Motivation: This study investigates optimized MAP-MRI methods (MAPL, CMAP, CMAPL) to enhance brain microstructure analysis, addressing limitations of signal sparsity and noise, which is crucial for detecting physiological and pathological changes. Goal(s): This study aims to enhance MAP-MRI accuracy in detecting brain microstructural changes by comparing optimized methods. Approach: We examined three MAP-MRI algorithms on MRI scans, assessing image quality through WSNR and subjective ratings, using ANOVA, Kruskal-Wallis, Κ tests, and GRF-corrected t-tests. Results: MAPL outperformed CMAP and CMAPL in image quality for RTOP, RTAP, MSD, and QIV mappings, effectively displaying bilateral dorsal visual pathways and regions linked to language, emotion, and decision-making. Impact: This study highlights the strengths and weaknesses of the MAPL, CMAP, and CMAPL algorithms in image quality and ability to reflect brain microstructural changes, offering valuable insights for future research on brain pathology and physiological changes using DWI technology
Ling et al. (Tue,) studied this question.