI.
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Affiliation(s)
- Zhibo Zhu
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - R Marc Lebel
- General Electric Healthcare, Calgary, Alberta, Canada.,Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Yannick Bliesener
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Jay Acharya
- Department of Radiology, University of Southern California, Los Angeles, California, USA
| | - Richard Frayne
- Radiology and Clinical Neuroscience, Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada.,Seaman Family MR Research Centre, Foothills Medical Centre, Calgary, Alberta, Canada
| | - Krishna S Nayak
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.,Department of Radiology, University of Southern California, Los Angeles, California, USA
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35
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Manning C, Stringer M, Dickie B, Clancy U, Valdés Hernandez MC, Wiseman SJ, Garcia DJ, Sakka E, Backes WH, Ingrisch M, Chappell F, Doubal F, Buckley C, Parkes LM, Parker GJM, Marshall I, Wardlaw JM, Thrippleton MJ. Sources of systematic error in DCE-MRI estimation of low-level blood-brain barrier leakage. Magn Reson Med 2021; 86:1888-1903. [PMID: 34002894 DOI: 10.1002/mrm.28833] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 03/19/2021] [Accepted: 04/16/2021] [Indexed: 12/29/2022]
Abstract
PURPOSE Dynamic contrast-enhanced (DCE) -MRI with Patlak model analysis is increasingly used to quantify low-level blood-brain barrier (BBB) leakage in studies of pathophysiology. We aimed to investigate systematic errors due to physiological, experimental, and modeling factors influencing quantification of the permeability-surface area product PS and blood plasma volume vp , and to propose modifications to reduce the errors so that subtle differences in BBB permeability can be accurately measured. METHODS Simulations were performed to predict the effects of potential sources of systematic error on conventional PS and vp quantification: restricted BBB water exchange, reduced cerebral blood flow, arterial input function (AIF) delay and B 1 + error. The impact of targeted modifications to the acquisition and processing were evaluated, including: assumption of fast versus no BBB water exchange, bolus versus slow injection of contrast agent, exclusion of early data from model fitting and B 1 + correction. The optimal protocol was applied in a cohort of recent mild ischaemic stroke patients. RESULTS Simulation results demonstrated substantial systematic errors due to the factors investigated (absolute PS error ≤ 4.48 × 10-4 min-1 ). However, these were reduced (≤0.56 × 10-4 min-1 ) by applying modifications to the acquisition and processing pipeline. Processing modifications also had substantial effects on in-vivo normal-appearing white matter PS estimation (absolute change ≤ 0.45 × 10-4 min-1 ). CONCLUSION Measuring subtle BBB leakage with DCE-MRI presents unique challenges and is affected by several confounds that should be considered when acquiring or interpreting such data. The evaluated modifications should improve accuracy in studies of neurodegenerative diseases involving subtle BBB breakdown.
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Affiliation(s)
- Cameron Manning
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.,UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Michael Stringer
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.,UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Ben Dickie
- Division of Neuroscience and Experimental Psychology, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Una Clancy
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.,UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Maria C Valdés Hernandez
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.,UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Stewart J Wiseman
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.,UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Daniela Jaime Garcia
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.,UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Eleni Sakka
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.,UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Walter H Backes
- Department of Radiology & Nuclear Medicine, School for Mental Health & Neuroscience and School for Cardiovascular Diseases, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Michael Ingrisch
- Department of Radiology, Ludwig-Maximilians-University Hospital Munich, Munich, Germany
| | - Francesca Chappell
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.,UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Fergus Doubal
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.,UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom
| | | | - Laura M Parkes
- Division of Neuroscience and Experimental Psychology, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | - Geoff J M Parker
- Centre for Medical Image Computing and Department of Neuroinflammation, UCL, London, United Kingdom
| | - Ian Marshall
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.,UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom.,Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Joanna M Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.,UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom.,Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Michael J Thrippleton
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom.,UK Dementia Research Institute, University of Edinburgh, Edinburgh, United Kingdom.,Edinburgh Imaging, University of Edinburgh, Edinburgh, United Kingdom
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36
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Rowley CD, Campbell JSW, Wu Z, Leppert IR, Rudko DA, Pike GB, Tardif CL. A model-based framework for correcting B 1 + inhomogeneity effects in magnetization transfer saturation and inhomogeneous magnetization transfer saturation maps. Magn Reson Med 2021; 86:2192-2207. [PMID: 33956348 DOI: 10.1002/mrm.28831] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 03/08/2021] [Accepted: 04/16/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE In this work, we propose that Δ B 1 + -induced errors in magnetization transfer (MT) saturation (MTsat ) maps can be corrected with use of an R1 and B 1 + map and through numerical simulations of the sequence. THEORY AND METHODS One healthy subject was scanned at 3.0T using a partial quantitative MT protocol to estimate the relationship between observed R1 (R1,obs ) and apparent bound pool size ( M 0 , a p p B ) in the brain. MTsat values were simulated for a range of B 1 + , R1,obs , and M 0 , a p p B . An equation was fit to the simulated MTsat , then a linear relationship between R1,obs and M 0 , a p p B was generated. These results were used to generate correction factor maps for the MTsat acquired from single-point data. The proposed correction was compared to an empirical correction factor with different MT-preparation schemes. RESULTS M 0 , a p p B was highly correlated with R1,obs (r > 0.96), permitting the use of R1,obs to estimate M 0 , a p p B for B 1 + correction. All B 1 + corrected MTsat maps displayed a decreased correlation with B 1 + compared to uncorrected MTsat and MTsat corrected with an empirical factor in the corpus callosum. There was good agreement between the proposed approach and the empirical correction with radiofrequency saturation at 2 kHz, with larger deviations seen when using saturation pulses further off-resonance and in inhomogeneous (ih) MTsat maps. CONCLUSION The proposed correction decreases the dependence of MTsat on B 1 + inhomogeneities. Furthermore, this flexible framework permits the use of different saturation protocols, making it useful for correcting B 1 + inhomogeneities in ihMT.
