Aim The current proof-of-concept study investigates the value of radiomic features from normal 13N-ammonia positron emission tomography (PET) myocardial retention images to identify patients with reduced global myocardial flow reserve (MFR). Methods Data from 100 patients with normal retention 13N-ammonia PET scans were divided into two groups, according to global MFR (i.e., < 2 and ≥ 2), as derived from quantitative PET analysis. We extracted radiomic features from retention images at each of five different gray-level (GL) discretization (8, 16, 32, 64, and 128 bins). Outcome independent and dependent feature selection and subsequent univariate and multivariate analyses was performed to identify image features predicting reduced global MFR. Results A total of 475 radiomic features were extracted per patient. Outcome independent and dependent feature selection resulted in a remainder of 35 features. Discretization at 16 bins (GL16) yielded the highest number of significant predictors of reduced MFR and was chosen for the final analysis. GLRLM_GLNU was the most robust parameter and at a cut-off of 948 yielded an accuracy, sensitivity, specificity, negative and positive predictive value of 67%, 74%, 58%, 64%, and 69%, respectively, to detect diffusely impaired myocardial perfusion. Conclusion A single radiomic feature (GLRLM_GLNU) extracted from visually normal 13N-ammonia PET retention images independently predicts reduced global MFR with moderate accuracy. This concept could potentially be applied to other myocardial perfusion imaging modalities based purely on relative distribution patterns to allow for better detection of diffuse disease.

Background/Introduction CT-based fractional flow reserve (CT-FFR) has been extensively studied and established as a valuable tool for clinical decision making over the past decade. Nevertheless, clinical implementation has not been systematically adopted due to economic and technical reasons. Among the latter, the turn-around time for the computation and analysis' results potentially plays an important role. Purpose To evaluate the feasibility and diagnostic accuracy of CT-FFR computed on-site with a novel, deep learning-based algorithm using invasive hemodynamic indices as the reference standard. Methods Sixty-one patients who underwent clinically indicated coronary computed tomography angiography and invasive FFR (iFFR) and/or instantaneous wave-free ratio (iFR) measurements were retrospectively included. CT-FFR analysis was performed in 77 arteries using an on-site prototype software based on deep learning algorithms for coronary anatomy segmentation and prediction of the pressure drop under rest and hyperemia. The diagnostic performance of CT-FFR to detect hemodynamic significant lesions was assessed using iFFR (≤0.8) and/or iFR (≤0.89) as the standard of reference (60 patients with iFFR, 11 patients with iFR, and 3 patients with both) and the receiver operating characteristic area under the curve (AUC) was calculated. Furthermore, correlation analysis and Bland-Altman (BA) analysis was performed. Time for analysis including processing and manual edits to the lumen segmentation was recorded. Results CT-FFR analysis was successful in 59 (97%) patients and 74 (96%) arteries. In 74 arteries, 31 of 74 coronary lesions were invasively found to be hemodynamically significant. Total mean time for per patient CT-FFR analysis was 7 minutes and 55 seconds. Compared with invasive indices, per-lesion sensitivity and specificity of CT-FFR were 90%, and 98%, respectively. The AUC of CT-FFR vs. invasive indices for hemodynamic significance was 0.94, (95% confidence interval: 0.86–0.98). Compared to iFFR, CT-FFR correlated well (r=0.77) with only a very small bias (0.02) and narrow BA limits of agreement (−0.14 to 0.17). The per-lesion accuracy, sensitivity and specificity vs. iFFR were 96%, 93%, and 100%, respectively. Conclusion A novel deep learning-based CT-FFR algorithm yields excellent diagnostic accuracy compared to invasive hemodynamic indices to detect lesion-specific ischemia and offers the potential to be readily implemented into clinical practice given that it can be performed fast and on-site. Funding Acknowledgement Type of funding sources: None.

ObjectiveTo evaluate the impact of a motion-correction (MC) algorithm, applicable post-hoc and not dependent on extended padding, on the image quality and interpretability of coronary computed tomography angiography (CCTA).MethodsNinety consecutive patients undergoing CCTA on a latest-generation 256-slice CT device were prospectively included. CCTA was performed with prospective electrocardiogram-triggering and the shortest possible acquisition window (without padding) at 75% of the R-R-interval. All datasets were reconstructed without and with MC of the coronaries. The latter exploits the minimal padding inherent in cardiac CT scans with this device due to data acquisition also during the short time interval needed for the tube to reach target currents and voltage (“free” multiphase). Two blinded readers independently assessed image quality on a 4-point Likert scale for all segments.ResultsA total of 1,030 coronary segments were evaluated. Application of MC both with automatic and manual coronary centerline tracking resulted in a significant improvement in image quality as compared to the standard reconstruction without MC (mean Likert score 3.67 [3.50;3.81] vs 3.58 [3.40;3.73], P = 0.005, and 3.7 [3.55;3.82] vs 3.58 [3.40;3.73], P < 0.001, respectively). Furthermore, MC significantly reduced the proportion of non-evaluable segments and patients with at least one non-evaluable coronary segment from 2% to as low as 0.3%, and from 14% to as low as 3%. Reduction of motion artifacts was predominantly observed in the right coronary artery.ConclusionsA post-hoc device-specific MC algorithm improves image quality and interpretability of prospectively electrocardiogram-triggered CCTA and reduces the proportion of non-evaluable scans without any additional radiation dose exposure.

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