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Abstract #0304

Comparison of Self-Supervised Image Reconstruction Methods for Undersampled  Image Reconstruction: Validation in a Realistic Setting

Thomas Yu1,2, Tom Hilbert1,3,4, Gian Franco Piredda1,3,4, Erick Canales-Rodrıguez1, Tobias Kober1,3,4, and Jean-Philippe Thiran1,4
1Signal Processing Lab 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 2Medical Image Analysis Laboratory, Center for Biomedical Imaging (CIBM), University of Lausanne, Lausanne, Switzerland, 3Advanced Clinical Imaging Technology, Siemens Healthcare, Lausanne, Switzerland, 4Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland


Self-supervised reconstruction methods for undersampled acquisitions are becoming increasingly used. We compare different self-supervised reconstruction methods using fully sampled and prospectively/retrospectively accelerated data; we find that prospective and retrospective reconstructions can differ significantly in quantitative metrics and perceptual quality. To test the methods’ generalizability, prospectively accelerated data from multiple field strengths is reconstructed without retraining/retuning. We find that no-reference image quality metrics can distinguish state of the art methods from the baseline, albeit with ambiguity between the state of the art methods.

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