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Affiliation(s)
- Christopher D Rowley
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Jennifer S W Campbell
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Zhe Wu
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Techna Institute, University Health Network, Toronto, Ontario, Canada
| | - Ilana R Leppert
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - David A Rudko
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Gilbert Bruce Pike
- Hotchkiss Brain Institute and Departments of Radiology and Clinical Neuroscience, University of Calgary, Calgary, Canada
| | - Christine L Tardif
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.,Department of Biomedical Engineering, McGill University, Montreal, Quebec, Canada
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A Multi-Modal MRI Analysis of Cortical Structure in Relation to Gender Dysphoria, Sexual Orientation, and Age in Adolescents. J Clin Med 2021; 10:jcm10020345. [PMID: 33477567 PMCID: PMC7831120 DOI: 10.3390/jcm10020345] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 12/20/2020] [Accepted: 12/23/2020] [Indexed: 01/18/2023] Open
Abstract
Gender dysphoria (GD) is characterized by distress due to an incongruence between experienced gender and sex assigned at birth. Sex-differentiated brain regions are hypothesized to reflect the experienced gender in GD and may play a role in sexual orientation development. Magnetic resonance brain images were acquired from 16 GD adolescents assigned female at birth (AFAB) not receiving hormone therapy, 17 cisgender girls, and 14 cisgender boys (ages 12–17 years) to examine three morphological and microstructural gray matter features in 76 brain regions: surface area (SA), cortical thickness (CT), and T1 relaxation time. Sexual orientation was represented by degree of androphilia-gynephilia and sexual attraction strength. Multivariate analyses found that cisgender boys had larger SA than cisgender girls and GD AFAB. Shorter T1, reflecting denser, macromolecule-rich tissue, correlated with older age and stronger gynephilia in cisgender boys and GD AFAB, and with stronger attractions in cisgender boys. Thus, cortical morphometry (mainly SA) was related to sex assigned at birth, but not experienced gender. Effects of experienced gender were found as similarities in correlation patterns in GD AFAB and cisgender boys in age and sexual orientation (mainly T1), indicating the need to consider developmental trajectories and sexual orientation in brain studies of GD.
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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.
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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
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39
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Grondin MM, Liu F, Vignos MF, Samsonov A, Li WJ, Kijowski R, Henak CR. Bi-component T2 mapping correlates with articular cartilage material properties. J Biomech 2020; 116:110215. [PMID: 33482593 DOI: 10.1016/j.jbiomech.2020.110215] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 11/20/2020] [Accepted: 12/25/2020] [Indexed: 11/19/2022]
Abstract
Non-invasive estimation of cartilage material properties is useful for understanding cartilage health and creating subject-specific computational models. Bi-component T2 mapping measured using Multi-Component Driven Equilibrium Single Shot Observation of T1 and T2 (mcDESPOT) is sensitive for detecting cartilage degeneration within the human knee joint, but has not been correlated with cartilage composition and mechanical properties. Therefore, the purpose of this study was to investigate the relationship between bi-component T2 parameters measured using mcDESPOT at 3.0 T and cartilage composition and mechanical properties. Ex-vivo patellar cartilage specimens harvested from five human cadaveric knees were imaged using mcDESPOT at 3.0 T. Cartilage samples were removed from the patellae, mechanically tested to determine linear modulus and dissipated energy, and chemically tested to determine proteoglycan and collagen content. Parameter maps of single-component T2 relaxation time (T2), the T2 relaxation times of the fast relaxing macromolecular bound water component (T2F) and slow relaxing bulk water component (T2S), and the fraction of the fast relaxing macromolecular bound water component (FF) were compared to mechanical and chemical measures using linear regression. FF was significantly (p < 0.05) correlated with energy dissipation and linear modulus. T2 was significantly (p ≤ 0.05) correlated with elastic modulus at 1 Hz and energy dissipated at all frequencies. There were no other significant (p = 0.13-0.97) correlations between mcDESPOT parameters and mechanical properties. FF was significantly (p = 0.04) correlated with proteoglycan content. There were no other significant (p = 0.19-0.92) correlations between mcDESPOT parameters and proteoglycan or collagen content. This study suggests that FF measured using mcDESPOT at 3.0 T could be used to non-invasively estimate cartilage proteoglycan content, elastic modulus, and energy dissipation.
