Magnetic Resonance Spectroscopic Imaging

Magnetic Resonance Spectroscopic Imaging (MRSI) provides non-invasive metabolic maps of tissues, but suffers from limitations like low signal-to-noise ratio and spectral overlap. Current research heavily utilizes deep learning, employing convolutional neural networks (CNNs) and vision transformers (ViTs) to address these challenges, focusing on improved signal processing (e.g., water and lipid removal), faster reconstruction, and enhanced spatial resolution through super-resolution techniques. These advancements improve metabolite quantification accuracy and enable faster, higher-resolution imaging, ultimately increasing the clinical utility and throughput of MRSI for diagnosing and monitoring neurological diseases and cancers.