Sunday, November 25 | 11:05 a.m.-11:15 a.m. | SSA03-03 | Room S404AB
CT myocardial perfusion may be a better predictor for major adverse cardiac events in patients with coronary artery disease than fractional flow reserve CT (FFR-CT), according to this study being presented on Sunday.Researchers from the Medical University of South Carolina and the University of Groningen in the Netherlands reviewed the imaging data of 81 patients with coronary artery disease (CAD) who underwent both coronary CT angiography (CCTA) and stress dynamic CT myocardial perfusion at one of four institutions. They used FFR-CT and myocardial blood-flow measurements from these data to estimate the prognostic value of the two imaging modalities.
Both FFR-CT and CT perfusion had a higher area under the curve for predicting major adverse cardiac events, with blood-flow measurements from CT perfusion providing the most accurate predictions. Combining the information from the two techniques further improved their predictive value, compared with using either of the techniques alone.
"Dynamic CT perfusion has the highest predictive value for major adverse cardiac events," doctoral candidate Marly van Assen told AuntMinnie.com. "Both FFR-CT and CT perfusion have added value in the prognostication of major adverse cardiac events and could provide complementary information."




















![Images show the pectoralis muscles of a healthy male individual who never smoked (age, 66 years; height, 178 cm; body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], 28.4; number of cigarette pack-years, 0; forced expiratory volume in 1 second [FEV1], 97.6% predicted; FEV1: forced vital capacity [FVC] ratio, 0.71; pectoralis muscle area [PMA], 59.4 cm2; pectoralis muscle volume [PMV], 764 cm3) and a male individual with a smoking history and chronic obstructive pulmonary disorder (COPD) (age, 66 years; height, 178 cm; BMI, 27.5; number of cigarette pack-years, 43.2, FEV1, 48% predicted; FEV1:FVC, 0.56; PMA, 35 cm2; PMV, 480.8 cm3) from the Canadian Cohort Obstructive Lung Disease (i.e., CanCOLD) study. The CT image is shown in the axial plane. The PMV is automatically extracted using the developed deep learning model and overlayed onto the lungs for visual clarity.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/03/genkin.25LqljVF0y.jpg?auto=format%2Ccompress&crop=focalpoint&fit=crop&h=112&q=70&w=112)