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Grussu F, Ianuş A, Tur C, Prados F, Schneider T, Kaden E, Ourselin S, Drobnjak I, Zhang H, Alexander DC, Gandini Wheeler-Kingshott CAM. Relevance of time-dependence for clinically viable diffusion imaging of the spinal cord. Magn Reson Med 2018; 81:1247-1264. [PMID: 30229564 PMCID: PMC6586052 DOI: 10.1002/mrm.27463] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Revised: 06/28/2018] [Accepted: 06/29/2018] [Indexed: 12/17/2022]
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.
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Affiliation(s)
- Francesco Grussu
- Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom.,Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Andrada Ianuş
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.,Champalimaud Centre for the Unknown, Champalimaud Foundation, Lisbon, Portugal
| | - Carmen Tur
- Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Ferran Prados
- Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom.,Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | | | - Enrico Kaden
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Sébastien Ourselin
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Ivana Drobnjak
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Hui Zhang
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.,Clinical Imaging Research Centre, National University of Singapore, Singapore, Singapore
| | - Claudia A M Gandini Wheeler-Kingshott
- Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom.,Brain MRI 3T Research Centre, C. Mondino National Neurological Institute, Pavia, Italy.,Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
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