Generalised boundary shift integral for longitudinal assessment of spinal cord atrophy
Ferran Prados,
Marcello Moccia,
Aubrey Johnson,
Marios Yiannakas,
Francesco Grussu,
Manuel Jorge Cardoso,
Olga Ciccarelli,
Sebastien Ourselin,
Frederik Barkhof,
Claudia Wheeler-Kingshott
Affiliations
Ferran Prados
Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, 90 High Holborn, London, WC1V 6LJ, UK; NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, Russell Square, London, WC1B 5EH, UK; e-health Center, Universitat Oberta de Catalunya, Barcelona, Spain; Corresponding author. 1st Floor, 90 High Holborn, London, WC1V 6LJ, UK.
Marcello Moccia
NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, Russell Square, London, WC1B 5EH, UK; Multiple Sclerosis Clinical Care and Research Centre, Department of Neurosciences, Federico II University, Naples, Italy
Aubrey Johnson
Smith College, Northampton, MA, USA
Marios Yiannakas
NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, Russell Square, London, WC1B 5EH, UK
Francesco Grussu
NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, Russell Square, London, WC1B 5EH, UK; Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, 90 High Holborn, London, WC1V 6LJ, UK
Manuel Jorge Cardoso
Department of Biomedical Engineering & Imaging Sciences, King’s College London, UK
Olga Ciccarelli
NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, Russell Square, London, WC1B 5EH, UK
Sebastien Ourselin
Department of Biomedical Engineering & Imaging Sciences, King’s College London, UK
Frederik Barkhof
Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, 90 High Holborn, London, WC1V 6LJ, UK; NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, Russell Square, London, WC1B 5EH, UK; Dept. of Radiology & Nuclear Medicine, VU University Medical Centre, Amsterdam, the Netherlands
Claudia Wheeler-Kingshott
NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, Russell Square, London, WC1B 5EH, UK; Brain MRI 3T , UKCenter, IRCCS Mondino Foundation, Pavia, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
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.