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Affiliation(s)
- Matthew M Grondin
- Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Fang Liu
- Department of Radiology, Massachusetts General Hospital, Harvard University, Boston, MA, USA
| | - Michael F Vignos
- Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Alexey Samsonov
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Wan-Ju Li
- Department of Orthopedics and Rehabilitation, University of Wisconsin-Madison, Madison, WI, USA; Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Richard Kijowski
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Corinne R Henak
- Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI, USA; Department of Orthopedics and Rehabilitation, University of Wisconsin-Madison, Madison, WI, USA; Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA.
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40
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Desmond KL, Xu R, Sun Y, Chavez S. A practical method for post-acquisition reduction of bias in fast, whole-brain B1-maps. Magn Reson Imaging 2020; 77:88-98. [PMID: 33338561 DOI: 10.1016/j.mri.2020.12.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 11/22/2020] [Accepted: 12/13/2020] [Indexed: 12/24/2022]
Abstract
Large consistent differences have been observed between maps of the flip angle correction factor (commonly called "B1-maps") produced with different fast methods in the human brain. We present an empirical procedure for first-order multiplicative bias correction that can be applied when more than one B1-mapping method is available. We use a B1-map measurement in a calibration phantom as a reference and the voxel-wise histogram mode between ratios of B1-maps produced from different methods to calculate determine the bias as a multiplicative correcting scale factor. Institutional implementations of four common methods of B1-mapping were assessed: Method of Slopes, FSE and EPI double angle methods (DAM), and Bloch-Siegert. In human subjects, the multiplicative bias used to correct for each of the four methods was: Method of Slopes = 1.005, FSE-DAM = 0.956, EPI-DAM = 1.080, and Bloch-Siegert = 1.128. Scaling to remove this bias between methods produces more consistent B1-maps which enable more consistent values for any computations requiring flip angle correction. In addition, we present evidence that the corrected B1 maps, using our calibration method, are also more accurate.
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Affiliation(s)
- Kimberly L Desmond
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario M5T 1R8, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario M5T 1R8, Canada.
| | - Ruiyang Xu
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario M5T 1R8, Canada
| | - Yutong Sun
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario M5T 1R8, Canada
| | - Sofia Chavez
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario M5T 1R8, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario M5T 1R8, Canada
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41
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Welsink-Karssies MM, Schrantee A, Caan MWA, Hollak CEM, Janssen MCH, Oussoren E, de Vries MC, Roosendaal SD, Engelen M, Bosch AM. Gray and white matter are both affected in classical galactosemia: An explorative study on the association between neuroimaging and clinical outcome. Mol Genet Metab 2020; 131:370-379. [PMID: 33199205 DOI: 10.1016/j.ymgme.2020.11.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 11/01/2020] [Accepted: 11/01/2020] [Indexed: 01/11/2023]
Abstract
BACKGROUND Classical Galactosemia (CG) is an inherited disorder of galactose metabolism caused by a deficiency of the galactose-1-phosphate uridylyltransferase (GALT) enzyme resulting in neurocognitive complications. As in many Inborn Errors of Metabolism, the metabolic pathway of CG is well-defined, but the pathophysiology and high variability in clinical outcome are poorly understood. The aim of this study was to investigate structural changes of the brain of CG patients on MRI and their association with clinical outcome. METHODS In this prospective cohort study an MRI protocol was developed to evaluate gray matter (GM) and white matter (WM) volume of the cerebrum and cerebellum, WM hyperintensity volume, WM microstructure and myelin content with the use of conventional MRI techniques, diffusion tensor imaging (DTI) and quantitative T1 mapping. The association between several neuroimaging parameters and both neurological and intellectual outcome was investigated. RESULTS Twenty-one patients with CG (median age 22 years, range 8-47) and 24 controls (median age 30, range 16-52) were included. Compared to controls, the WM of CG patients was lower in volume and the microstructure of WM was impaired both in the whole brain and corticospinal tract (CST) and the lower R1 values of WM, GM and the CST were indicative of less myelin. The volume of WM lesions were comparable between patients and controls. The 9/16 patients with a poor neurological outcome (defined as the presence of a tremor and/or dystonia), demonstrated a lower WM volume, an impaired WM microstructure and lower R1 