MRI Field-transfer Reconstruction with Limited Data: Regularization by
  Neural Style Transfer
Guoyao Shen Yancheng Zhu Hernan Jara Sean B. Andersson Chad W. Farris Stephan Anderson Xin Zhang
Recent works have demonstrated success in MRI reconstruction using deep
learning-based models. However, most reported approaches require training on a
task-specific, large-scale dataset. Regularization by denoising (RED) is a
general pipeline which embeds a denoiser as a prior for image reconstruction.
The potential of RED has been demonstrated for multiple image-related tasks
such as denoising, deblurring and super-resolution. In this work, we propose a
regularization by neural style transfer (RNST) method to further leverage the
priors from the neural transfer and denoising engine. This enables RNST to
reconstruct a high-quality image from a noisy low-quality image with different
image styles and limited data. We validate RNST with clinical MRI scans from
1.5T and 3T and show that RNST can significantly boost image quality. Our
results highlight the capability of the RNST framework for MRI reconstruction
and the potential for reconstruction tasks with limited data.