Relevance of time‐dependence for clinically viable diffusion imaging of the spinal cord

Purpose Time‐dependence is a key feature of the diffusion‐weighted (DW) signal, knowledge of which informs biophysical modelling. Here, we study time‐dependence in the human spinal cord, as its axonal structure is specific and different from the brain. Methods We run Monte Carlo simulations using a...

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Vydáno v:	Magnetic resonance in medicine Ročník 81; číslo 2; s. 1247 - 1264
Hlavní autoři:	Grussu, Francesco, Ianuş, Andrada, Tur, Carmen, Prados, Ferran, Schneider, Torben, Kaden, Enrico, Ourselin, Sébastien, Drobnjak, Ivana, Zhang, Hui, Alexander, Daniel C., Gandini Wheeler‐Kingshott, Claudia A. M.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Wiley Subscription Services, Inc 01.02.2019 John Wiley and Sons Inc
Témata:	Adult Algorithms Axons Axons - pathology Brain Brain - diagnostic imaging Computer Simulation Cylinders Diffusion Diffusion Magnetic Resonance Imaging diffusion time Feasibility studies Female Full Papers—Biophysics and Basic Biomedical Research Healthy Volunteers Humans Image Processing, Computer-Assisted - methods Kurtosis Magnetic resonance imaging Male microstructure Monte Carlo Method Monte Carlo simulation Monte Carlo simulations Neuroimaging Spinal cord Spinal Cord - diagnostic imaging Substantia alba Time dependence Time Factors white matter white matter Monte Carlo simulations microstructure diffusion time spinal cord
ISSN:	0740-3194, 1522-2594, 1522-2594
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Abstract	Purpose Time‐dependence is a key feature of the diffusion‐weighted (DW) signal, knowledge of which informs biophysical modelling. Here, we study time‐dependence in the human spinal cord, as its axonal structure is specific and different from the brain. Methods We run Monte Carlo simulations using a synthetic model of spinal cord white matter (WM) (large axons), and of brain WM (smaller axons). Furthermore, we study clinically feasible multi‐shell DW scans of the cervical spinal cord (b = 0; b = 711 s mm−2; b = 2855 s mm−2), obtained using three diffusion times (Δ of 29, 52 and 76 ms) from three volunteers. Results Both intra‐/extra‐axonal perpendicular diffusivities and kurtosis excess show time‐dependence in our synthetic spinal cord model. This time‐dependence is reflected mostly in the intra‐axonal perpendicular DW signal, which also exhibits strong decay, unlike our brain model. Time‐dependence of the total DW signal appears detectable in the presence of noise in our synthetic spinal cord model, but not in the brain. In WM in vivo, we observe time‐dependent macroscopic and microscopic diffusivities and diffusion kurtosis, NODDI and two‐compartment SMT metrics. Accounting for large axon calibers improves fitting of multi‐compartment models to a minor extent. Conclusions Time‐dependence of clinically viable DW MRI metrics can be detected in vivo in spinal cord WM, thus providing new opportunities for the non‐invasive estimation of microstructural properties. The time‐dependence of the perpendicular DW signal may feature strong intra‐axonal contributions due to large spinal axon caliber. Hence, a popular model known as “stick” (zero‐radius cylinder) may be sub‐optimal to describe signals from the largest spinal axons.
