DeepMpMRI: Tensor-decomposition Regularized Learning for Fast and High-Fidelity Multi-Parametric Microstructural MR Imaging

• URL: http://arxiv.org/abs/2405.03159v1
• Date: Mon, 6 May 2024 04:36:02 GMT
• Title: DeepMpMRI: Tensor-decomposition Regularized Learning for Fast and High-Fidelity Multi-Parametric Microstructural MR Imaging
• Authors: Wenxin Fan, Jian Cheng, Cheng Li, Xinrui Ma, Jing Yang, Juan Zou, Ruoyou Wu, Zan Chen, Yuanjing Feng, Hairong Zheng, Shanshan Wang,
• Abstract summary: This paper proposes a unified framework for fast and high-fidelity multi-parametric estimation from various diffusion models. DeepMpMRI is equipped with a newly designed tensor-decomposition-based regularizer to effectively capture fine details.
• Score: 15.408939800451696
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep learning has emerged as a promising approach for learning the nonlinear mapping between diffusion-weighted MR images and tissue parameters, which enables automatic and deep understanding of the brain microstructures. However, the efficiency and accuracy in the multi-parametric estimations are still limited since previous studies tend to estimate multi-parametric maps with dense sampling and isolated signal modeling. This paper proposes DeepMpMRI, a unified framework for fast and high-fidelity multi-parametric estimation from various diffusion models using sparsely sampled q-space data. DeepMpMRI is equipped with a newly designed tensor-decomposition-based regularizer to effectively capture fine details by exploiting the correlation across parameters. In addition, we introduce a Nesterov-based adaptive learning algorithm that optimizes the regularization parameter dynamically to enhance the performance. DeepMpMRI is an extendable framework capable of incorporating flexible network architecture. Experimental results demonstrate the superiority of our approach over 5 state-of-the-art methods in simultaneously estimating multi-parametric maps for various diffusion models with fine-grained details both quantitatively and qualitatively, achieving 4.5 - 22.5$\times$ acceleration compared to the dense sampling of a total of 270 diffusion gradients.

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