Florian Hellmeier1, Jan Brüning1, Simon Sündermann2,3,4, Lina Jarmatz1, Marie Schafstedde1,5,6, Leonid Goubergrits1,7, Titus Kühne1,4,6, Sarah Nordmeyer1,6. 1. Charité - Universitätsmedizin Berlin, Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Berlin, Germany. 2. Charité - Universitätsmedizin Berlin, Department of Cardiovascular Surgery, Berlin, Germany. 3. German Heart Center Berlin, Department of Cardiothoracic and Vascular Surgery, Berlin, Germany. 4. DZHK (German Center for Cardiovascular Research), Partner Site Berlin, Berlin, Germany. 5. Berlin Institute of Health (BIH), Berlin, Germany. 6. German Heart Center Berlin, Department of Congenital Heart Disease, Berlin, Germany. 7. Einstein Center Digital Future, Berlin, Germany.
Abstract
Objectives: Prediction of aortic hemodynamics after aortic valve replacement (AVR) could help optimize treatment planning and improve outcomes. This study aims to demonstrate an approach to predict postoperative maximum velocity, maximum pressure gradient, secondary flow degree (SFD), and normalized flow displacement (NFD) in patients receiving biological AVR. Methods: Virtual AVR was performed for 10 patients, who received actual AVR with a biological prosthesis. The virtual AVRs used only preoperative anatomical and 4D flow MRI data. Subsequently, computational fluid dynamics (CFD) simulations were performed and the abovementioned hemodynamic parameters compared between postoperative 4D flow MRI data and CFD results. Results: For maximum velocities and pressure gradients, postoperative 4D flow MRI data and CFD results were strongly correlated (R 2 = 0.75 and R 2 = 0.81) with low root mean square error (0.21 m/s and 3.8 mmHg). SFD and NFD were moderately and weakly correlated at R 2 = 0.44 and R 2 = 0.20, respectively. Flow visualization through streamlines indicates good qualitative agreement between 4D flow MRI data and CFD results in most cases. Conclusion: The approach presented here seems suitable to estimate postoperative maximum velocity and pressure gradient in patients receiving biological AVR, using only preoperative MRI data. The workflow can be performed in a reasonable time frame and offers a method to estimate postoperative valve prosthesis performance and to identify patients at risk of patient-prosthesis mismatch preoperatively. Novel parameters, such as SFD and NFD, appear to be more sensitive, and estimation seems harder. Further workflow optimization and validation of results seems warranted.
Objectives: Prediction of aortic hemodynamics after aortic valve replacement (AVR) could help optimize treatment planning and improve outcomes. This study aims to demonstrate an approach to predict postoperative maximum velocity, maximum pressure gradient, secondary flow degree (SFD), and normalized flow displacement (NFD) in patients receiving biological AVR. Methods: Virtual AVR was performed for 10 patients, who received actual AVR with a biological prosthesis. The virtual AVRs used only preoperative anatomical and 4D flow MRI data. Subsequently, computational fluid dynamics (CFD) simulations were performed and the abovementioned hemodynamic parameters compared between postoperative 4D flow MRI data and CFD results. Results: For maximum velocities and pressure gradients, postoperative 4D flow MRI data and CFD results were strongly correlated (R 2 = 0.75 and R 2 = 0.81) with low root mean square error (0.21 m/s and 3.8 mmHg). SFD and NFD were moderately and weakly correlated at R 2 = 0.44 and R 2 = 0.20, respectively. Flow visualization through streamlines indicates good qualitative agreement between 4D flow MRI data and CFD results in most cases. Conclusion: The approach presented here seems suitable to estimate postoperative maximum velocity and pressure gradient in patients receiving biological AVR, using only preoperative MRI data. The workflow can be performed in a reasonable time frame and offers a method to estimate postoperative valve prosthesis performance and to identify patients at risk of patient-prosthesis mismatch preoperatively. Novel parameters, such as SFD and NFD, appear to be more sensitive, and estimation seems harder. Further workflow optimization and validation of results seems warranted.
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