values of the WM indicative of less myelin content compared to 7/16 patients without movement disorders. In 15/21 patients with a poor intellectual outcome (defined as an IQ < 85) both GM and WM were affected with a lower cerebral and cerebellar WM and GM volume compared to 6/21 patients with an IQ ≥ 85. Both the severity of the tremor (as indicated by the Tremor Rating Scale) and IQ (as continuous measure) were associated with several neuroimaging parameters such as GM volume, WM volume, CSF volume, WM microstructure parameters and R1 values of GM and WM. CONCLUSION In this explorative study performed in patients with Classical Galactosemia, not only WM but also GM pathology was found, with more severe brain abnormalities on MRI in patients with a poor neurological and intellectual outcome. The finding that structural changes of the brain were associated with the severity of long-term complications indicates that quantitative MRI techniques could be of use to explain neurological and cognitive dysfunction as part of the disease spectrum. Based on the clinical outcome of patients, the absence of widespread WM lesions and the finding that both GM and WM are affected, CG could be primarily a GM disease with secondary damage to the WM as a result of neuronal degeneration. To investigate this further the course of GM and WM should be evaluated in longitudinal research, which could also clarify if CG is a neurodegenerative disease.
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Affiliation(s)
- Mendy M Welsink-Karssies
- Department of Pediatrics, Division of Metabolic Disorders, Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Anouk Schrantee
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Matthan W A Caan
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Department of Biomedical Engineering, Amsterdam University Medical Center, location AMC, Amsterdam, the Netherlands
| | - Carla E M Hollak
- Department of Internal Medicine, Division of Endocrinology and Metabolism, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Mirian C H Janssen
- Department of Internal Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Esmee Oussoren
- Department of Pediatrics, Center for Lysosomal and Metabolic Diseases, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Maaike C de Vries
- Department of Pediatrics, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Stefan D Roosendaal
- Department of Radiology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Marc Engelen
- Department of Pediatrics, Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands; Department of Neurology, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Annet M Bosch
- Department of Pediatrics, Division of Metabolic Disorders, Emma Children's Hospital, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands.
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Accelerating quantitative MR imaging with the incorporation of B1 compensation using deep learning. Magn Reson Imaging 2020; 72:78-86. [DOI: 10.1016/j.mri.2020.06.011] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 05/20/2020] [Accepted: 06/13/2020] [Indexed: 11/21/2022]
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Isaacs BR, Keuken MC, Alkemade A, Temel Y, Bazin PL, Forstmann BU. Methodological Considerations for Neuroimaging in Deep Brain Stimulation of the Subthalamic Nucleus in Parkinson's Disease Patients. J Clin Med 2020; 9:E3124. [PMID: 32992558 PMCID: PMC7600568 DOI: 10.3390/jcm9103124] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 09/17/2020] [Accepted: 09/25/2020] [Indexed: 12/17/2022] Open
Abstract
Deep brain stimulation (DBS) of the subthalamic nucleus is a neurosurgical intervention for Parkinson's disease patients who no longer appropriately respond to drug treatments. A small fraction of patients will fail to respond to DBS, develop psychiatric and cognitive side-effects, or incur surgery-related complications such as infections and hemorrhagic events. In these cases, DBS may require recalibration, reimplantation, or removal. These negative responses to treatment can partly be attributed to suboptimal pre-operative planning procedures via direct targeting through low-field and low-resolution magnetic resonance imaging (MRI). One solution for increasing the success and efficacy of DBS is to optimize preoperative planning procedures via sophisticated neuroimaging techniques such as high-resolution MRI and higher field strengths to improve visualization of DBS targets and vasculature. We discuss targeting approaches, MRI acquisition, parameters, and post-acquisition analyses. Additionally, we highlight a number of approaches including the use of ultra-high field (UHF) MRI to overcome limitations of standard settings. There is a trade-off between spatial resolution, motion artifacts, and acquisition time, which could potentially be dissolved through the use of UHF-MRI. Image registration, correction, and post-processing techniques may require combined expertise of traditional radiologists, clinicians, and fundamental researchers. The optimization of pre-operative planning with MRI can therefore be best achieved through direct collaboration between researchers and clinicians.
