Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy

The present study evaluated the impact of deep-learning image reconstruction (DLIR) on noise, image quality, and diagnostic accuracy. In 43 patients who underwent clinically indicated coronary CT angiography and invasive coronary angiography, image quality was improved by up to 62% at similar noise levels. In addition, DLIR-H yielded the highest noise reduction (up to 43%) and best image quality (improvement of up to 138%). More importantly, sensitivity (92% vs. 88%), specificity (73% vs. 73%) and diagnostic accuracy (82% vs. 80%) of DLIR were at least non-inferior to ASiR-V.

Source: Journal of Cardiovascular Computed Tomography - January 12, 2020 Category: Radiology Authors: Dominik C. Benz, Georgios Benetos, Georgios Rampidis, Elia von Felten, Adam Bakula, Aleksandra Susta, Ken Kudura, Michael Messerli, Tobias A. Fuchs, Catherine Gebhard, Aju P. Pazhenkottil, Philipp A. Kaufmann, Ronny R. Buechel Source Type: research