• Corpus ID: 246430767

Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI

@article{Yu2022ValidationAG,
  title={Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI},
  author={Thomas Yu and Tom Hilbert and Gian Franco Piredda and Arun A. Joseph and Gabriele Bonanno and Salim Zenkhri and Patrick Omoumi and Meritxell Bach Cuadra and Erick Jorge Canales-Rodr{\'i}guez and Thomas Kober and Jean-Philippe Thiran},
  journal={ArXiv},
  year={2022},
  volume={abs/2201.12535}
}
Deep learning methods have become the state of the art for undersampled MR reconstruction. Particularly for cases where it is infeasible or impossible for ground truth, fully sampled data to be acquired, self-supervised machine learning methods for reconstruction are becoming increasingly used. However potential issues in the validation of such methods, as well as their generalizability, remain underexplored. In this paper, we investigate important aspects of the validation of self-supervised… 
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