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

Synopsis

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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