Joint Registration and Change Detection in Longitudinal Brain MRI

Abstract

Automatic change detection in longitudinal brain MRI classically consists in a sequential pipeline where registration is estimated as a pre-processing step before detecting pathological changes such as lesion appearance. Deformable registration can advantageously be considered over rigid or affine transforms in the presence of geometrical distortions or brain atrophy to reduce false positive detections. However, this may be at the cost of underestimating the changes of interest due to the over-compensation of the differences between baseline and follow-up studies. In this article, we propose to overcome this limitation with a framework where deformable registration and lesion change are estimated jointly. We compare this joint framework with its sequential counterpart based on either affine or deformable registration. We demonstrate the benefits for detecting multiple sclerosis lesion evolutions on both synthetic and real data. © 2020 IEEE.