Korean J Radiol.  2022 Nov;23(11):1044-1054. 10.3348/kjr.2022.0127.

Improvement in Image Quality and Visibility of Coronary Arteries, Stents, and Valve Structures on CT Angiography by Deep Learning Reconstruction

Affiliations
  • 1Department of Radiology, College of Medicine, Seoul National University, Seoul, Korea
  • 2Medical Imaging AI Research Center, Canon Medical Systems Korea, Seoul, Korea
  • 3Department of Radiology, Inje University Seoul Paik Hospital, Seoul, Korea
  • 4Department of Radiology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
  • 5Department of Radiology, Inje University Ilsan Paik Hospital, Goyang, Korea
  • 6ConnectAI Research Center, Yonsei University College of Medicine, Seoul, Korea

Abstract


Objective
This study aimed to investigate whether a deep learning reconstruction (DLR) method improves the image quality, stent evaluation, and visibility of the valve apparatus in coronary computed tomography angiography (CCTA) when compared with filtered back projection (FBP) and hybrid iterative reconstruction (IR) methods.
Materials and Methods
CCTA images of 51 patients (mean age ± standard deviation [SD], 63.9 ± 9.8 years, 36 male) who underwent examination at a single institution were reconstructed using DLR, FBP, and hybrid IR methods and reviewed. CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and stent evaluation, including 10%– 90% edge rise slope (ERS) and 10%–90% edge rise distance (ERD), were measured. Quantitative data are summarized as the mean ± SD. The subjective visual scores (1 for worst -5 for best) of the images were obtained for the following: overall image quality, image noise, and appearance of stent, vessel, and aortic and tricuspid valve apparatus (annulus, leaflets, papillary muscles, and chordae tendineae). These parameters were compared between the DLR, FBP, and hybrid IR methods.
Results
DLR provided higher Hounsfield unit (HU) values in the aorta and similar attenuation in the fat and muscle compared with FBP and hybrid IR. The image noise in HU was significantly lower in DLR (12.6 ± 2.2) than in hybrid IR (24.2 ± 3.0) and FBP (54.2 ± 9.5) (p < 0.001). The SNR and CNR were significantly higher in the DLR group than in the FBP and hybrid IR groups (p < 0.001). In the coronary stent, the mean value of ERS was significantly higher in DLR (1260.4 ± 242.5 HU/mm) than that of FBP (801.9 ± 170.7 HU/mm) and hybrid IR (641.9 ± 112.0 HU/mm). The mean value of ERD was measured as 0.8 ± 0.1 mm for DLR while it was 1.1 ± 0.2 mm for FBP and 1.1 ± 0.2 mm for hybrid IR. The subjective visual scores were higher in the DLR than in the images reconstructed with FBP and hybrid IR.
Conclusion
DLR reconstruction provided better images than FBP and hybrid IR reconstruction.

Keyword

Deep learning reconstruction; Hybrid iterative reconstruction; Filtered-back projection; Coronary stent; Valve apparatus
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