BACKGROUND: Anomalous aortic origin of coronary artery (AAOCA) with intramural segment is associated with risk of sudden cardiac death, probably related to a compressive mechanism exerted by the aorta. However, the intramural compression occurrence and magnitude during the cardiac cycle remain unknown. We hypothesized that (1) in end diastole, the intramural segment is narrower, more elliptic, and has greater resistance than extramural segment; (2) the intramural segment experiences a further compression in systole; and (3) morphometry and its systolic changes vary within different lumen cross-sections of the intramural segment. METHODS: Phasic changes of lumen cross-sectional coronary area, roundness (minimum/maximum lumen diameter), and hemodynamic resistance (Poiseuille law for noncircular sections) were derived from intravascular ultrasound pullbacks at rest for the ostial, distal intramural, and extramural segments. Data were obtained for 35 AAOCA (n=23 with intramural tract) after retrospective image-based gating and manual lumen segmentation. Differences between systolic and end-diastolic phases in each section, between sections of the same coronary, and between AAOCA with and without intramural tract were assessed by nonparametric statistical tests. RESULTS: In end diastole, both the ostial and distal intramural sections were more elliptical ( P <0.001) than the reference extramural section and the correspondent sections in AAOCA without intramural segment. In systole, AAOCA with intramural segment showed a flattening at the ostium (−6.76% [10.82%]; P =0.024) and a flattening (−5.36% [16.56%]; P =0.011), a narrowing (−4.62% [11.38%]; P =0.020), and a resistance increase (15.61% [30.07%]; P =0.012) at the distal intramural section. No-intramural sections did not show morphological changes during the entire cardiac cycle. CONCLUSIONS: AAOCA with intramural segment has pathological segment-specific dynamic compression mainly in the systole under resting conditions. Studying AAOCA behavior with intravascular ultrasound during the cardiac cycle may help to evaluate and quantify the severity of the narrowing.

This work aimed to automatically segment and classify the coronary arteries with either normal or anomalous origin from the aorta (AAOCA) using convolutional neural networks (CNNs), seeking to enhance and fasten clinician diagnosis. We implemented three single-view 2D Attention U-Nets with 3D view integration and trained them to automatically segment the aortic root and coronary arteries of 124 computed tomography angiographies (CTAs), with normal coronaries or AAOCA. Furthermore, we automatically classified the segmented geometries as normal or AAOCA using a decision tree model. For CTAs in the test set (n = 13), we obtained median Dice score coefficients of 0.95 and 0.84 for the aortic root and the coronary arteries, respectively. Moreover, the classification between normal and AAOCA showed excellent performance with accuracy, precision, and recall all equal to 1 in the test set. We developed a deep learning-based method to automatically segment and classify normal coronary and AAOCA. Our results represent a step towards an automatic screening and risk profiling of patients with AAOCA, based on CTA.

Background: Anomalous Aortic Origin of a Coronary Artery (AAOCA) is a congenital condition that can lead to ischemia and sudden cardiac death. Current diagnostic tools are unable to fully quantify the pathological behavior that occurs mainly with physical effort. Methods: Patients’ computed tomography scans and centerline-based geometric quantities were used to develop three-dimensional computer-aided design models of the main anatomical variants of AAOCA. Blood pressure ranging from rest to extreme effort was simulated through structural finite element analyses, and the pressurized geometries were analyzed to evaluate coronary lumen cross-sectional areas and variations at the different loading conditions. Results: We simulated 39 subjects, demonstrating the ability to reproduce accurately the patient-specific anatomy of different AAOCA variants and capture pathological behaviors. AAOCAs with intramural courses showed compression along the proximal segment with a caliber reduction ranging from 0.14% to 18.87% at different pressure levels. The percentage of proximal narrowing relative to the distal segment was greater than any other type of anomalous course and exceeded 50% with simulated exertion. Conclusions: The present study proposes a computational pipeline to investigate conditions not reproducible in clinical practice, providing information to support decision-making in the management of AAOCA patients.

Aims: We aim to explore whether biomechanical simulations can identify coronary compression sites and support pre-operative assessment as a proof-of-concept approach in subjects with anomalous aortic origin of a coronary artery. Methods and results: Patient-specific computer-aided engineering models of the aortic root and coronary arteries were developed from computed tomography scans of subjects with anomalous aortic origin of coronary arteries, retrospectively evaluated to include variants with and without an intramural segment. The fixed and dynamic components of stenosis were assessed by simulating blood pressure from rest to extreme effort and by retrieving the percentage of narrowing of the proximal anomalous lumen with reference to the distal segment. Patients were classified as potential candidates for surgery if the degree of stenosis was >50% in any of the simulated pressure scenarios. A total of 31 patients (19 with interarterial course and intramural segment, 12 with non-intramural course) were simulated. Simulations showed promising trends of agreement with retrospective clinical treatment decisions (13 aligned with surgery, 11 aligned with non-surgical management). This concordance with surgical decisions should not be interpreted as validation for predicting myocardial ischaemia. Conclusion: Computational models of the anomalous aortic origin of a coronary artery have not yet been routinely introduced into the clinical setting. Our method demonstrates potential as a proof-of-concept tool for identifying coronary compression-prone regions and estimating stenosis severity, but further studies are needed to validate its diagnostic and clinical utility.

Background: Anomalous Aortic Origin of a Coronary Artery (AAOCA) high‐risk anatomical features (intramural segment, acute take‐off angle, slit‐like ostium) cause coronary stenosis leading to myocardial infarction. How these features alter coronary physiology remains unclear. Aims: We aimed to evaluate their impact on coronary blood supply using continuous thermodilution. Methods: We prospectively enrolled 51 AAOCA patients undergoing continuous thermodilution. Subjects were classified into an ischemic risk (IR) group (intramural segment, acute angle, oval or slit ostium) or non‐ischemic risk (no‐IR) group. Non‐anomalous coronary arteries served as controls. We measured absolute coronary blood flow (CBF) and microvascular resistance. Results: Subjects (median age 48.4 years) presented mostly with angina, syncope, and arrhythmia. Final analysis included 48 AAOCA cases (31 IR, 17 no‐IR) and 45 controls. The IR group had a median IM length of 9 mm, take‐off angle of 31.9°, and a slit‐like or oval ostium. The no‐IR group had a median angle of 51.5° and an oval or round ostium. At rest, absolute CBF and microvascular resistance showed no significant differences among groups. However, during hyperemia, the IR group had significantly reduced CBF compared to controls (179 vs. 290 mL/min, p = 0.015). The IR group demonstrated higher microvascular resistance at rest (1473 vs. 1150, p = 0.024) and during hyperemia (433 vs. 305, p = 0.012), indicating persistently increased coronary vascular tone. Conclusions: High‐risk anatomical features of AAOCA are associated with altered coronary physiology. An intramural segment, acute take‐off angle, and slit‐like or oval ostium are linked to reduced hyperemic coronary blood flow due to increased microvascular resistance.