High-Dimensional MR Spatiospectral Imaging by Integrating Physics-Based Modeling and Data-Driven Machine Learning: Current progress and future directions

Magnetic resonance spectroscopic imaging (MRSI) offers a unique molecular window into the physiological and pathological processes in the human body. However, the applications of MRSI have been limited by a number of long-standing technical challenges due to the high dimensionality and low signal-to-noise ratio (SNR). Recent technological developments integrating physics-based modeling and data-driven machine learning that exploit the unique physical and mathematical properties of MRSI signals have demonstrated impressive performance in addressing these challenges for rapid high-resolution quantitative MRSI. This article provides a systematic review of recent progress in the context of MRSI physics and offers perspectives on promising future directions.