Liu Mingming, Chen Xiuzhen, Liu Weimin, Guo Yuefei, Zhu Yanqiu, Duan Yani, Huang Wanyue, Kong Wei, Yan Cui, Qin Jie
Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Department of Radiology, The Shaoguan Affiliated Hospital of Southern Medical University, Shaoguan, China.
Quant Imaging Med Surg. 2024 Feb 1;14(2):1616-1635. doi: 10.21037/qims-23-1064. Epub 2024 Jan 17.
The high-definition standard (HD-standard) scan mode has been proven to display stents better than the standard (STND) scan mode but with more image noise. Deep learning image reconstruction (DLIR) is capable of reducing image noise. This study examined the impact of HD-standard scan mode with DLIR algorithms on stent and coronary artery image quality in coronary computed tomography angiography (CCTA) via a comparison with conventional STND scan mode and adaptive statistical iterative reconstruction-Veo (ASIR-V) algorithms.
The data of 121 patients who underwent HD-standard mode scans (group A: N=47, with coronary stent) or STND mode scans (group B: N=74, without coronary stent) were retrospectively collected. All images were reconstructed with ASIR-V at a level of 50% (ASIR-V50%) and a level of 80% (ASIR-V80%) and with DLIR at medium (DLIR-M) and high (DLIR-H) levels. The noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), artifact index (AI), and in-stent diameter were measured as objective evaluation parameters. Subjective assessment involved a 5-point scale for overall image quality, image noise, stent appearance, stent artifacts, vascular sharpness, and diagnostic confidence. Diagnostic confidence was evaluated based on the presence or absence of significant stenosis (≥50% lumen reduction). Both subjective and objective evaluations were conducted by two radiologists independently, with kappa and intraclass correlation statistics being used to test the interobserver agreement.
There were 76 evaluable stents in group A, and the DLIR-H algorithm significantly outperformed other algorithms, demonstrating the lowest noise (41.6±7.1/41.3±7.2) and AI (32.4±8.9/31.2±10.1), the highest SNR (14.6±3.5/15.0±3.5) and CNR (13.6±3.8/13.9±3.8), and the largest in-stent diameter (2.18±0.61/2.19±0.61) in representing true stent diameter (all P values <0.01), as well as the highest score in each subjective evaluation parameter. In group B, a total of 296 coronary arteries were evaluated, and the DLIR-H algorithm provided the best objective image quality, with statistically superior noise, SNR, and CNR compared with the other algorithms (all P values <0.05). Moreover, the HD-standard mode scan with DLIR provided better image quality and a lower radiation dose than did the STND mode scan with ASIR-V (P<0.01).
HD-standard scan mode with DLIR-H improves image quality of both stents and coronary arteries on CCTA under a lower radiation dose.
高分辨率标准（HD标准）扫描模式已被证明在显示支架方面优于标准（STND）扫描模式，但图像噪声更多。深度学习图像重建（DLIR）能够减少图像噪声。本研究通过与传统的STND扫描模式和自适应统计迭代重建-Veo（ASIR-V）算法进行比较，探讨了采用DLIR算法的HD标准扫描模式对冠状动脉计算机断层扫描血管造影（CCTA）中支架和冠状动脉图像质量的影响。
回顾性收集121例行HD标准模式扫描（A组：N = 47，有冠状动脉支架）或STND模式扫描（B组：N = 74，无冠状动脉支架）患者的数据。所有图像均采用50%水平（ASIR-V50%）和80%水平（ASIR-V80%）的ASIR-V以及中等（DLIR-M）和高（DLIR-H）水平的DLIR进行重建。测量噪声、信噪比（SNR）、对比噪声比（CNR）、伪影指数（AI）和支架内直径作为客观评估参数。主观评估采用5分制对整体图像质量、图像噪声、支架外观、支架伪影、血管清晰度和诊断信心进行评分。根据是否存在显著狭窄（管腔缩小≥50%）评估诊断信心。主观和客观评估均由两名放射科医生独立进行，采用kappa和组内相关统计检验观察者间的一致性。
A组有76个可评估支架，DLIR-H算法明显优于其他算法，在表示真实支架直径方面显示出最低的噪声（41.6±7.1/41.3±7.2）和AI（32.4±8.9/31.2±10.1）、最高的SNR（14.6±3.5/15.0±3.5）和CNR（13.6±3.8/13.9±3.8）以及最大的支架内直径（2.18±0.61/2.19±0.61）（所有P值<0.01），并且在每个主观评估参数中得分最高。在B组中，共评估了296条冠状动脉，DLIR-H算法提供了最佳的客观图像质量，与其他算法相比，其噪声、SNR和CNR在统计学上更优（所有P值<0.05）。此外，与采用ASIR-V的STND模式扫描相比，采用DLIR的HD标准模式扫描提供了更好的图像质量和更低的辐射剂量（P<0.01）。
采用DLIR-H的HD标准扫描模式在较低辐射剂量下可提高CCTA中支架和冠状动脉的图像质量。
