Introduction Combination of computed tomography angiography (CTA) and adenosine stress CT myocardial perfusion (CTP) allows for coronary artery lesion assessment as well as myocardial ischemia. Nowadays, ischemia on CTP is assessed semi-quantitatively by visual analysis. The aim of this study was to fully quantify myocardial ischemia and the subtended myocardial mass on CTP. Methods We included 33 patients referred for a combined CTA and adenosine stress CTP with good or excellent imaging quality on CTP. Firstly, the coronary artery tree was automatically extracted from CTA and the relevant coronary artery lesions (≥50%) were manually defined (Fig. 1A). Secondly, epi- and endocardial contours along with CTP deficits were manually defined in short-axis images (Fig. 1D, 1E). Thirdly, a Voronoi-based algorithm was used to quantify the subtended myocardial mass (Fig. 1B). Fourthly, the perfusion defect and subtended myocardial mass were spatially registered to the CTA and measured in grams (Fig. 1F, 1C). Finally, this can be used to quantitatively correlate the perfusion defect to the subtended myocardial mass. Results Voronoi-based segmentation was successful in all cases. We assessed a total of 64 relevant coronary artery lesions. Average values for left ventricular mass, total subtended mass and perfusion defect mass were 118, 69 and 7 grams respectively. In 19/33 patients (58%) the total perfusion defect mass could be distributed over the relevant coronary artery lesion(s). Conclusions Quantification of myocardial ischemia and subtended myocardial mass using a Voronoi-based segmentation algorithm seem feasible at adenosine stress CTP and allows for quantitative correlation of coronary artery lesions to corresponding areas of myocardial hypoperfusion. FUNDunding Acknowledgement Type of funding sources: None. Figure 1

Background Computed Tomography Coronary Angiography (CTCA) is an effective non-invasive imaging modality for anatomo-functional assessment of coronary artery disease (CAD). Radiomics features have been used for diagnosis or outcome prediction, however, their potential value for characterizing flow limiting coronary lesions has not been explored. Purpose To assess whether application of novel radiomics and machine learning (ML) techniques on CTCA derived datasets improves characterization of functionally significant coronary lesions. Methods Consecutive patients with stable chest pain and intermediate pre-test likelihood for CAD, who underwent CTCA and PET-or SPECT-Myocardial Perfusion Imaging (MPI) respectively, were prospectively evaluated and included in the analysis. PET-MPI was considered abnormal when >1 contiguous segments showed both stress Myocardial Blood Flow ≤2.3mL/g/min and Myocardial Flow Reserve (MFR) ≤2.5 for 15O-water or <1.79 mL/g/min and ≤2.0 for 13N-ammonia respectively. Defect reversibility (DR) was defined as a summed difference score (SDS) between stress and rest images ≥2. CTCA and functional images were fused to assign each myocardial segment to the pertinent coronary territory. Stenosis severity, plaque characteristics and radiomic plaque features were assessed in the total length of the 3 main coronary vessels. In total, 1765 features were extracted from each vessel and a feature reduction and model creation pipeline was constructed [Figure 1]. Two separate datasets: a) coronary stenosis (≥50%) + plaque characteristics and b) coronary stenosis (≥50%) + plaque characteristics + radiomics were formulated and compared in terms of AUCs accordingly. Results A total of 292 coronary vessels (140 with corresponding PET-MPI data and 152 with SPECT MPI data) were analysed. Plaque burden and stenosis severity were the only independent predictors of impaired myocardial perfusion on PET-MPI, with an AUC = 0.749, (95% CI: 0.658–0.826). Stenosis severity, kurtosis, contrast, interquartile range and entropy were predictors of an abnormal PET-MPI result and their combination resulted in an AUC = 0.854, (95% CI: 0.775–0.914). The difference between the 2 models was statistically significant (p-diff: 0.02, 95% CI: 0.0165–0.194). Stenosis severity was the only predictor of a DR on SPECT-MPI, AUC = 0.624 (95% CI: 0.542–0.702). Small Dependence High Gray Level Emphasis, Cluster Prominence, Region Length, wavelet Median and square Median were predictors of a positive SPECT result, with AUC = 0.816, (95% CI: 0.745–0.875). The difference between the two models was statistically significant (p-diff: 0.006, 95% CI: 0.152–0.329) Conclusion Radiomic futures can be combined with anatomical and morphological characteristics of coronary lesions in CTCA imaging and provide valuable complementary information for characterizing functionally significant coronary lesions. Funding Acknowledgement Type of funding sources: Public grant(s) – EU funding. Main funding source(s): This work was supported from European Regional Development Fund, Operational Programme “Competitiveness, Entrepreneurship and Innovation 2014-2022 (EPAnEK)”, titled: The Greek Research Infrastructure for Personalized Medicine (pMED-GR)

Introduction Use of serial coronary computed tomography angiography (CCTA) allows for the early assessment of coronary plaque progression which may aid in the prevention of major adverse cardiac events (MACE). However, assessment of serial CCTA is done by using anatomical landmarks for matching baseline and follow-up scans. Recently, a tool has been developed allowing for automatic quantification of plaque progression dynamics in serial CCTA utilizing plaque delineation. Purpose The aim of this study was to determine the thresholds that define whether there is plaque progression and/or regression. These thresholds depend on the contrast to noise ratio (CNR) which is an objective marker for scan quality as the latter impacts the plaque delineation. Methods Thresholds and CNR ratios were determined on 50 patients referred for a CCTA due to thoracic complaints. Two scan phases were selected from each patient in which maximum and minimum differences in plaque delineation were measured. Also, CNR was calculated separately for all three major epicardial coronary vessels. A total of 100 scans were analyzed in the current analysis accounting for a total of 300 coronary vessels. First, vessel and lumen wall delineation was done semi-automatically for all major epicardial coronary vessels. Secondly, manual drawings of 7 regions of interest (ROI) per scan were used to quantify scan quality which was defined using the CNR and calculated for each vessel separately. As plaque differences of two scans at the same moment in time should always be zero, the minimum and maximum difference in plaque delineation was used in these scans along with the CNR in order to create calibration graphs on which a linear regression analysis was performed (Figure 1, charts A & B). Inter-observer measurements were calculated using Pearson's correlation coefficient. Results A total of 300 coronary vessels were assessed at CCTA. Semi-automatic vessel and lumen wall delineation as well as CNR calculation was successful in all cases. Subsequent linear regression analysis performed on the CNR and maximal and minimal plaque delineation differences and taking into account the standard error of the estimate revealed the following formulas for minimum and maximum cut-off values: Max = [(0.660 − (002 × CNR)] + 0.349 Min = [(−1.028 + (0.012 × CNR)) − 0.61 The average CNR values was 13.4±3.6. Average maximum and minimum difference in plaque delineation was 0.7±0.3mm and −0.9±0.6mm respectively. The inter-observer correlation for CNR values was excellent yielding a correlation coefficient of 0.872 (p<0.001). The importance of using thresholds and subsequent calculation of vessel specific cutoff values is demonstrated in Figure 2. Conclusion Development of vessel-specific quality-based thresholds for the quantification and visualization of plaque progression dynamics as assessed by serial CCTA seems feasible and may aid in the early detection of atherosclerosis progression. Funding Acknowledgement Type of funding sources: None.

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Introduction: Combination of computed tomography angiography (CTA) and adenosine stress CT myocardial perfusion (CTP) allows for coronary artery lesion assessment as well as myocardial ischemia. Nowadays, ischemia on CTP is assessed semi-quantitatively by visual analysis. The aim of this study was to fully quantify myocardial ischemia and the subtended myocardial mass on CTP. Methods: We included 33 patients referred for a combined CTA and adenosine stress CTP with good or excellent imaging quality on CTP. Firstly, the coronary artery tree was automatically extracted from CTA and the relevant coronary artery lesions (≥ 50%) were manually defined (fig 1A). Secondly, epi- and endocardial contours along with CTP deficits were manually defined in short-axis images (fig 1D, 1E). Thirdly, a Voronoi-based algorithm was used to quantify the subtended myocardial mass (fig 1B). Fourthly, the perfusion defect and subtended myocardial mass were spatially registered to the CTA and measured in grams (fig 1F, 1C). Finally, this can be used to quantitatively correlate the perfusion defect to the subtended myocardial mass. To assess reproducibility, left ventricular epicardial and endocardial contours along with perfusion defects were re-drawn. Results: Voronoi-based segmentation was successful in all cases. We assessed a total of 64 relevant coronary artery lesions. Average values for left ventricular mass, total subtended mass and perfusion defect mass were 118, 69 and 7 grams respectively. In 19/33 patients (58%) the total perfusion defect mass could be distributed over the relevant coronary artery lesion(s). Results were highly reproducible (r≥0.822, p<0.01). Conclusions: Quantification of myocardial ischemia and subtended myocardial mass using a Voronoi-based segmentation algorithm seem feasible at adenosine stress CTP and allows for quantitative correlation of coronary artery lesions to corresponding areas of myocardial hypoperfusion.