AbstractList	PurposeTime‐dependence is a key feature of the diffusion‐weighted (DW) signal, knowledge of which informs biophysical modelling. Here, we study time‐dependence in the human spinal cord, as its axonal structure is specific and different from the brain.MethodsWe run Monte Carlo simulations using a synthetic model of spinal cord white matter (WM) (large axons), and of brain WM (smaller axons). Furthermore, we study clinically feasible multi‐shell DW scans of the cervical spinal cord (b = 0; b = 711 s mm−2; b = 2855 s mm−2), obtained using three diffusion times (Δ of 29, 52 and 76 ms) from three volunteers.ResultsBoth intra‐/extra‐axonal perpendicular diffusivities and kurtosis excess show time‐dependence in our synthetic spinal cord model. This time‐dependence is reflected mostly in the intra‐axonal perpendicular DW signal, which also exhibits strong decay, unlike our brain model. Time‐dependence of the total DW signal appears detectable in the presence of noise in our synthetic spinal cord model, but not in the brain. In WM in vivo, we observe time‐dependent macroscopic and microscopic diffusivities and diffusion kurtosis, NODDI and two‐compartment SMT metrics. Accounting for large axon calibers improves fitting of multi‐compartment models to a minor extent.ConclusionsTime‐dependence of clinically viable DW MRI metrics can be detected in vivo in spinal cord WM, thus providing new opportunities for the non‐invasive estimation of microstructural properties. The time‐dependence of the perpendicular DW signal may feature strong intra‐axonal contributions due to large spinal axon caliber. Hence, a popular model known as “stick” (zero‐radius cylinder) may be sub‐optimal to describe signals from the largest spinal axons. Time-dependence is a key feature of the diffusion-weighted (DW) signal, knowledge of which informs biophysical modelling. Here, we study time-dependence in the human spinal cord, as its axonal structure is specific and different from the brain. We run Monte Carlo simulations using a synthetic model of spinal cord white matter (WM) (large axons), and of brain WM (smaller axons). Furthermore, we study clinically feasible multi-shell DW scans of the cervical spinal cord (b = 0; b = 711 s mm ; b = 2855 s mm ), obtained using three diffusion times (Δ of 29, 52 and 76 ms) from three volunteers. Both intra-/extra-axonal perpendicular diffusivities and kurtosis excess show time-dependence in our synthetic spinal cord model. This time-dependence is reflected mostly in the intra-axonal perpendicular DW signal, which also exhibits strong decay, unlike our brain model. Time-dependence of the total DW signal appears detectable in the presence of noise in our synthetic spinal cord model, but not in the brain. In WM in vivo, we observe time-dependent macroscopic and microscopic diffusivities and diffusion kurtosis, NODDI and two-compartment SMT metrics. Accounting for large axon calibers improves fitting of multi-compartment models to a minor extent. Time-dependence of clinically viable DW MRI metrics can be detected in vivo in spinal cord WM, thus providing new opportunities for the non-invasive estimation of microstructural properties. The time-dependence of the perpendicular DW signal may feature strong intra-axonal contributions due to large spinal axon caliber. Hence, a popular model known as "stick" (zero-radius cylinder) may be sub-optimal to describe signals from the largest spinal axons. Purpose Time‐dependence is a key feature of the diffusion‐weighted (DW) signal, knowledge of which informs biophysical modelling. Here, we study time‐dependence in the human spinal cord, as its axonal structure is specific and different from the brain. Methods We run Monte Carlo simulations using a synthetic model of spinal cord white matter (WM) (large axons), and of brain WM (smaller axons). Furthermore, we study clinically feasible multi‐shell DW scans of the cervical spinal cord (b = 0; b = 711 s mm−2; b = 2855 s mm−2), obtained using three diffusion times (Δ of 29, 52 and 76 ms) from three volunteers. Results Both intra‐/extra‐axonal perpendicular diffusivities and kurtosis excess show time‐dependence in our synthetic spinal cord model. This time‐dependence is reflected mostly in the intra‐axonal perpendicular DW signal, which also exhibits strong decay, unlike our brain model. Time‐dependence of the total DW signal appears detectable in the presence of noise in our synthetic spinal cord model, but not in the brain. In WM in vivo, we observe time‐dependent macroscopic and microscopic diffusivities and diffusion kurtosis, NODDI and two‐compartment SMT metrics. Accounting for large axon calibers improves fitting of multi‐compartment models to a minor extent. Conclusions Time‐dependence of clinically viable DW MRI metrics can be detected in vivo in spinal cord WM, thus providing new opportunities for the non‐invasive estimation of microstructural properties. The time‐dependence of the perpendicular DW signal may feature strong intra‐axonal contributions due to large spinal axon caliber. Hence, a popular model known as “stick” (zero‐radius cylinder) may be sub‐optimal to describe signals from the largest spinal axons. Time-dependence is a key feature of the diffusion-weighted (DW) signal, knowledge of which informs biophysical modelling. Here, we study time-dependence in the human spinal cord, as its axonal structure is specific and different from the brain.PURPOSETime-dependence is a key feature of the diffusion-weighted (DW) signal, knowledge of which informs biophysical modelling. Here, we study time-dependence in the human spinal cord, as its axonal structure is specific and