{'fraction_non_alphanumeric': 0.020118343195266272, 'fraction_numerical': 0.0, 'mean_word_length': 6.109243697478991, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Improved efficiency for microstructure imaging using high-dimensional MR correlation spectroscopic imaging

Magnetic Resonance Correlation Spectroscopic Imaging (MR-CSI) is a high-dimensional imaging approach that our group recently introduced for studying microstructure. MR-CSI uses imaging data that is simultaneously encoded with multiple MR contrast mechanisms, and applies a spatial-spectral reconstruction framework to estimate a spectroscopic image with a high-dimensional correlation spectrum for every spatial location. While MR-CSI enables powerful new capabilities for spatially mapping sub-voxel microstructural compartments, the contrast encoding can be slow due to the curse of dimensionality. This paper reviews MR-CSI and investigates a principled estimation theoretic approach that can enable more efficient experiment designs.