Highly accelerated MR parametric mapping by undersampling the k-space and reducing the contrast number simultaneously with deep learning

• URL: http://arxiv.org/abs/2112.00730v1
• Date: Wed, 1 Dec 2021 07:29:29 GMT
• Title: Highly accelerated MR parametric mapping by undersampling the k-space and reducing the contrast number simultaneously with deep learning
• Authors: Yanjie Zhu, Haoxiang Li, Yuanyuan Liu, Muzi Guo, Guanxun Cheng, Gang Yang, Haifeng Wang and Dong Liang
• Abstract summary: We propose a novel deep learning-based method called RG-Net (reconstruction and generation network) for highly accelerated MR parametric mapping. The framework consists of a reconstruction module and a generative module. RG-Net yields a high-quality T1rho map at a high acceleration rate of 17.
• Score: 18.839338336031577
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Purpose: To propose a novel deep learning-based method called RG-Net (reconstruction and generation network) for highly accelerated MR parametric mapping by undersampling k-space and reducing the acquired contrast number simultaneously. Methods: The proposed framework consists of a reconstruction module and a generative module. The reconstruction module reconstructs MR images from the acquired few undersampled k-space data with the help of a data prior. The generative module then synthesizes the remaining multi-contrast images from the reconstructed images, where the exponential model is implicitly incorporated into the image generation through the supervision of fully sampled labels. The RG-Net was evaluated on the T1\r{ho} mapping data of knee and brain at different acceleration rates. Regional T1\r{ho} analysis for cartilage and the brain was performed to access the performance of RG-Net. Results: RG-Net yields a high-quality T1\r{ho} map at a high acceleration rate of 17. Compared with the competing methods that only undersample k-space, our framework achieves better performance in T1\r{ho} value analysis. Our method also improves quality of T1\r{ho} maps on patient with glioma. Conclusion: The proposed RG-Net that adopted a new strategy by undersampling k-space and reducing the contrast number simultaneously for fast MR parametric mapping, can achieve a high acceleration rate while maintaining good reconstruction quality. The generative module of our framework can also be used as an insert module in other fast MR parametric mapping methods. Keywords: Deep learning, convolutional neural network, fast MR parametric mapping

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