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Affiliation(s)
- Bethany R. Isaacs
- Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, 1018 WS Amsterdam, The Netherlands; (A.A.); (P.-L.B.); (B.U.F.)
- Department of Experimental Neurosurgery, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands;
| | - Max C. Keuken
- Municipality of Amsterdam, Services & Data, Cluster Social, 1000 AE Amsterdam, The Netherlands;
| | - Anneke Alkemade
- Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, 1018 WS Amsterdam, The Netherlands; (A.A.); (P.-L.B.); (B.U.F.)
| | - Yasin Temel
- Department of Experimental Neurosurgery, Maastricht University Medical Center, 6202 AZ Maastricht, The Netherlands;
| | - Pierre-Louis Bazin
- Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, 1018 WS Amsterdam, The Netherlands; (A.A.); (P.-L.B.); (B.U.F.)
- Max Planck Institute for Human Cognitive and Brain Sciences, D-04103 Leipzig, Germany
| | - Birte U. Forstmann
- Integrative Model-based Cognitive Neuroscience Research Unit, University of Amsterdam, 1018 WS Amsterdam, The Netherlands; (A.A.); (P.-L.B.); (B.U.F.)
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Edmondson DA, Yeh CL, Hélie S, Dydak U. Whole-brain R1 predicts manganese exposure and biological effects in welders. Arch Toxicol 2020; 94:3409-3420. [PMID: 32875357 DOI: 10.1007/s00204-020-02839-7] [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: 02/21/2020] [Accepted: 07/09/2020] [Indexed: 12/25/2022]
Abstract
Manganese (Mn) is a neurotoxicant that, due to its paramagnetic property, also functions as a magnetic resonance imaging (MRI) T1 contrast agent. Previous studies in Mn toxicity have shown that Mn accumulates in the brain, which may lead to parkinsonian symptoms. In this article, we trained support vector machines (SVM) using whole-brain R1 (R1 = 1/T1) maps from 57 welders and 32 controls to classify subjects based on their air Mn concentration ([Mn]Air), Mn brain accumulation (ExMnBrain), gross motor dysfunction (UPDRS), thalamic GABA concentration (GABAThal), and total years welding. R1 was highly predictive of [Mn]Air above a threshold of 0.20 mg/m3 with an accuracy of 88.8% and recall of 88.9%. R1 was also predictive of subjects with GABAThal having less than or equal to 2.6 mM with an accuracy of 82% and recall of 78.9%. Finally, we used an SVM to predict age as a method of verifying that the results could be attributed to Mn exposure. We found that R1 was predictive of age below 48 years of age with accuracies ranging between 75 and 82% with recall between 94.7% and 76.9% but was not predictive above 48 years of age. Together, this suggests that lower levels of exposure (< 0.20 mg/m3 and < 18 years of welding on the job) do not produce discernable signatures, whereas higher air exposures and subjects with more total years welding produce signatures in the brain that are readily identifiable using SVM.
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Affiliation(s)
- David A Edmondson
- School of Health Sciences, Purdue University, 550 Stadium Dr., Hampton Hall of Civil Engineering, West Lafayette, IN, 47907, USA.,Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.,Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Chien-Lin Yeh
- School of Health Sciences, Purdue University, 550 Stadium Dr., Hampton Hall of Civil Engineering, West Lafayette, IN, 47907, USA.,Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sébastien Hélie
- Department of Psychological Sciences, Purdue University, West Lafayette, IN, USA
| | - Ulrike Dydak
- School of Health Sciences, Purdue University, 550 Stadium Dr., Hampton Hall of Civil Engineering, West Lafayette, IN, 47907, USA. .,Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA.
