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Wavelet-Driven Decoupling and Physics-Informed Mapping Network for Accelerated Multi-parametric MR Imaging
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DOI：10.1007/978-3-032-04927-8_63.png)
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Abstract
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Multi-parametric magnetic resonance imaging (MRI) is an advanced MRI technique that can provide multiple quantitative maps simultaneously based on acquired multi-echo images. However, the lengthy scan time often limits its application. Accelerated multiparametric MRI using deep learning is of great interest. The existing studies have two limitations: 1) inefficient use of the multi-echo information; 2) lack of physical prior for parametric mapping. To address these issues, in this work, we propose a novel decoupling-driven and physicsinformed reconstruction network for accelerated multi-parametric MRI. Specifically, to better align and integrate multi-echo information, we propose a novel decoupling technique consisting of wavelet-driven decoupling module, contrastive and echo-dependent decoupling losses, such that the multi-echo features can be effectively decoupled into echo-dependent and echo-independent components. Only the echo-independent features are fused across multiple echoes. Besides, Bloch equations are incorporated as physical priors to guide the parametric mapping network. Experimental results on our in-house data (12-echo sequence) show that our method outperforms the state-of-the-art methods by 1.54% in average SSIM and 1.70 dB in average PSNR for 4x acceleration, which significantly advances the performance limitation for multi-parametric MRI. Our code is available at https://github.com/IDEARL23/WDPM-Net.
Keywords:
Multi-parametric MRI
Quantitative MRI
Wavelet-driven
Feature decoupling
Physics-informed mapping
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