  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
