Image quality and diagnostic performance evaluation in transcatheter aortic valve implantation candidates with atrial fibrillation using a whole-heart coverage CT scanner

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

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.

Utility of Coronary Computed Tomography Angiography in Patients Undergoing Transcatheter Aortic Valve Implantation: A Meta-Analysis and Meta-Regression Based on Published Data from 7458 Patients

BACKGROUND

Coronary CT angiography (CCTA) may detect coronary artery disease (CAD) in transcatheter aortic valve implantation (TAVI) patients and may obviate invasive coronary angiography (ICA) in selected patients. We assessed the diagnostic accuracy of CCTA for detecting CAD in TAVI patients based on published data.

METHODS

Meta-analysis and meta-regression were performed based on a comprehensive electronic search, including relevant studies assessing the diagnostic accuracy of CCTA in the setting of TAVI patients compared to ICA. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), were calculated on a patient and per segment level.

RESULTS

Overall, 27 studies (total of 7458 patients) were included. On the patient level, the CCTA's pooled sensitivity and NPV were 95% (95% CI: 93-97%) and 97% (95% CI: 95-98%), respectively, while the specificity and PPV were at 73% (95% CI: 62-82%) and 64% (95% CI: 57-71%), respectively. On the segmental coronary vessel level, the sensitivity and NPV were 90% (95% CI: 79-96%) and 98% (95% CI: 97-99%).

CONCLUSIONS

This meta-analysis highlights CCTA's potential as a first-line diagnostic tool although its limited PPV and specificity may pose challenges when interpreting heavily calcified arteries. This study underscores the need for further research and protocol standardization in this area.

New Whole-Heart motion correction algorithm enables diagnostic CT of aortic valve and coronary arteries in systolic phase for transcatheter aortic valve implantation candidates

OBJECTIVES

To investigate the ability of new generation snapshot freeze (NGSSF) algorithm in improving diagnostic image quality of both aortic valve and coronary arteries for transcatheter aortic valve implantation (TAVI) candidates in TAVI planning CT.

METHODS

Sixty-four TAVI candidates underwent TAVI planning CT were enrolled. Scans from coronary CT angiography were reconstructed at 20%, 30%, 40%, and 75% R-R cardiac phases with NGSSF and standard (STD) algorithm. In each phase, following parameters were compared: aortic valve measurements and their reproducibility; image quality of aortic valve and coronary arteries. The diagnostic accuracies of TAVI planning CT for coronary artery stenosis in 30% R-R phase with NGSSF and STD algorithms were calculated in 47out of 64 patients with invasive coronary angiography as reference standard.

RESULTS

For subjective image quality evaluation, the excellent rate for aortic valve improved from 25.0% to 93.8% and the interpretable rate for coronary arteries increased from 20.3% to 95.3% in the 30% phase images with NGSSF compared with images with STD. For the detection of > 50% coronary artery stenosis, the 30% phase images with NGSSF provided a sensitivity of 90%, specificity of 81.48%, negative predictive value of 91.7%, and positive predictive value of 78.3% on a per-patient basis; While images with STD, had a corresponding results of 95.0%, 33.33%, 90.0%, and 51.4%, respectively.

CONCLUSIONS

NGSSF significantly improves image quality for both aortic valve and coronary arteries compared with STD for TAVI patients of all heart rates. NGSSF enables the accurate measurement for aortic valve and satisfactory diagnostic performance for coronary arteries stenosis in the same systolic phase for TAVI planning.