Effect of deep learning image reconstruction with high-definition standard scan mode on image quality of coronary stents and arteries

Mingming Liu; Xiuzhen Chen; Weimin Liu; Yuefei Guo; Yanqiu Zhu; Yani Duan; Wanyue Huang; Wei Kong; Cui Yan; Jie Qin

doi:10.21037/qims-23-1064

Effect of deep learning image reconstruction with high-definition standard scan mode on image quality of coronary stents and arteries

Quant Imaging Med Surg. 2024 Feb 1;14(2):1616-1635. doi: 10.21037/qims-23-1064. Epub 2024 Jan 17.

Authors

Mingming Liu^#¹, Xiuzhen Chen^#¹, Weimin Liu¹, Yuefei Guo¹, Yanqiu Zhu¹, Yani Duan¹, Wanyue Huang¹, Wei Kong², Cui Yan³, Jie Qin¹

Affiliations

¹ Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
² Department of Radiology, The Shaoguan Affiliated Hospital of Southern Medical University, Shaoguan, China.
³ Department of Cardiopulmonary Rehabilitation and Sleep Science, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.

^# Contributed equally.

Abstract

Background: 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.

Methods: 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.

Results: 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).

Conclusions: HD-standard scan mode with DLIR-H improves image quality of both stents and coronary arteries on CCTA under a lower radiation dose.

Keywords: Deep learning image reconstruction; coronary computed tomography angiography (CCTA); coronary stents; high-definition standard scan mode (HD-standard scan mode).