AID-DTI: fast and high-fidelity diffusion tensor imaging with detail-preserving model-based deep learning

Motivation: Existing methods tend to suffer from Rician noise, leading to detail loss during the reconstruction of DTI-derived parametric maps. This issue becomes particularly pronounced when sparsely sampled q-space data are used. Goal(s): Our goal was to facilitate fast and high-fidelity estimation of DTI metrics. Approach: We propose a novel SVD-based regularizer, which can effectively preserve fine details while suppressing noise during network training. Results: Experimental results consistently demonstrate that the proposed method estimates DTI parameter maps with finer details, outperforming current state-of-the-art methods. Impact: The proposed method may facilitate fast and high-fidelity DTI with a newly designed SVD-based regularizer, and it has a potential to become a practical tool in clinical and neuroscientific applications.