# 3D Anatomical Structure-guided Deep Learning for Accurate Diffusion Microstructure Imaging

arXiv: https://arxiv.org/abs/2502.17933 · HTML: https://mlsanity.com/paper/2502.17933/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 25 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI](https://mlsanity.com/paper/2111.07220/) (2021)
- [Knowledge-driven deep learning for fast MR imaging: undersampled MR image reconstruction from supervised to un-supervised learning](https://mlsanity.com/paper/2402.02704/) (2024)
- [Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes](https://mlsanity.com/paper/1706.06682/) (2017)
- [CSR-dMRI: Continuous Super-Resolution of Diffusion MRI with Anatomical Structure-assisted Implicit Neural Representation Learning](https://mlsanity.com/paper/2404.03209/) (2024)
- [Diff5T: Benchmarking Human Brain Diffusion MRI with an Extensive 5.0 Tesla K-Space and Spatial Dataset](https://mlsanity.com/paper/2412.06666/) (2024)
- [Patch-CNN: Training data-efficient deep learning for high-fidelity diffusion tensor estimation from minimal diffusion protocols](https://mlsanity.com/paper/2307.01346/) (2023)
- [RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling under Continuous Representation](https://mlsanity.com/paper/2408.01944/) (2024)
- [AID-DTI: Accelerating High-fidelity Diffusion Tensor Imaging with Detail-Preserving Model-based Deep Learning](https://mlsanity.com/paper/2401.01693/) (2024)
- [DeepMpMRI: Tensor-decomposition Regularized Learning for Fast and High-Fidelity Multi-Parametric Microstructural MR Imaging](https://mlsanity.com/paper/2405.03159/) (2024)

## Abstract (arXiv)

Diffusion magnetic resonance imaging (dMRI) is a crucial non-invasive technique for exploring the microstructure of the living human brain. Traditional hand-crafted and model-based tissue microstructure reconstruction methods often require extensive diffusion gradient sampling, which can be time-consuming and limits the clinical applicability of tissue microstructure information. Recent advances in deep learning have shown promise in microstructure estimation; however, accurately estimating tissue microstructure from clinically feasible dMRI scans remains challenging without appropriate constraints. This paper introduces a novel framework that achieves high-fidelity and rapid diffusion microstructure imaging by simultaneously leveraging anatomical information from macro-level priors and mutual information across parameters. This approach enhances time efficiency while maintaining accuracy in microstructure estimation. Experimental results demonstrate that our method outperforms four state-of-the-art techniques, achieving a peak signal-to-noise ratio (PSNR) of 30.51±0.58 and a structural similarity index measure (SSIM) of 0.97±0.004 in estimating parametric maps of multiple diffusion models. Notably, our method achieves a 15× acceleration compared to the dense sampling approach, which typically utilizes 270 diffusion gradients.