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Congress: ECR25
Poster Number: C-11699
Type: Poster: EPOS Radiologist (scientific)
Authorblock: I. Ayx, L. Lichti, S. Büttner, T. Papavassiliu, K. Sopova, S. O. Schönberg, M. Kuru, C. A. Marschner; Mannheim/DE
Disclosures:
Isabelle Ayx: Nothing to disclose
Lena Lichti: Nothing to disclose
Sylvia Büttner: Nothing to disclose
Theano Papavassiliu: Nothing to disclose
Kateryna Sopova: Nothing to disclose
Stefan Oswald Schönberg: Nothing to disclose
Mustafa Kuru: Nothing to disclose
Constantin Arndt Marschner: Nothing to disclose
Keywords: Artificial Intelligence, Cardiac, CT-Angiography, Diagnostic procedure, Arteriosclerosis
Purpose

Coronary CT angiography (CCTA) is a valuable tool for excluding coronary artery disease, though its lack of specificity remains a limitation (1-3). To minimize unnecessary invasive catheter angiographies (ICA), current guidelines recommend the use of CT-derived fractional flow reserve (CT-FFR) to assess the hemodynamic significance of coronary artery stenosis (4). Additionally, the introduction of photon-counting CT (PCCT) has the potential to enhance CCTA by offering improved spatial and temporal resolution (5,6), which could, in turn, refine CT-FFR analysis for more individualized patient care.

This study seeks to evaluate the potential of CT-FFR in assessing the hemodynamic significance of coronary artery stenoses at a lesion level using a PCCT dataset. The results will be compared with those obtained from clinically indicated ICA and the major adverse cardiovascular event (MACE) rate.

GALLERY