Improved stent sharpness evaluation with Super-Resolution deep learning reconstruction in coronary computed tomography angiography

Jae-Kyun Ryu; Ki Hwan Kim; Chuluunbaatar Otgonbaatar; Da Som Kim; Hackjoon Shim; Jung Wook Seo

doi:10.1093/bjr/tqae094

Improved stent sharpness evaluation with Super-Resolution deep learning reconstruction in coronary computed tomography angiography

Br J Radiol. 2024 May 11:tqae094. doi: 10.1093/bjr/tqae094. Online ahead of print.

Authors

Jae-Kyun Ryu¹, Ki Hwan Kim², Chuluunbaatar Otgonbaatar³, Da Som Kim⁴, Hackjoon Shim^{1

5}, Jung Wook Seo²

Affiliations

¹ Medical Imaging AI Research Center, Canon Medical Systems Korea, Seoul, Republic of Korea.
² Department of Radiology, Inje University Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Republic of Korea.
³ Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
⁴ Department of Radiology, Inje University Busan Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
⁵ ConnectAI Research Center, Yonsei University College of Medicine, Seoul, Republic of Korea.

PMID: 38733576
DOI: 10.1093/bjr/tqae094

Abstract

Objectives: This study aimed to assess the impact of super-resolution deep learning reconstruction (SR-DLR) on coronary computed tomography angiography (CCTA) image quality and blooming artifacts from coronary artery stents in comparison to conventional methods, including hybrid iterative reconstruction (HIR) and deep learning-based reconstruction (DLR).

Methods: A retrospective analysis included sixty-six CCTA patients from July to November 2022. Major coronary arteries were evaluated for image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). Stent sharpness was quantified using 10-90% edge rise slope (ERS) and 10-90% edge rise distance (ERD). Qualitative analysis employed a 5-point scoring system to assess overall image quality, image noise, vessel wall, and stent structure.

Results: SR-DLR demonstrated significantly lower image noise compared to HIR and DLR. SNR and CNR were notably higher in SR-DLR. Stent ERS was significantly improved in SR-DLR, with mean ERD values of 0.70 ± 0.20 mm for SR-DLR, 1.13 ± 0.28 mm for HIR, and 0.85 ± 0.26 mm for DLR. Qualitatively, SR-DLR scored higher in all categories.

Conclusions: SR-DLR produces images with lower image noise, leading to improved overall image quality, compared with HIR and DLR. SR-DLR is a valuable image reconstruction algorithm for enhancing the spatial resolution and sharpness of coronary artery stents without being constrained by hardware limitations.

Advanced in knowledge: The overall image quality was significantly higher in SR-DLR, resulting in sharper coronary artery stents compared to HIR and DLR.

Keywords: Coronary CT angiography; Coronary stent; Deep learning reconstruction; Hybrid iterative reconstruction; Super-resolution deep learning reconstruction.