A novel coupling algorithm for computing blood flow in viscoelastic arterial models

Personalized Pre- and Post-Operative Hemodynamic Assessment of Aortic Coarctation from 3D Rotational Angiography

PURPOSE

Coarctation of Aorta (CoA) is a congenital disease consisting of a narrowing that obstructs the systemic blood flow. This proof-of-concept study aimed to develop a framework for automatically and robustly personalizing aortic hemodynamic computations for the assessment of pre- and post-intervention CoA patients from 3D rotational angiography (3DRA) data.

METHODS

We propose a framework that combines hemodynamic modelling and machine learning (ML) based techniques, and rely on 3DRA data for non-invasive pressure computation in CoA patients. The key features of our framework are a parameter estimation method for calibrating inlet and outlet boundary conditions, and regional mechanical wall properties, to ensure that the computational results match the patient-specific measurements, and an improved ML based pressure drop model capable of predicting the instantaneous pressure drop for a wide range of flow conditions and anatomical CoA variations.

RESULTS

We evaluated the framework by investigating 6 patient datasets, under pre- and post-operative setting, and, since all calibration procedures converged successfully, the proposed approach is deemed robust. We compared the peak-to-peak and the cycle-averaged pressure drop computed using the reduced-order hemodynamic model with the catheter based measurements, before and after virtual and actual stenting. The mean absolute error for the peak-to-peak pressure drop, which is the most relevant measure for clinical decision making, was 2.98 mmHg for the pre- and 2.11 mmHg for the post-operative setting. Moreover, the proposed method is computationally efficient: the average execution time was of only [Formula: see text] minutes on a standard hardware configuration.

CONCLUSION

The use of 3DRA for hemodynamic modelling could allow for a complete hemodynamic assessment, as well as virtual interventions or surgeries and predictive modeling. However, before such an approach can be used routinely, significant advancements are required for automating the workflow.

Value of Machine Learning-based Coronary CT Fractional Flow Reserve Applied to Triple-Rule-Out CT Angiography in Acute Chest Pain

Purpose

To evaluate the additional value of noninvasive artificial intelligence (AI)-based CT-derived fractional flow reserve (CT FFR), derived from triple-rule-out coronary CT angiography for acute chest pain (ACP) in the emergency department (ED) setting.

Materials and Methods

AI-based CT FFR from triple-rule-out CT angiography data sets was retrospectively obtained in 159 of 271 eligible patients (102 men; mean age, 57.0 years ± 9.7 [standard deviation]) presenting to the ED with ACP. The agreement between CT FFR (≤ 0.80) and stenosis at triple-rule-out CT angiography (≥ 50%), as well as downstream cardiac diagnostic testing, was investigated. Furthermore, the predictive value of CT FFR for coronary revascularization and major adverse cardiac events (MACE) was assessed over a 1-year follow-up period.

Results

CT FFR and triple-rule-out CT angiography demonstrated agreement in severity of coronary artery disease (CAD) in 52% (82 of 159) of all cases. CT FFR of 0.80 and less served as a better predictor for coronary revascularization and MACE than stenosis of 50% and greater at triple-rule-out CT angiography (odds ratio, 3.4; 95% confidence interval: 1.4, 8.2 vs odds ratio, 2.2; 95% confidence interval: 0.9, 5.3) (P < .01). In the subgroup of patients with additional noninvasive cardiac testing (94 of 159), there was higher agreement as to the presence or absence of significant disease with CT FFR (55%) than with coronary triple-rule-out CT angiography (47%) (P = .23).

Conclusion

CT FFR derived from triple-rule-out CT angiography was a better predictor for coronary revascularization and MACE and showed better agreement with additional diagnostic testing than triple-rule-out CT angiography. Therefore, CT FFR may improve the specificity in identifying patients with ACP with significant CAD in the ED setting and reduce unnecessary downstream testing.© RSNA, 2020See also the commentary by Ihdayhid and Ben Zekry in this issue.

Non-invasive assessment of patient-specific aortic haemodynamics from four-dimensional flow MRI data

We introduce a parameter estimation framework for automatically and robustly personalizing aortic haemodynamic computations from four-dimensional magnetic resonance imaging data. The framework is based on a reduced-order multiscale fluid-structure interaction blood flow model, and on two calibration procedures. First, Windkessel parameters of the outlet boundary conditions are personalized by solving a system of nonlinear equations. Second, the regional mechanical wall properties of the aorta are personalized by employing a nonlinear least-squares minimization method. The two calibration procedures are run sequentially and iteratively until both procedures have converged. The parameter estimation framework was successfully evaluated on 15 datasets from patients with aortic valve disease. On average, only 1.27 ± 0.96 and 7.07 ± 1.44 iterations were required to personalize the outlet boundary conditions and the regional mechanical wall properties, respectively. Overall, the computational model was in close agreement with the clinical measurements used as objectives (pressures, flow rates, cross-sectional areas), with a maximum error of less than 1%. Given its level of automation, robustness and the short execution time (6.2 ± 1.2 min on a standard hardware configuration), the framework is potentially well suited for a clinical setting.