Deep Learning-based Voxel-wise Estimation of Vessel Size Distribution from MR Gradient Echo Sampling of the Free Induction Decay and Spin Echo

Motivation: DSC-MRI is vital for diagnosing brain pathologies. Our goal is to harness GESFIDE MR signal evolution through deep learning (DL) to estimate vessel size distribution (VSD), which would allow us to explore deeper into the complexities of tumor vascular microstructure and other pathologies. Goal(s): Our objective is to assess the capabilities of GESFIDE in providing voxel-wise VSD estimate. Approach: We simulated GESFIDE signals with the FPFDM method. A DL network, VSD estimator (VSDE), was trained to estimate VSDs. Results: Our validation demonstrates GESFIDE's promise in assessing VSD as a distinct contrast mechanism, offering insights into tumor microstructure and pathologies. Impact: Our study reveals GESFIDE's potential for VSD estimation. Leveraging this unique contrast mechanism allows in-depth exploration of tumor microstructure and other pathologies through histogram analysis. Ongoing research aims to broaden VSD applicability and optimize GESFIDE parameters.