A generalizable deep learning system for cardiac MRI.

Rohan Shad¹, Cyril Zakka², Dhamanpreet Kaur², Mrudang Mathur², Robyn Fong², Joseph Cho², Ross Warren Filice³, John Mongan⁴, Kimberly Kallianos⁴, Nishith Khandwala⁵, David Eng⁵, Matthew Leipzig², Walter R Witschey⁶, Alejandro de Feria⁷, Victor A Ferrari⁷, Euan A Ashley⁸, Michael A Acker⁹, Curtis Langlotz¹⁰, William Hiesinger¹¹

¹Division of Cardiovascular Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, PA, USA. rohan.shad@pennmedicine.upenn.edu.

²Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, USA.

³Department of Radiology, Medstar Georgetown University Hospital, Washington, DC, USA.

⁴Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.

⁵Bunkerhill Health, San Francisco, CA, USA.

⁶Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.

⁷Division of Cardiovascular Medicine, Department of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

⁸Division of Cardiovascular Medicine, Department of Medicine, Genetics, and Biomedical Data Science, Stanford University, Stanford, CA, USA.

⁹Division of Cardiovascular Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, PA, USA.

¹⁰Department of Radiology, Medicine, and Biomedical Data Science, Stanford University, Stanford, CA, USA.

¹¹Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, USA. willhies@stanford.edu.

PubMed DOI

Abstract

Cardiac MRI allows for a comprehensive assessment of myocardial structure, function and tissue characteristics. Here we describe a foundational vision system for cardiac MRI, capable of representing the breadth of human cardiovascular disease and health. Our deep-learning model is trained via self-supervised contrastive learning, in which visual concepts in cine-sequence cardiac MRI scans are lear…

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