Abstract #4048

AI assisted upscaling of low-resolution cardiac MRI reduces acquisition time and yields qualitatively comparable images to standard techniques

Dmitrij Kravchenko^1,2, Alexander Isaak^1,2, Narine Mesropyan^1,2, Claus Christian Pieper¹, Daniel Kuetting^1,2, Leon M. Bischoff^1,2, Shuo Zhang³, Christoph Katemann³, Johannes M. Peeters⁴, Oliver Weber³, Ulrike Attenberger¹, and Julian Luetkens^1,2

¹Diagnostic and interventional radiology, University Hospital Bonn, Bonn, Germany, ²Quantitative Imaging Laboratory Bonn, Bonn, Germany, ³Philips GmbH Market DACH, Hamburg, Germany, ⁴Philips MR Clinical Science, Best, Netherlands

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Cardiovascular

AI assisted upscaling of low-resolution cine bSSFP images yields comparable image quality to conventional images with no clinically significant difference in volumetric data at a reduction of acquisition time by a factor of 1.5 to 2.

Keywords: Artificial intelligence, acceleration, Superresolution, cardiac MRI

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