Deep Learning Framework for Quantifying High-Resolution MRSI Data of the Human Brain at 7T

Motivation: Addressing challenges in ultra-short TE MRSI data quantification of the Human Brain at 7T utilizing deep learningGoal(s): Develop a Variational Physics-Informed Autoencoder (VPIAE) to enhance MRSI metabolite quantification, ensuring faster, robust, and efficient metabolite mapping with uncertainty estimates. Approach: Combine a variational autoencoder with a physics-informed decoder, training on 7T MRSI brain data, and benchmark against a traditional method (LCModel)Results: VPIAE outperforms conventional MRSI methods in speed by 6 orders of magnitude, offers comparable accuracy, and provides uncertainty estimates for reliable interpretation, promising advancements in clinical and research applications. Impact: VPIAE enables swift MRSI analysis, crucial for clinicians diagnosing neurological conditions and researchers studying metabolic brain changes. It opens avenues for exploring brain metabolite dynamics with greater fidelity and advancing the field's understanding of brain metabolism.