Abstract
Neurodegenerative disorders like Huntington's Disease (HD) urgently require specific, non-invasive imaging biomarkers for early diagnosis. While standard diffusion MRI detects pathology, it lacks cellular specificity. Soma And Neurite Density Imaging (SANDI) is a promising imaging technique that separates the MRI signal from cell bodies (soma) and neurites, offering in vivo histological insights. However, SANDI's preclinical application in HD is hampered by prohibitive scan times, low spatial resolution, and a lack of direct biological validation. This project aims to develop, validate, and apply a cutting-edge computational framework for high-precision, high-resolution, and biologically interpretable SANDI in an HD mouse model through three synergistic objectives: 1) Improving precision: determine acquisition parameters that maximize SANDI readout precision without increasing scan time; 2) Enhancing resolution: Engineer a novel Super-Resolution Reconstruction framework to combine multiple low-resolution scans into high resolution SANDI maps, revealing fine cytoarchitecture; 3) Improving biological interpretation: Longitudinally apply the "Super-SANDI" pipeline to an HD mouse cohort and rigorously validating against matched, 3D whole-brain histology using light-sheet microscopy. By bridging MRI physics, computational modeling, and neurobiology, this research will transform SANDI into a highly specific, robust biomarker for mapping complex microstructural neurodegeneration.
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