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

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.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 trained and tested on the Tmapping data from 8 volunteers at net acceleration rates of 17, respectively. Regional Tanalysis for cartilage and the brain was performed to assess the performance of RG-Net.RG-Net yields a high-quality Tmap at a high acceleration rate of 17. Compared with the competing methods that only undersample k-space, our framework achieves better performance in Tvalue analysis.The proposed RG-Net can achieve a high acceleration rate while maintaining good reconstruction quality by undersampling k-space and reducing the contrast number simultaneously for fast MR parametric mapping. The generative module of our framework can also be used as an insertable module in other fast MR parametric mapping methods.