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

IF 1.8 4区医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

British Journal of Radiology Pub Date : 2024-06-18 DOI:10.1093/bjr/tqae094

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

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引用次数: 0

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.

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在冠状动脉计算机断层扫描血管造影中利用超级分辨率深度学习重建改进支架清晰度评估。

研究目的本研究旨在评估超分辨率深度学习重建（SR-DLR）与传统方法（包括混合迭代重建（HIR）和基于深度学习的重建（DLR））相比，对冠状动脉计算机断层扫描血管造影（CCTA）图像质量和冠状动脉支架出血伪影的影响：回顾性分析包括 2022 年 7 月至 11 月期间的 66 名 CCTA 患者。对主要冠状动脉的图像噪声、信噪比（SNR）和对比度与噪声比（CNR）进行了评估。支架清晰度采用 10-90% 边缘上升斜率 (ERS) 和 10-90% 边缘上升距离 (ERD) 进行量化。定性分析采用 5 点评分法评估整体图像质量、图像噪声、血管壁和支架结构：结果：与 HIR 和 DLR 相比，SR-DLR 的图像噪点明显更低。SR-DLR 的 SNR 和 CNR 明显更高。SR-DLR 的支架 ERS 明显改善，SR-DLR 的平均 ERD 值为 0.70 ± 0.20 毫米，HIR 为 1.13 ± 0.28 毫米，DLR 为 0.85 ± 0.26 毫米。从质量上看，SR-DLR 在所有类别中的得分都更高：与 HIR 和 DLR 相比，SR-DLR 生成的图像噪点更低，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，可提高冠状动脉支架的空间分辨率和清晰度，而不受硬件限制：与 HIR 和 DLR 相比，SR-DLR 的整体图像质量明显更高，使冠状动脉支架更加清晰。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

British Journal of Radiology 医学-核医学

CiteScore

5.30

自引率

3.80%

发文量

330

审稿时长

2-4 weeks

期刊介绍： BJR is the international research journal of the British Institute of Radiology and is the oldest scientific journal in the field of radiology and related sciences. Dating back to 1896, BJR’s history is radiology’s history, and the journal has featured some landmark papers such as the first description of Computed Tomography "Computerized transverse axial tomography" by Godfrey Hounsfield in 1973. A valuable historical resource, the complete BJR archive has been digitized from 1896. Quick Facts: - 2015 Impact Factor – 1.840 - Receipt to first decision – average of 6 weeks - Acceptance to online publication – average of 3 weeks - ISSN: 0007-1285 - eISSN: 1748-880X Open Access option