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 ±â€Š0.09 and significantly higher than the cFRR value of 0.89 ±â€Š0.13 distal to the stenosis (p < 0.001*). The mean degree of stenosis was 44.02 ±â€Š26.99 % (range 1-99 %) with an area of 5.39 ±â€Š3.30 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 ±â€Š4.66 min. in single-vessel CAD vs. 12.91 ±â€Š3.92 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 · Non-invasive on-site quantification of cFFR is feasible with minimal observer interaction.. · Deep-learning based algorithms allow robust and semi-automatic on-site determination of cFFR.. · The mean follow-up time varied significantly with the extent of vascular CAD..