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Maunder A, Rao M, Robb F, Wild JM. An 8-element Tx/Rx array utilizing MEMS detuning combined with 6 Rx loops for 19 F and 1 H lung imaging at 1.5T. Magn Reson Med 2020; 84:2262-2277. [PMID: 32281139 DOI: 10.1002/mrm.28260] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 02/27/2020] [Accepted: 02/27/2020] [Indexed: 12/24/2022]
Abstract
PURPOSE To firstly improve the attainable image SNR of 19 F and 1 H C3 F8 lung imaging at 1.5 tesla using an 8-element transmit/receive (Tx/Rx) flexible vest array combined with a 6-element Rx-only array, and to secondly evaluate microelectromechanical systems for switching the array elements between the 2 resonant frequencies. METHODS The Tx efficiency and homogeneity of the 8-element array were measured and simulated for 1 H imaging in a cylindrical phantom and then evaluated for in vivo 19 F/1 H imaging. The added improvement provided by the 6-element Rx-only array was quantified through simulation and measurement and compared to the ultimate SNR. It was verified through the measurement of isolation that microelectromechanical systems switches provided broadband isolation of Tx/Rx circuitry such that the 19 F tuned Tx/Rx array could be effectively used for both 19 F and 1 H nuclei. RESULTS For 1 H imaging, the measured Tx efficiency/homogeneity (mean ± percent SD; 6.79 μ T / kW ± 26 % ) was comparable to that simulated ( 7.57 μ T / kW ± 20 % ). The 6 additional Rx-only loops increased the mean Rx sensitivity when compared to the 8-element array by a factor of 1.41× and 1.45× in simulation and measurement, respectively. In regions central to the thorax, the simulated SNR of the 14-element array achieves ≥70% of the ultimate SNR when including noise from the matching circuits and preamplifiers. A measured microelectromechanical systems switching speed of 12 µs and added minimum 22 dB of isolation between Tx and Rx were sufficient for Tx/Rx switching in this application. CONCLUSION The described single-tuned array driven at 19 F and 1 H, utilizing microelectromechanical systems technology, provides excellent results for 19 F and 1 H dual-nuclear lung ventilation imaging.
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Affiliation(s)
- Adam Maunder
- POLARIS, Imaging Group, Department of IICD, University of Sheffield, Sheffield, United Kingdom
| | - Madhwesha Rao
- POLARIS, Imaging Group, Department of IICD, University of Sheffield, Sheffield, United Kingdom
| | - Fraser Robb
- POLARIS, Imaging Group, Department of IICD, University of Sheffield, Sheffield, United Kingdom.,GE Healthcare, Aurora, OH, USA
| | - Jim M Wild
- POLARIS, Imaging Group, Department of IICD, University of Sheffield, Sheffield, United Kingdom
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Microstructural imaging in temporal lobe epilepsy: Diffusion imaging changes relate to reduced neurite density. NEUROIMAGE-CLINICAL 2020; 26:102231. [PMID: 32146320 PMCID: PMC7063236 DOI: 10.1016/j.nicl.2020.102231] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 02/27/2020] [Accepted: 02/27/2020] [Indexed: 01/06/2023]
Abstract
PURPOSE Previous imaging studies in patients with refractory temporal lobe epilepsy (TLE) have examined the spatial distribution of changes in imaging parameters such as diffusion tensor imaging (DTI) metrics and cortical thickness. Multi-compartment models offer greater specificity with parameters more directly related to known changes in TLE such as altered neuronal density and myelination. We studied the spatial distribution of conventional and novel metrics including neurite density derived from NODDI (Neurite Orientation Dispersion and Density Imaging) and myelin water fraction (MWF) derived from mcDESPOT (Multi-Compartment Driven Equilibrium Single Pulse Observation of T1/T2)] to infer the underlying neurobiology of changes in conventional metrics. METHODS 20 patients with TLE and 20 matched controls underwent magnetic resonance imaging including a volumetric T1-weighted sequence, multi-shell diffusion from which DTI and NODDI metrics were derived and a protocol suitable for mcDESPOT fitting. Models of the grey matter-white matter and grey matter-CSF surfaces were automatically generated from the T1-weighted MRI. Conventional diffusion and novel metrics of neurite density and MWF were sampled from intracortical grey matter and subcortical white matter surfaces and cortical thickness was measured. RESULTS In intracortical grey matter, diffusivity was increased in the ipsilateral temporal and frontopolar cortices with more restricted areas of reduced neurite density. Diffusivity increases were largely related to reductions in neurite density, and to a lesser extent CSF partial volume effects, but not MWF. In subcortical white matter, widespread bilateral reductions in fractional anisotropy and increases in radial diffusivity were seen. These were primarily related to reduced neurite density, with an additional relationship to reduced MWF in the temporal pole and anterolateral temporal neocortex. Changes were greater with increasing epilepsy duration. Bilaterally reduced cortical thickness in the mesial temporal lobe and centroparietal cortices was unrelated to neurite density and MWF. CONCLUSIONS Diffusivity changes in grey and white matter are primarily related to reduced neurite density with an additional relationship to reduced MWF in the temporal pole. Neurite density may represent a more sensitive and specific biomarker of progressive neuronal damage in refractory TLE that deserves further study.
