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Published on: June 3, 2018
Personalized Pre- and Post-Operative Hemodynamic Assessment of Aortic Coarctation from 3D Rotational Angiography
Cosmin-Ioan Nita1,2, Andrei Puiu1,2, Daniel Bunescu1,2
1Advanta, Siemens SRL, 3A Eroilor, 500007, Brasov, Romania.
Insights
This study developed a personalized hemodynamic modeling framework for Coarctation of Aorta (CoA) patients using 3D rotational angiography. The approach accurately computes pressure drop, aiding in pre- and post-intervention assessments.
Area of Science:
- Cardiovascular Imaging
- Biomedical Engineering
- Computational Fluid Dynamics
Background:
- Coarctation of Aorta (CoA) is a congenital narrowing obstructing blood flow.
- Accurate hemodynamic assessment is crucial for CoA patient management.
- Current methods for assessing CoA hemodynamics have limitations.
Purpose of the Study:
- To develop a robust framework for personalized hemodynamic computations in CoA patients.
- To utilize 3D rotational angiography (3DRA) data for non-invasive pressure assessment.
- To improve the prediction of pressure drop in CoA using machine learning.
Main Methods:
- Combined hemodynamic modeling with machine learning (ML) techniques.
- Developed parameter estimation for boundary conditions and wall properties.
- Created an ML-based pressure drop model for diverse CoA anatomies and flow conditions.
Main Results:
- Framework evaluated on 6 patient datasets (pre- and post-intervention).
- Achieved mean absolute errors of 2.98 mmHg (pre-op) and 2.11 mmHg (post-op) for peak-to-peak pressure drop.
- Demonstrated computational efficiency with an average execution time of [Formula: see text] minutes.
Conclusions:
- The proposed framework is robust and accurate for personalized hemodynamic assessment in CoA.
- 3DRA-based hemodynamic modeling offers potential for virtual interventions and predictive analysis.
- Automation of the workflow is necessary for routine clinical adoption.
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.
