Accelerating multi-directional diffusion MRI through patch-based joint reconstruction

Diffusion magnetic resonance imaging (dMRI) is a valuable technique for studying tissue microstructure and connectivity in the brain. However, acquiring high-resolution dMRI data is time-consuming, limiting its clinical applicability. Traditional parallel imaging techniques can accelerate the acquisition of dMRI, but they are constrained by the geometry factor. In this study, we propose a novel patch-based multiple diffusion directions joint reconstruction method that simultaneously capitalizes on the intra- and inter-image correlation across multiple diffusion directions by grouping similar 3D image patches and then enforces the sparsity of these groups in sensitivity encoding (SENSE) reconstruction, termed PB-SENSE. The simulation and in vivo experiments demonstrated that the proposed method can achieve high-quality images comparable to those obtained from fully sampled data, even with an acceleration of 5. This suggests that the proposed method has the potential to enhance the practical application of high-resolution diffusion imaging.

Authors

• Zhongbiao Xu
• Rongli Zhang
• Wei Huang Nanchang University Yichun University
• Guanhua Deng
• Xiaoyun Liang
• Li Guo School of Artificial Intelligence, Beijing University of Posts and Telecommunications
• Junying Cheng
• Yaohui Wang Shanghai AI Laboratory
• Feng Liu
• Zhaolin Chen
• Zhifeng Chen

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