different from the brain.We run Monte Carlo simulations using a synthetic model of spinal cord white matter (WM) (large axons), and of brain WM (smaller axons). Furthermore, we study clinically feasible multi-shell DW scans of the cervical spinal cord (b = 0; b = 711 s mm-2 ; b = 2855 s mm-2 ), obtained using three diffusion times (Δ of 29, 52 and 76 ms) from three volunteers.METHODSWe run Monte Carlo simulations using a synthetic model of spinal cord white matter (WM) (large axons), and of brain WM (smaller axons). Furthermore, we study clinically feasible multi-shell DW scans of the cervical spinal cord (b = 0; b = 711 s mm-2 ; b = 2855 s mm-2 ), obtained using three diffusion times (Δ of 29, 52 and 76 ms) from three volunteers.Both intra-/extra-axonal perpendicular diffusivities and kurtosis excess show time-dependence in our synthetic spinal cord model. This time-dependence is reflected mostly in the intra-axonal perpendicular DW signal, which also exhibits strong decay, unlike our brain model. Time-dependence of the total DW signal appears detectable in the presence of noise in our synthetic spinal cord model, but not in the brain. In WM in vivo, we observe time-dependent macroscopic and microscopic diffusivities and diffusion kurtosis, NODDI and two-compartment SMT metrics. Accounting for large axon calibers improves fitting of multi-compartment models to a minor extent.RESULTSBoth intra-/extra-axonal perpendicular diffusivities and kurtosis excess show time-dependence in our synthetic spinal cord model. This time-dependence is reflected mostly in the intra-axonal perpendicular DW signal, which also exhibits strong decay, unlike our brain model. Time-dependence of the total DW signal appears detectable in the presence of noise in our synthetic spinal cord model, but not in the brain. In WM in vivo, we observe time-dependent macroscopic and microscopic diffusivities and diffusion kurtosis, NODDI and two-compartment SMT metrics. Accounting for large axon calibers improves fitting of multi-compartment models to a minor extent.Time-dependence of clinically viable DW MRI metrics can be detected in vivo in spinal cord WM, thus providing new opportunities for the non-invasive estimation of microstructural properties. The time-dependence of the perpendicular DW signal may feature strong intra-axonal contributions due to large spinal axon caliber. Hence, a popular model known as "stick" (zero-radius cylinder) may be sub-optimal to describe signals from the largest spinal axons.CONCLUSIONSTime-dependence of clinically viable DW MRI metrics can be detected in vivo in spinal cord WM, thus providing new opportunities for the non-invasive estimation of microstructural properties. The time-dependence of the perpendicular DW signal may feature strong intra-axonal contributions due to large spinal axon caliber. Hence, a popular model known as "stick" (zero-radius cylinder) may be sub-optimal to describe signals from the largest spinal axons.
Author	Grussu, Francesco Kaden, Enrico Zhang, Hui Gandini Wheeler‐Kingshott, Claudia A. M. Drobnjak, Ivana Ianuş, Andrada Ourselin, Sébastien Tur, Carmen Schneider, Torben Alexander, Daniel C. Prados, Ferran
AuthorAffiliation	2 Centre for Medical Image Computing, Department of Computer Science University College London London United Kingdom 7 Brain MRI 3T Research Centre C. Mondino National Neurological Institute Pavia Italy 5 Philips United Kingdom Guildford Surrey United Kingdom 6 Clinical Imaging Research Centre National University of Singapore Singapore Singapore 4 Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering University College London London United Kingdom 8 Department of Brain and Behavioural Sciences University of Pavia Pavia Italy 1 Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences University College London London United Kingdom 3 Champalimaud Centre for the Unknown Champalimaud Foundation Lisbon Portugal
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Copyright	2018 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine 2018 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. 2018. This article is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Snippet	Purpose Time‐dependence is a key feature of the diffusion‐weighted (DW) signal, knowledge of which informs biophysical modelling. Here, we study... Time-dependence is a key feature of the diffusion-weighted (DW) signal, knowledge of which informs biophysical modelling. Here, we study time-dependence in the... PurposeTime‐dependence is a key feature of the diffusion‐weighted (DW) signal, knowledge of which informs biophysical modelling. Here, we study time‐dependence...
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SubjectTerms	Adult Algorithms Axons Axons - pathology Brain Brain - diagnostic imaging Computer Simulation Cylinders Diffusion Diffusion Magnetic Resonance Imaging diffusion time Feasibility studies Female Full Papers—Biophysics and Basic Biomedical Research Healthy Volunteers Humans Image Processing, Computer-Assisted - methods Kurtosis Magnetic resonance imaging Male microstructure Monte Carlo Method Monte Carlo simulation Monte Carlo simulations Neuroimaging Spinal cord Spinal Cord - diagnostic imaging Substantia alba Time dependence Time Factors white matter
Title	Relevance of time‐dependence for clinically viable diffusion imaging of the spinal cord
URI	https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fmrm.27463 https://www.ncbi.nlm.nih.gov/pubmed/30229564 https://www.proquest.com/docview/2153941395 https://www.proquest.com/docview/2111741036 https://pubmed.ncbi.nlm.nih.gov/PMC6586052
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