Joint q-Space Sampling Optimization and Reconstruction Framework for Accurate and Fast Diffusion Magnetic Resonance Imaging

Motivation: Current deep learning methods for fast dMRI signal estimation are limited in the accuracy and imaging speed. Goal(s): Our goal is to enhance the quality of signal estimation and imaging speed for dMRI, by introducing a new deep learning method. Approach: Our approach fully utilizes the information in both the diffusion gradient domain and spatial domain to design a joint sparse sampling optimization and reconstruction deep learning framework, along with a specifically designed loss function. Results: The proposed method achieved up to 15x acceleration while maintaining high estimation accuracy, increasing SSIM by 7% compared with other q-space learning approaches. Impact: The dMRI signal estimation performance of our method is promising, as it incorporates domain knowledge into the deep learning process. This approach improves the acquisition and reconstruction workflow of dMRI, benefiting clinical applicability.