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Generalised boundary shift integral for longitudinal assessment of spinal cord atrophy.

Authors :
Prados F
Moccia M
Johnson A
Yiannakas M
Grussu F
Cardoso MJ
Ciccarelli O
Ourselin S
Barkhof F
Wheeler-Kingshott C
Source :
NeuroImage [Neuroimage] 2020 Apr 01; Vol. 209, pp. 116489. Date of Electronic Publication: 2019 Dec 24.
Publication Year :
2020

Abstract

Spinal cord atrophy measurements obtained from structural magnetic resonance imaging (MRI) are associated with disability in many neurological diseases and serve as in vivo biomarkers of neurodegeneration. Longitudinal spinal cord atrophy rate is commonly determined from the numerical difference between two volumes (based on 3D surface fitting) or two cross-sectional areas (CSA, based on 2D edge detection) obtained at different time-points. Being an indirect measure, atrophy rates are susceptible to variable segmentation errors at the edge of the spinal cord. To overcome those limitations, we developed a new registration-based pipeline that measures atrophy rates directly. We based our approach on the generalised boundary shift integral (GBSI) method, which registers 2 scans and uses a probabilistic XOR mask over the edge of the spinal cord, thereby measuring atrophy more accurately than segmentation-based techniques. Using a large cohort of longitudinal spinal cord images (610 subjects with multiple sclerosis from a multi-centre trial and 52 healthy controls), we demonstrated that GBSI is a sensitive, quantitative and objective measure of longitudinal spinal cord volume change. The GBSI pipeline is repeatable, reproducible, and provides more precise measurements of longitudinal spinal cord atrophy than segmentation-based methods in longitudinal spinal cord atrophy studies.<br /> (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Details

Language :
English
ISSN :
1095-9572
Volume :
209
Database :
MEDLINE
Journal :
NeuroImage
Publication Type :
Academic Journal
Accession number :
31877375
Full Text :
https://doi.org/10.1016/j.neuroimage.2019.116489