Highly accelerated MR parametric mapping by undersampling the k-space and reducing the contrast number simultaneously with deep learning
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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 T1h̊o̊ mapping data of knee and brain at different acceleration rates. Regional T1h̊o̊ analysis for cartilage and the brain was performed to access the performance of RG-Net. Results: RG-Net yields a high-quality T1h̊o̊ map at a high acceleration rate of 17. Compared with the competing methods that only undersample k-space, our framework achieves better performance in T1h̊o̊ value analysis. Our method also improves quality of T1h̊o̊ 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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