# AID-DTI: Accelerating High-fidelity Diffusion Tensor Imaging with Detail-Preserving Model-based Deep Learning

arXiv: https://arxiv.org/abs/2401.01693 · HTML: https://mlsanity.com/paper/2401.01693/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 17 references that are in the map.
- First posted on arXiv 3 Jan 2024.

## Builds on (most cited first, 1 of 1)

- [A microstructure estimation Transformer inspired by sparse representation for diffusion MRI](https://mlsanity.com/paper/2205.06450/) (2022)

## Built on by (most cited first, 1 of 1)

- [3D Anatomical Structure-guided Deep Learning for Accurate Diffusion Microstructure Imaging](https://mlsanity.com/paper/2502.17933/) (2025)

## Abstract (arXiv)

Deep learning has shown great potential in accelerating diffusion tensor imaging (DTI). Nevertheless, existing methods tend to suffer from Rician noise and detail loss in reconstructing the DTI-derived parametric maps especially when sparsely sampled q-space data are used. This paper proposes a novel method, AID-DTI (Accelerating hIgh fiDelity Diffusion Tensor Imaging), to facilitate fast and accurate DTI with only six measurements. AID-DTI is equipped with a newly designed Singular Value Decomposition (SVD)-based regularizer, which can effectively capture fine details while suppressing noise during network training. Experimental results on Human Connectome Project (HCP) data consistently demonstrate that the proposed method estimates DTI parameter maps with fine-grained details and outperforms three state-of-the-art methods both quantitatively and qualitatively.