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

TL;DR

This paper presents a deep learning framework that integrates anatomical priors to rapidly and accurately estimate brain microstructure from diffusion MRI scans, significantly reducing sampling time while maintaining high fidelity.

Contribution

It introduces a novel method combining macro-level anatomical information and mutual parameter constraints for efficient microstructure imaging from clinically feasible dMRI data.

Findings

Achieves a PSNR of 30.51 and SSIM of 0.97 in microstructure estimation.

Outperforms four state-of-the-art methods in accuracy.

Provides a 15-fold acceleration over traditional dense sampling methods.

Abstract

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$\pm$0.58 and a structural similarity index measure (SSIM) of 0.97$\pm$0.004 in estimating parametric maps of multiple diffusion models. Notably, our method achieves a 15$\times$ acceleration compared to the dense sampling approach, which typically utilizes 270 diffusion gradients.