MR Vascular Fingerprinting with Deep Learning to Estimate Brain Physiological Parameters

Motivation: Magnetic resonance vascular fingerprinting quantitatively measures microvascular blood oxygen saturation, cerebral blood volume, and vessel radii. Matching simulated signals to in-vivo data is computationally expensive, therefore, we leverage deep learning to alleviate the burden. Goal(s): Build a model to simultaneously and accurately estimate three physiological parameters from a GESFIDE (gradient echo sampling of free induction decay and echo) sequence. Approach: The model has two fully-connected layers to estimate three parameters, and was validated with synthetic signals and healthy subject parameter mapping. Results: The model achieves comparable root-mean-squared-error to traditional fingerprint matching in test signals. We show physiological reasonable values in healthy subject maps. Impact: We leverage deep learning in MR vascular fingerprinting to simultaneously estimate brain physiological parameters through training with simulated vascular dictionaries. The model enables quantitative measurements of oxygenation, blood volume and vessel radius in test signals and in-vivo mapping.