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47
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Ljungberg E, Wood T, Solana AB, Kolind S, Williams SCR, Wiesinger F, Barker GJ. Silent T
1
mapping using the variable flip angle method with B
1
correction. Magn Reson Med 2020; 84:813-824. [DOI: 10.1002/mrm.28178] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 12/11/2019] [Accepted: 12/30/2019] [Indexed: 11/12/2022]
Affiliation(s)
- Emil Ljungberg
- Department of Neuroimaging Institute of Psychiatry, Psychology & Neuroscience, King's College London London UK
| | - Tobias Wood
- Department of Neuroimaging Institute of Psychiatry, Psychology & Neuroscience, King's College London London UK
| | | | - Shannon Kolind
- Department of Physics and Astronomy University of British Columbia Vancouver BC Canada
- Department of Radiology University of British Columbia Vancouver BC Canada
- International Collaboration on Repair Discoveries University of British Columbia Vancouver BC Canada
- Medicine (Neurology) University of British Columbia Vancouver BC Canada
| | - Steven C. R. Williams
- Department of Neuroimaging Institute of Psychiatry, Psychology & Neuroscience, King's College London London UK
| | - Florian Wiesinger
- Department of Neuroimaging Institute of Psychiatry, Psychology & Neuroscience, King's College London London UK
- ASL Europe, General Electric Healthcare Munich Germany
| | - Gareth J. Barker
- Department of Neuroimaging Institute of Psychiatry, Psychology & Neuroscience, King's College London London UK
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48
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Björnholm L, Nikkinen J, Kiviniemi V, Niemelä S, Drakesmith M, Evans JC, Pike GB, Richer L, Pausova Z, Veijola J, Paus T. Prenatal exposure to maternal cigarette smoking and structural properties of the human corpus callosum. Neuroimage 2019; 209:116477. [PMID: 31874257 DOI: 10.1016/j.neuroimage.2019.116477] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 12/09/2019] [Accepted: 12/18/2019] [Indexed: 11/28/2022] Open
Abstract
Alterations induced by prenatal exposure to nicotine have been observed in experimental (rodent) studies. While numerous developmental outcomes have been associated with prenatal exposure to maternal cigarette smoking (PEMCS) in humans, the possible relation with brain structure is less clear. Here we sought to elucidate the relation between PEMCS and structural properties of human corpus callosum in adolescence and early adulthood in a total of 1,747 youth. We deployed three community-based cohorts of 446 (age 25-27 years, 46% exposed), 934 (age 12-18 years, 47% exposed) and 367 individuals (age 18-21 years, 9% exposed). A mega-analysis revealed lower mean diffusivity in the callosal segments of exposed males. We speculate that prenatal exposure to maternal cigarette smoking disrupts the early programming of callosal structure and increases the relative portion of small-diameter fibres.
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Affiliation(s)
- L Björnholm
- Department of Psychiatry, Research Unit of Clinical Neuroscience, University of Oulu and Oulu University Hospital, Oulu, Finland.
| | - J Nikkinen
- Department of Radiotherapy, Oulu University Hospital, Oulu, Finland; MIPT/MRC, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - V Kiviniemi
- Institute of Diagnostics, Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland; Oulu Functional Neuroimaging, MIPT/MRC, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - S Niemelä
- Department of Psychiatry, University of Turku, Turku, Finland; Addiction Psychiatry Unit, Department of Psychiatry, Hospital District of Southwest Finland, Finland
| | - M Drakesmith
- School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - J C Evans
- School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - G B Pike
- Department of Radiology and Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - L Richer
- Department of Health Sciences, Université du Québec à Chicoutimi, Chicoutimi, QC, Canada
| | - Z Pausova
- The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada; Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, ON, Canada
| | - J Veijola
- Department of Psychiatry, Research Unit of Clinical Neuroscience, University of Oulu and Oulu University Hospital, Oulu, Finland
| | - T Paus
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada; Departments of Psychology and Psychiatry, University of Toronto, Toronto, Canada.
