On-site CT-derived cFFR in patients with suspected coronary artery disease: Feasibility on a 128-row CT scanner in everyday clinical practice

Abstract

Purpose Technical feasibility of CT-based calculation of fractional flow reserve (cFFR) using a 128-row computed tomography scanner in an everyday routine setting. Post-processing and everyday practicability should be analyzed on the scanner on-site in connection with clinical parameters. Materials and Methods This single-center retrospective analysis included 230 patients (74 female; mean age 63.8 years) with CCTA within 21 months between 01/2018 and 09/2019 without non-pathological examinations. cFFR values were obtained using a deep learning-based non-commercial research prototype (cFFR Version3.5.0; Siemens Healthineers GmbH, Erlangen). cFFR values were evaluated at two points: at the maximum point of the stenosis and 1.0 cm distal to the stenosis. Comparison with invasive coronary angiography in 57/230 patients (24.7 %) was performed. CT parameters and quality were evaluated. Further subgroup classification concerning criteria of technical postprocessing was performed: no changes necessary, minor corrections necessary, major corrections necessary, and no evaluation was possible. The required time from starting the software to the final result was evaluated. Results A total of 116/448 (25.9 %) mild, 223/448 (49.8 %) moderate, and 109/448 (24.3 %) obstructive stenoses was found. The mean cFFR at the maximum point of the stenosis was 0.92 ± 0.09 and significantly higher than the cFRR value of 0.89 ± 0.13 distal to the stenosis (p < 0.001*). The mean degree of stenosis was 44.02 ± 26.99 % (range: 1–99 %) with an area of 5.39 ± 3.30 mm2. In a total of 45 patients (19.1 %), a relevant reduction in cFFR below 0.80 was determined. Overall, in 57/230 patients (24.8 %), catheter angiography was performed. No significant difference in the degree of maximal stenosis (CAD-RADS 0–2/3/4) was detected between the classification of CCTA and ICA (p = 0.171). The mean post-processing time varied significantly with 8.34 ± 4.66 min. in single-vessel CAD vs. 12.91 ± 3.92 min. in two-vessel CAD vs. 21.80 ± 5.94 min. in three-vessel CAD (each p < 0.001). Conclusion Noninvasive onsite quantification of cFFR is feasible with minimal observer interaction in a routine real-world setting on a 128-row scanner. Deep learning-based algorithms allow a robust and semi-automatic on-site determination of cFFR based on data from standard CT scanners. Key Points: