Improved stent sharpness evaluation with super-resolution deep learning reconstruction in coronary CT angiography

Author:

Ryu Jae-Kyun1,Kim Ki Hwan2,Otgonbaatar Chuluunbaatar3ORCID,Kim Da Som4,Shim Hackjoon15,Seo Jung Wook2ORCID

Affiliation:

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

Abstract

Abstract Objectives This study aimed to assess the impact of super-resolution deep learning reconstruction (SR-DLR) on coronary CT 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 66 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. Advances in knowledge The overall image quality was significantly higher in SR-DLR, resulting in sharper coronary artery stents compared to HIR and DLR.

Funder

BRACCO Research Foundation

Publisher

Oxford University Press (OUP)

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