A peer-reviewed research article on Deep Learning, reveals that an Artificial intelligence (AI) algorithm can achieve similar results to a human expert in terms of assessing image quality on 3D whole-heart cardiac magnetic resonance (MR) imaging.
The article, entitled “Deep Learning to Automate Reference-Free Image Quality Assessment of Whole Heart MR Images”, was published May 27, 2020 in Radiology: Artificial Intelligence. The abstract reports that an artificial intelligence–based algorithm can mimic expert visual image quality assessment and allows for fast and automated image quality grading of three-dimensional whole-heart MR images.
The scientific article was picked up by AuntMinnie.com, a comprehensive community Internet site for radiologists and related professionals in the medical imaging industry. The article, “AI can handle quality assessment of 3D cardiac MRI” was posted on 2 June, 2020.
Professor Matthias Stuber, Section Head of CIBM MRI CHUV-UNIL and senior author of the study said,
“The fact the media has picked up on the article is a testimony of our collaborative, translational and interdisciplinary work. I think it nicely exemplifies what the CIBM is about, and how such collaborative research can thrive in a hospital setting. In fact, this concerns data that we had begun acquiring some seven years ago, with a technique that we developed 10 years ago, and now we were able to harvest these data and perform some AI research.”
The findings were published through a collaboration between researchers and clinicians at the CHUV in Radiology and Cardiology, with contributions of the CIBM Signal Processing EPFL UNIGE Section (Dimitri Van De Ville), and a long standing Siemens Healthcare collaboration (Davide Piccini & Tobias Kober).
Citation: RSNA, Radiology: Artificial Intelligence, “Deep Learning to Automate Reference-Free Image Quality Assessment of Whole-Heart MR Images, Davide Piccini*, Robin Demesmaeker*, John Heerfordt, Jérôme Yerly, Lorenzo Di Sopra, Pier Giorgio Masci, Juerg Schwitter, Dimitri Van De Ville, Jonas Richiardi, Tobias Kober, Matthias Stuber.
* D.P. and R.D. contributed equally to this work.