Cosmin-Ioan Nita1,2, Andrei Puiu1,2, Daniel Bunescu1,2, Lucian Mihai Itu3,4, Viorel Mihalef5, Gouthami Chintalapani6, Aimee Armstrong7, Jeffrey Zampi8, Lee Benson9, Puneet Sharma5, Saikiran Rapaka5. 1. Advanta, Siemens SRL, 3A Eroilor, 500007, Brasov, Romania. 2. Automation and Information Technology, Transilvania University of Brasov, 5 Mihai Viteazu, 5000174, Brasov, Romania. 3. Advanta, Siemens SRL, 3A Eroilor, 500007, Brasov, Romania. lucian.itu@siemens.com. 4. Automation and Information Technology, Transilvania University of Brasov, 5 Mihai Viteazu, 5000174, Brasov, Romania. lucian.itu@siemens.com. 5. Digital Services, Digital Technology & Innovation, Siemens Healthineers, 755 College Road, Princeton, NJ, 08540, USA. 6. Advanced Therapies, Siemens Healthineers, Malvern, USA. 7. The Heart Center, Nationwide Children's Hospital, Columbus, OH, USA. 8. The Division of Pediatric Cardiology, University of Michigan, Ann Arbor, MI, USA. 9. The Division of Cardiology, The Labatt Family Heart Center, The Hospital for Sick Children, Toronto, Canada.
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.
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.
Authors: D Gallo; G De Santis; F Negri; D Tresoldi; R Ponzini; D Massai; M A Deriu; P Segers; B Verhegghe; G Rizzo; U Morbiducci Journal: Ann Biomed Eng Date: 2011-10-19 Impact factor: 3.934
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Authors: Jordi Alastruey; Ashraf W Khir; Koen S Matthys; Patrick Segers; Spencer J Sherwin; Pascal R Verdonck; Kim H Parker; Joaquim Peiró Journal: J Biomech Date: 2011-07-02 Impact factor: 2.712