3D Anatomical Structure-guided Deep Learning for Accurate Diffusion Microstructure Imaging
This work addresses the problem of time-consuming microstructure imaging for clinical applications, offering a significant speed-up while maintaining accuracy.
The paper tackles the challenge of accurately estimating tissue microstructure from clinically feasible diffusion MRI scans by introducing a novel framework that leverages anatomical priors and mutual information, achieving a 15× acceleration and outperforming state-of-the-art methods with a PSNR of 30.51±0.58 and SSIM of 0.97±0.004.
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.