Deep Learning Image Reconstruction for Transcatheter Aortic Valve Implantation Planning: Image Quality, Diagnostic Performance, Contrast volume and Radiation Dose Assessment

Yu Zhang; Zixuan Liu; Yong Cheng; Ziwei Wang; Zhenlin Li; Jianying Li; Tao Shuai

doi:10.1016/j.acra.2024.02.026

Deep Learning Image Reconstruction for Transcatheter Aortic Valve Implantation Planning: Image Quality, Diagnostic Performance, Contrast volume and Radiation Dose Assessment

Acad Radiol. 2024 Mar 11:S1076-6332(24)00096-5. doi: 10.1016/j.acra.2024.02.026. Online ahead of print.

Authors

Yu Zhang¹, Zixuan Liu¹, Yong Cheng¹, Ziwei Wang¹, Zhenlin Li¹, Jianying Li², Tao Shuai³

Affiliations

¹ Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, Sichuan 610041, China.
² GE Healthcare, Beijing, China.
³ Department of Radiology, West China Hospital, Sichuan University, 37 Guoxue Alley, Chengdu, Sichuan 610041, China. Electronic address: shuaitao1985@126.com.

PMID: 38472024
DOI: 10.1016/j.acra.2024.02.026

Abstract

Rationale and objectives: To assess image quality, contrast volume and radiation dose reduction potential and diagnostic performance with the use of high-strength deep learning image reconstruction (DLIR-H) in transcatheter aortic valve implantation (TAVI) planning CT.

Methods: We prospectively enrolled 128 patients referred to TAVI-planning CT. Patients were randomly divided into two groups: DLIR-H group (n = 64) and conventional group (n = 64). The DLIR-H group was scanned with tube voltage of 80kVp and body weighted-dependent contrast injection rate of 28mgI/kg/s, images reconstructed using DLIR-H; the conventional group was scanned with 100kVp and contrast injection rate of 40mgI/kg/s, and images reconstructed using adaptive statistical iterative reconstruction-V at 50% (ASIR-V 50%). Radiation dose, contrast volume, contrast injection rate, and image quality were compared between the two groups. The diagnostic performance of TAVI planning CT for coronary stenosis in 115 patients were calculated using invasive coronary angiography as golden standard.

Results: DLIR-H group significantly reduced radiation dose (4.94 ± 0.39mSv vs. 7.93 ± 1.20mSv, p < 0.001), contrast dose (45.28 ± 5.38 mL vs. 63.26 ± 9.88 mL, p < 0.001), and contrast injection rate (3.1 ± 0.31 mL/s vs. 4.9 ± 0.2 mL/s, p < 0.001) compared to the conventional group. Images in DLIR-H group had significantly higher SNR and CNR (all p < 0.001). For the diagnostic performance on a per-patient basis, TAVI planning CT in the DLIR-H group provided 100% sensitivity, 92.1% specificity, 100% negative predictive value (NPV), and 84.2% positive predictive value for the detection of > 50% stenosis. In the conventional group, the corresponding results were 94.7%, 95.3%, 97.6%, and 90.0%, respectively.

Conclusion: DLIR-H in TAVI-planning CT provides improved image quality with reduced radiation and contrast doses, and enables satisfactory diagnostic performance for coronary arteries stenosis.

Keywords: Computed tomography angiography; Deep learning; Radiation dosage; Transcatheter aortic valve Implantation.