arXiv subjects

Lorenz Raber

Publications and source records attributed to Lorenz Raber.

2 recordsLinked to original sources

arXiv2026

Fluid dynamics informed CCTA-derived geometric parameters in right coronary artery anomalies predict abnormal invasive Adenosine FFR and Dobutamine FFR

Background: Right anomalous aortic origin of coronary arteries (R-AAOCA) involves fixed compression, assessable with adenosine-derived fractional flow reserve (FFRAdnosine), and additional stress-induced dynamic compression captured by dobutamine-derived FFR (FFRDobutamine). We hypothesized that coronary CT angiography (CCTA)-derived fluid dynamics-informed parameters outperform conventional metrics in predicting both FFR types. Methods: We retrospectively analyzed CCTA data from R-AAOCA patients who underwent invasive FFRAdnosine and FFRDobutamine assessment. Parameters were categorized as: (1) conventional metrics (cross-sectional area, perimeter, minor/major axis, intramural lumen area [ILA], effective diameter, area and diameter stenosis ratios) and (2) fluid dynamics-informed metrics (hydraulic diameter, elliptic ratio, circularity, hydraulic diameter stenosis ratio, resistance index [RI], ostial angulation penalty [OAP], and comprehensive stenosis score [CSS]). Hemodynamic relevance was defined as FFR$\leq$0.80. Results: 81 patients were included. FFRAdnosine$\leq$0.80 occurred in 5 (6.2%) and FFRDobutamine$\leq$0.80 in 16 (19.8%) patients. For FFRAdnosine, top discriminators were RI (AUC=0.97), OAP (AUC=0.97), ostial area (AUC=0.96), and CSS (AUC=0.95). For FFRDobutamine, ostial minor diameter led (AUC=0.85), followed by RI (AUC=0.83) and ILA minor diameter (AUC=0.81). RI explained 45% and 43% of FFRAdnosine and FFRDobutamine variance, respectively. At optimal thresholds, RI achieved 100% sensitivity and 95% specificity for FFRAdnosine; ostial minor diameter achieved 100% sensitivity and 57% specificity for FFRDobutamine. Conclusions: In R-AAOCA, CCTA-derived fluid dynamics-informed metrics provide excellent and superior performance compared with conventional geometric parameters in predicting hemodynamic relevance of fixed compression.

physics.med-ph↗

arXiv2025

A novel framework for fully-automated co-registration of intravascular ultrasound and optical coherence tomography imaging data

Aims: To develop a deep-learning (DL) framework that will allow fully automated longitudinal and circumferential co-registration of intravascular ultrasound (IVUS) and optical coherence tomography (OCT) images. Methods and results: Data from 230 patients (714 vessels) with acute coronary syndrome that underwent near-infrared spectroscopy (NIRS)-IVUS and OCT imaging in their non-culprit vessels were included in the present analysis. The lumen borders annotated by expert analysts in 61,655 NIRS-IVUS and 62,334 OCT frames, and the side branches and calcific tissue identified in 10,000 NIRS-IVUS frames and 10,000 OCT frames, were used to train DL solutions for the automated extraction of these features. The trained DL solutions were used to process NIRS-IVUS and OCT images and their output was used by a dynamic time warping algorithm to co-register longitudinally the NIRS-IVUS and OCT images, while the circumferential registration of the IVUS and OCT was optimized through dynamic programming. On a test set of 77 vessels from 22 patients, the DL method showed high concordance with the expert analysts for the longitudinal and circumferential co-registration of the two imaging sets (concordance correlation coefficient >0.99 for the longitudinal and >0.90 for the circumferential co-registration). The Williams Index was 0.96 for longitudinal and 0.97 for circumferential co-registration, indicating a comparable performance to the analysts. The time needed for the DL pipeline to process imaging data from a vessel was <90s. Conclusion: The fully automated, DL-based framework introduced in this study for the co-registration of IVUS and OCT is fast and provides estimations that compare favorably to the expert analysts. These features renders it useful in research in the analysis of large-scale data collected in studies that incorporate multimodality imaging to characterize plaque composition.

eess.IV↗