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Abstract #1870

SSFD: Self-Supervised Feature Distance Outperforms Conventional MR Image Reconstruction Quality Metrics

Philip M. Adamson1, Jeffrey Dominic1, Arjun Desai1, Christian Bluethgen2, Jeff P. Wood3, Ali B. Syed2, Robert Boutin2, Kathryn J. Stevens2, Daniel Spielman2, Shreyas Vasanawala2, John M. Pauly1, Akshay S. Chaudhari2, and Beliz Gunel1
1Department of Electrical Engineering, Stanford University, Palo Alto, CA, United States, 2Department of Radiology, Stanford University, Palo Alto, CA, United States, 3Austin Radiological Association, Austin, TX, United States


Evaluation of accelerated magnetic resonance imaging (MRI) reconstruction methods is imperfect due to the discordance between quantitative image quality metrics (IQMs) and radiologist-perceived image quality. Self-supervised learning (SSL) is a deep learning (DL) method that has become a popular pre-training tool due to its ability to capture generalizable and domain-specific feature representations of the underlying data without the need for labels. In this study, we derive a data-driven self-supervised feature distance (SSFD) IQM to assess MR image reconstruction quality. We demonstrate that SSFD is more highly correlated to three radiologist’s perceived image quality on DL-based sparse reconstructions than conventional IQMs.

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