Synthetic Generation and Latent Projection Denoising of Rim Lesions in Multiple Sclerosis
Journal:
arXiv
Published Date:
May 29, 2025
Abstract
Quantitative susceptibility maps from magnetic resonance images can provide
both prognostic and diagnostic information in multiple sclerosis, a
neurodegenerative disease characterized by the formation of lesions in white
matter brain tissue. In particular, susceptibility maps provide adequate
contrast to distinguish between "rim" lesions, surrounded by deposited
paramagnetic iron, and "non-rim" lesion types. These paramagnetic rim lesions
(PRLs) are an emerging biomarker in multiple sclerosis. Much effort has been
devoted to both detection and segmentation of such lesions to monitor
longitudinal change. As paramagnetic rim lesions are rare, addressing this
problem requires confronting the class imbalance between rim and non-rim
lesions. We produce synthetic quantitative susceptibility maps of paramagnetic
rim lesions and show that inclusion of such synthetic data improves classifier
performance and provide a multi-channel extension to generate accompanying
contrasts and probabilistic segmentation maps. We exploit the projection
capability of our trained generative network to demonstrate a novel denoising
approach that allows us to train on ambiguous rim cases and substantially
increase the minority class. We show that both synthetic lesion synthesis and
our proposed rim lesion label denoising method best approximate the unseen rim
lesion distribution and improve detection in a clinically interpretable manner.
We release our code and generated data at https://github.com/agr78/PRLx-GAN
upon publication.
