2025/02/14 by Yan Chen, Jyothi Rikhab Chand, Chen, Yan +7
Materials Science · Medicine · Neuroscience · #Brain Tumor Detection and Classification #FOS: Electrical engineering #FOS: Physical sciences #Image and Video Processing (eess.IV) #Machine Learning in Materials Science #Medical Physics (physics.med-ph) #Radiomics and Machine Learning in Medical Imaging #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2502.10580
openalex publication_date 2025/02/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Multi-contrast MRI methods acquire multiple images with different contrast weightings, which are used for the differentiation of the tissue types or quantitative mapping. However, the scan time needed to acquire multiple contrasts is prohibitively long for 3D acquisition schemes, which can offer isotropic image resolution. While deep learning-based methods have been extensively used to accelerate 2D and 2D + time problems, the high memory demand, computation time, and need for large training data sets make them challenging for large-scale volumes. To address these challenges, we generalize the plug-and-play multi-scale energy-based model (MuSE) to a regularized subspace recovery setting, where we jointly regularize the 3D multi-contrast spatial factors in a subspace formulation. The explicit energy-based formulation allows us to use variable splitting optimization methods for computationally efficient recovery.