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Drakesmith M, Harms R, Rudrapatna SU, Parker GD, Evans CJ, Jones DK. Estimating axon conduction velocity in vivo from microstructural MRI. Neuroimage 2019; 203:116186. [PMID: 31542512 PMCID: PMC6854468 DOI: 10.1016/j.neuroimage.2019.116186] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 09/09/2019] [Accepted: 09/10/2019] [Indexed: 11/19/2022] Open
Abstract
The conduction velocity (CV) of action potentials along axons is a key neurophysiological property central to neural communication. The ability to estimate CV in humans in vivo from non-invasive MRI methods would therefore represent a significant advance in neuroscience. However, there are two major challenges that this paper aims to address: (1) Much of the complexity of the neurophysiology of action potentials cannot be captured with currently available MRI techniques. Therefore, we seek to establish the variability in CV that can be captured when predicting CV purely from parameters that have been reported to be estimatable from MRI: inner axon diameter (AD) and g-ratio. (2) errors inherent in existing MRI-based biophysical models of tissue will propagate through to estimates of CV, the extent to which is currently unknown. Issue (1) is investigated by performing a sensitivity analysis on a comprehensive model of axon electrophysiology and determining the relative sensitivity to various morphological and electrical parameters. The investigations suggest that 85% of the variance in CV is accounted for by variation in AD and g-ratio. The observed dependency of CV on AD and g-ratio is well characterised by the previously reported model by Rushton. Issue (2) is investigated through simulation of diffusion and relaxometry MRI data for a range of axon morphologies, applying models of restricted diffusion and relaxation processes to derive estimates of axon volume fraction (AVF), AD and g-ratio and estimating CV from the derived parameters. The results show that errors in the AVF have the biggest detrimental impact on estimates of CV, particularly for sparse fibre populations (AVF<0.3). For our equipment set-up and acquisition protocol, CV estimates are most accurate (below 5% error) where AVF is above 0.3, g-ratio is between 0.6 and 0.85 and AD is high (above 4μm). CV estimates are robust to errors in g-ratio estimation but are highly sensitive to errors in AD estimation, particularly where ADs are small. We additionally show CV estimates in human corpus callosum in a small number of subjects. In conclusion, we demonstrate accurate CV estimates are possible in regions of the brain where AD is sufficiently large. Problems with estimating ADs for smaller axons presents a problem for estimating CV across the whole CNS and should be the focus of further study.
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Affiliation(s)
- Mark Drakesmith
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom.
| | - Robbert Harms
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, the Netherlands
| | - Suryanarayana Umesh Rudrapatna
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Phillips Inovation Campus, Bangalore, India
| | - Greg D Parker
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Experimental MRI Centre (EMRIC), School of Biosciences, Cardiff University, Cardiff, United Kingdom
| | - C John Evans
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom; Mary McKillop Institute for Health Research, Faculty of Health Sciences, Australian Catholic University, Melbourne, Victoria, 3065, Australia
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50
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Kaggie JD, Deen S, Kessler DA, McLean MA, Buonincontri G, Schulte RF, Addley H, Sala E, Brenton J, Graves MJ, Gallagher FA. Feasibility of Quantitative Magnetic Resonance Fingerprinting in Ovarian Tumors for T 1 and T 2 Mapping in a PET/MR Setting. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2019; 3:509-515. [PMID: 32066996 PMCID: PMC7025887 DOI: 10.1109/trpms.2019.2905366] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Multiparametric magnetic resonance imaging (MRI) can be used to characterize many cancer subtypes including ovarian cancer. Quantitative mapping of MRI relaxation values, such as T 1 and T 2 mapping, is promising for improving tumor assessment beyond conventional qualitative T 1- and T 2-weighted images. However, quantitative MRI relaxation mapping methods often involve long scan times due to sequentially measuring many parameters. Magnetic resonance fingerprinting (MRF) is a new method that enables fast quantitative MRI by exploiting the transient signals caused by the variation of pseudorandom sequence parameters. These transient signals are then matched to a simulated dictionary of T 1 and T 2 values to create quantitative maps. The ability of MRF to simultaneously measure multiple parameters, could represent a new approach to characterizing cancer and assessing treatment response. This feasibility study investigates MRF for simultaneous T 1, T 2, and relative proton density (rPD) mapping using ovarian cancer as a model system.
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Affiliation(s)
- Joshua D. Kaggie
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, U.K.; Cambridge University Hospitals, NHS Foundation Trust, Addenbrooke’s Hospital, Cambridge, U.K
| | - Surrin Deen
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, U.K.; Cambridge University Hospitals, NHS Foundation Trust, Addenbrooke’s Hospital, Cambridge, U.K
| | - Dimitri A. Kessler
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, U.K.; Cambridge University Hospitals, NHS Foundation Trust, Addenbrooke’s Hospital, Cambridge, U.K
| | - Mary A. McLean
- Cancer Research U.K. Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, U.K
| | | | | | - Helen Addley
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, U.K.; Cambridge University Hospitals, NHS Foundation Trust, Addenbrooke’s Hospital, Cambridge, U.K
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, U.K.; Cambridge University Hospitals, NHS Foundation Trust, Addenbrooke’s Hospital, Cambridge, U.K.; Cancer Research U.K. Cambridge Institute, Cambridge CB2 0RE, U.K
| | - James Brenton
- Cancer Research U.K. Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, U.K
| | - Martin J. Graves
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, U.K.; Cambridge University Hospitals, NHS Foundation Trust, Addenbrooke’s Hospital, Cambridge, U.K
| | - Ferdia A. Gallagher
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, U.K.; Cambridge University Hospitals, NHS Foundation Trust, Addenbrooke’s Hospital, Cambridge, U.K
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