What is the optimal anatomic location for coronary artery pressure measurement at CT-derived FFR?

Abstract

Background: CT-FFR is an area of growing interest in the field of cardiac imaging. However, the specific anatomic location distal to a lesion of interest where CT-FFR should be computed to yield the most valid results has not been examined. This study investigated the most appropriate anatomic location distal to a coronary artery stenosis for obtaining CT-FFR measurements.

Methods: 73 patients (60 ± 9 years; 58% male) with at least one coronary lesion with 40-90% stenosis on coronary CTA (either a 2 × 128 slice or a 2 × 192 slice dual-source CT scanner) underwent stress cardiac magnetic resonance (CMR) perfusion imaging for inducible ischemia. 133 coronary arteries and corresponding myocardial territories were analyzed. The most appropriate anatomic location for predicting lesion-specific ischemia via CT-FFR (cFFR version 1.4, Siemens) was determined as either the distance from the lesion of interest or as a multiple of the reference vessel diameter distal to the minimum lumen area (MLA).

Results: Inducible myocardial ischemia was found on MRI in 24 (18.1%) vessels/corresponding myocardial territories. The area under the ROC curve was A) 0.866 for CT-FFR measurement locations distal to the MLA expressed as a multiple of the reference diameter, B) 0.854 when expressed as a distance (mm) distal to the MLA, C) 0.803 for CT-FFR values measured in the distal vessel, and D) 0.725 according to stenosis severity on coronary CTA (A vs B p = 0.093; A vs D p = 0.003; A vs C p = 0.019; B vs D p = 0.006; B vs C p = 0.061; C vs D p = 0.082). The most optimal thresholds for agreement of CT-FFR with the reference CMR perfusion were at 41 mm or 10.9 times the proximal reference diameter distal to the MLA.

Conclusions: Our results suggest that the best agreement of CT-FFR with the reference CMR perfusion study is provided when CT-FFR values are computed at 41 mm or 10.9 times the proximal reference diameter distal to the MLA.

Keywords: Cardiac magnetic resonance; Computed tomography angiography; Coronary artery disease; Fractional flow reserve; Machine learning.