Ryu Jae-Kyun, Kim Ki Hwan, Otgonbaatar Chuluunbaatar, Kim Da Som, Shim Hackjoon, Seo Jung Wook
Medical Imaging AI Research Center, Canon Medical Systems Korea, Seoul, Republic of Korea.
Department of Radiology, Inje University Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Republic of Korea.
Br J Radiol. 2024 Jun 18;97(1159):1286-1294. doi: 10.1093/bjr/tqae094.
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).
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.
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.
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.
The overall image quality was significantly higher in SR-DLR, resulting in sharper coronary artery stents compared to HIR and DLR.
本研究旨在评估超分辨率深度学习重建（SR-DLR）与包括混合迭代重建（HIR）和基于深度学习的重建（DLR）在内的传统方法相比，对冠状动脉CT血管造影（CCTA）图像质量以及冠状动脉支架伪影的影响。
一项回顾性分析纳入了2022年7月至11月的66例CCTA患者。对主要冠状动脉进行图像噪声、信噪比（SNR）和对比噪声比（CNR）评估。使用10%-90%边缘上升斜率（ERS）和10%-90%边缘上升距离（ERD）对支架清晰度进行量化。定性分析采用5分制评分系统评估整体图像质量、图像噪声、血管壁和支架结构。
与HIR和DLR相比，SR-DLR的图像噪声显著更低。SR-DLR的SNR和CNR明显更高。SR-DLR的支架ERS显著改善，SR-DLR的平均ERD值为0.70±0.20mm，HIR为1.13±0.28mm，DLR为0.85±0.26mm。定性评估中，SR-DLR在所有类别中得分更高。
与HIR和DLR相比，SR-DLR生成的图像具有更低的图像噪声，从而提高了整体图像质量。SR-DLR是一种有价值的图像重建算法，可在不受硬件限制的情况下提高冠状动脉支架的空间分辨率和清晰度。
与HIR和DLR相比，SR-DLR的整体图像质量显著更高，冠状动脉支架更清晰。
