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. 2023 Aug 17;5(4):e230085.
doi: 10.1148/ryct.230085. eCollection 2023 Aug.

Super-Resolution Deep Learning Reconstruction for Improved Image Quality of Coronary CT Angiography

Masafumi Takafuji  1 , Kakuya Kitagawa  1 , Sachio Mizutani  1 , Akane Hamaguchi  1 , Ryosuke Kisou  1 , Kotaro Iio  1 , Kazuhide Ichikawa  1 , Daisuke Izumi  1 , Hajime Sakuma  1
Affiliations

Super-Resolution Deep Learning Reconstruction for Improved Image Quality of Coronary CT Angiography

Masafumi Takafuji et al. Radiol Cardiothorac Imaging. .

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: SR-DLR significantly decreased image noise by 31% compared with C-DLR (12.6 HU ± 2.3 vs 18.2 HU ± 1.9; P < .001). Signal-to-noise ratio and edge sharpness were significantly improved by SR-DLR compared with C-DLR (signal-to-noise ratio, 38.7 ± 8.3 vs 26.2 ± 4.6; P < .001; edge sharpness, 560 HU/mm ± 191 vs 463 HU/mm ± 164; P < .001). The FWHM of stent was significantly thinner on SR-DLR (0.72 mm ± 0.22) than on C-DLR (1.01 mm ± 0.21; P < .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: SR-DLR improved vessel sharpness, image noise, and accuracy of coronary stenosis grading compared with the C-DLR technique.Keywords: CT Angiography, Cardiac, Coronary Arteries Supplemental material is available for this article. © RSNA, 2023.

Keywords: CT Angiography; Cardiac; Coronary Arteries.

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Conflict of interest statement

Disclosures of conflicts of interest: M.T. No relevant relationships. K.K. Honoraria for lectures from Siemens Healthcare Japan, Bayer Yakuhin, and Eisai; endowed chair for Siemens Healthcare Japan and Fuji Film Medical. S.M. No relevant relationships. A.H. No relevant relationships. R.K. No relevant relationships. K. Iio No relevant relationships. K. Ichikawa No relevant relationships. D.I. No relevant relationships. H.S. No relevant relationships.

Figures

Flowchart of patient selection. Of the 58 patients included, 13 underwent invasive coronary angiography (ICA). Vessel-based image quality analysis was performed in 228 vessels of the 58 patients. Segment-based analysis of stenosis grades was performed in 167 segments of 13 patients who underwent both coronary CT angiography (CCTA) and ICA. CAD = coronary artery disease.
Figure 1:
Flowchart of patient selection. Of the 58 patients included, 13 underwent invasive coronary angiography (ICA). Vessel-based image quality analysis was performed in 228 vessels of the 58 patients. Segment-based analysis of stenosis grades was performed in 167 segments of 13 patients who underwent both coronary CT angiography (CCTA) and ICA. CAD = coronary artery disease.
Profile curves show definition of edge sharpness and full width at half maximum (FWHM). Edge sharpness is defined as the maximum slope of the profile curve. (A) FWHM of coronary arteries without stent or calcified plaque (FWHM-vessel) was measured. (B) FWHMs of lumen (FWHM-lumen) and stent strut (FWHM-stent) were measured.
Figure 2:
Profile curves show definition of edge sharpness and full width at half maximum (FWHM). Edge sharpness is defined as the maximum slope of the profile curve. (A) FWHM of coronary arteries without stent or calcified plaque (FWHM-vessel) was measured. (B) FWHMs of lumen (FWHM-lumen) and stent strut (FWHM-stent) were measured.
Edge of stent was sharper on coronary CT angiogram using super-resolution deep learning reconstruction (SR-DLR) than on that using conventional DLR (C-DLR). Profile curve demonstrates that SR-DLR reduced the thickness of full width at half maximum of stent.
Figure 3:
Edge of stent was sharper on coronary CT angiogram using super-resolution deep learning reconstruction (SR-DLR) than on that using conventional DLR (C-DLR). Profile curve demonstrates that SR-DLR reduced the thickness of full width at half maximum of stent.
Images in a 66-year-old man after percutaneous coronary intervention of the left anterior descending artery. (A, B) On coronary CT angiogram, edge of stent and calcified and noncalcified plaque all appear sharper using super-resolution deep learning reconstruction (SR-DLR) (B) than using conventional (C-DLR) (A). (C) Invasive coronary angiogram (ICA) demonstrates mild stenosis in the left anterior descending artery.
Figure 4:
Images in a 66-year-old man after percutaneous coronary intervention of the left anterior descending artery. (A, B) On coronary CT angiogram, edge of stent and calcified and noncalcified plaque all appear sharper using super-resolution deep learning reconstruction (SR-DLR) (B) than using conventional (C-DLR) (A). (C) Invasive coronary angiogram (ICA) demonstrates mild stenosis in the left anterior descending artery.
Images in an 81-year-old woman with chest pain on exertion. Severe stenosis was observed in the left anterior descending artery on an 8-mm maximum intensity projection image of coronary CT angiography (CCTA). Compared with CCTA using conventional deep learning reconstruction (C-DLR) (A), the stringlike continuity of coronary artery was more clearly observed on CCTA using super-resolution DLR (SR-DLR) (B). (C) Invasive coronary angiogram (ICA) demonstrates severe stenosis in the left anterior descending artery.
Figure 5:
Images in an 81-year-old woman with chest pain on exertion. Severe stenosis was observed in the left anterior descending artery on an 8-mm maximum intensity projection image of coronary CT angiography (CCTA). Compared with CCTA using conventional deep learning reconstruction (C-DLR) (A), the stringlike continuity of coronary artery was more clearly observed on CCTA using super-resolution DLR (SR-DLR) (B). (C) Invasive coronary angiogram (ICA) demonstrates severe stenosis in the left anterior descending artery.
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