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. 2022 Feb;13(1):14-40.
doi: 10.1007/s13239-021-00552-9. Epub 2021 Jun 18.

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

Cosmin-Ioan Nita  1   2 , Andrei Puiu  1   2 , Daniel Bunescu  1   2 , Lucian Mihai Itu  3   4 , Viorel Mihalef  5 , Gouthami Chintalapani  6 , Aimee Armstrong  7 , Jeffrey Zampi  8 , Lee Benson  9 , Puneet Sharma  5 , Saikiran Rapaka  5
Affiliations

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

Cosmin-Ioan Nita et al. Cardiovasc Eng Technol. 2022 Feb.

Abstract

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.

Keywords: 3D Rotational Angiography; Aortic coarctation; Hemodynamic modelling; Machine learning; Parameter estimation framework.

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