3D Anatomical Structure-guided Deep Learning for Accurate Diffusion Microstructure Imaging
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910843375976448 |
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| author | Ma, Xinrui Cheng, Jian Fan, Wenxin Wu, Ruoyou Ye, Yongquan Wang, Shanshan |
| author_facet | Ma, Xinrui Cheng, Jian Fan, Wenxin Wu, Ruoyou Ye, Yongquan Wang, Shanshan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_17933 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 3D Anatomical Structure-guided Deep Learning for Accurate Diffusion Microstructure Imaging Ma, Xinrui Cheng, Jian Fan, Wenxin Wu, Ruoyou Ye, Yongquan Wang, Shanshan Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | 3D Anatomical Structure-guided Deep Learning for Accurate Diffusion Microstructure Imaging |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.17933 |