Single Image Compressed Sensing MRI via a Self-Supervised Deep Denoising
  Approach
Marlon Bran Lorenzana Feng Liu Shekhar S. Chandra
Popular methods in compressed sensing (CS) are dependent on deep learning
(DL), where large amounts of data are used to train non-linear reconstruction
models. However, ensuring generalisability over and access to multiple datasets
is challenging to realise for real-world applications. To address these
concerns, this paper proposes a single image, self-supervised (SS) CS-MRI
framework that enables a joint deep and sparse regularisation of CS artefacts.
The approach effectively dampens structured CS artefacts, which can be
difficult to remove assuming sparse reconstruction, or relying solely on the
inductive biases of CNN to produce noise-free images. Image quality is thereby
improved compared to either approach alone. Metrics are evaluated using
Cartesian 1D masks on a brain and knee dataset, with PSNR improving by 2-4dB on
average.