Super-Resolution Deep Learning Reconstruction for Improved Image Quality of Coronary CT Angiography
Masafumi Takafuji, Kakuya Kitagawa, Sachio Mizutani, Akane Hamaguchi, Ryosuke Kisou, Kotaro Iio, Kazuhide Ichikawa, Daisuke Izumi, Hajime Sakuma
Abstract
Purpose: To investigate image noise and edge sharpness of coronary CT angiography (CCTA) with super-resolution deep learning reconstruction (SR-DLR) compared with conventional DLR (C-DLR) and to evaluate agreement in stenosis grading using CCTA with that from invasive coronary angiography (ICA) as the reference standard. Materials and Methods: This retrospective study included 58 patients (mean age, 69.0 years ± 12.8 [SD]; 38 men, 20 women) who underwent CCTA using 320-row CT between April and September 2022. All images were reconstructed with two different algorithms: SR-DLR and C-DLR. Image noise, signal-to-noise ratio, edge sharpness, full width at half maximum (FWHM) of stent, and agreement in stenosis grading with that from ICA were compared. Stenosis was visually graded from 0 to 5, with 5 indicating occlusion. Results: < .001). Agreement in stenosis grading between CCTA and ICA was improved on SR-DLR compared with C-DLR (weighted κ = 0.83 vs 0.77). Conclusion: . © RSNA, 2023.