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Generalised boundary shift integral for longitudinal assessment of spinal cord atrophy

Prados, F; Moccia, M; Johnson, A; Yiannakas, M; Grussu, F; Cardoso, MJ; Ciccarelli, O; ... Wheeler-Kingshott, C; + view all (2020) Generalised boundary shift integral for longitudinal assessment of spinal cord atrophy. Neuroimage , 209 , Article 116489. 10.1016/j.neuroimage.2019.116489. Green open access

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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.

Type: Article
Title: Generalised boundary shift integral for longitudinal assessment of spinal cord atrophy
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.neuroimage.2019.116489
Publisher version: https://doi.org/10.1016/j.neuroimage.2019.116489
Language: English
Additional information: © 2019 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Brain Repair and Rehabilitation
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neuroinflammation
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery-pp.ucl.ac.uk/id/eprint/10088799
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