Jan Brüning1, Florian Hellmeier2, Pavlo Yevtushenko2, Titus Kühne2,3,4, Leonid Goubergrits2. 1. Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, Forum 4 - 0.0561, 13353, Berlin, Germany. jan.bruening@charite.de. 2. Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine, Charité - Universitätsmedizin Berlin, Augustenburger Platz 1, Forum 4 - 0.0561, 13353, Berlin, Germany. 3. Department of Congenital Heart Disease - Unit of Cardiovascular Imaging, German Heart Center Berlin, Berlin, Germany. 4. DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany.
Abstract
PURPOSE: Numerical assessment of the pressure drop across an aortic coarctation using CFD is a promising approach to replace invasive catheter-based measurements. The aim of this study was to investigate and quantify the uncertainty of numerical calculation of the pressure drop introduced during two essential steps of medical image processing: segmentation of the patient-specific geometry and measurement of patient-specific flow rates from 4D-flow-MRI. METHODS: Based on the baseline segmentation, geometries with different stenosis diameters were generated for a sample of ten patients. The pressure drop generated by these geometries was calculated for different volume flow rates using computational fluid dynamics. Based on these simulations, a second order polynomial fit was calculated. Based on these polynomial fits an uncertainty of pressure drop calculation was quantified. RESULTS: The calculated pressure drop values varied strongly between the patients. In four patients, pressure drops above and below the clinical threshold of 20 mmHg were found. The median standard deviation of the pressure drop was 2.3 mmHg. The sensitivity of the pressure drop toward changes in the volume flow rate or the stenosis geometry varied between patients. CONCLUSION: The uncertainty of numerical pressure drop calculation introduced by uncertainties during image segmentation and measurement of volume flow rates was comparable to the uncertainty of pressure drop measurements using invasive catheterization. However, in some patients this uncertainty would have led to different treatment decision. Therefore, patient-specific uncertainty assessment might help to better understand the reliability of a numerically calculated biomarker as the pressure drop across an aortic coarctation.
PURPOSE: Numerical assessment of the pressure drop across an aortic coarctation using CFD is a promising approach to replace invasive catheter-based measurements. The aim of this study was to investigate and quantify the uncertainty of numerical calculation of the pressure drop introduced during two essential steps of medical image processing: segmentation of the patient-specific geometry and measurement of patient-specific flow rates from 4D-flow-MRI. METHODS: Based on the baseline segmentation, geometries with different stenosis diameters were generated for a sample of ten patients. The pressure drop generated by these geometries was calculated for different volume flow rates using computational fluid dynamics. Based on these simulations, a second order polynomial fit was calculated. Based on these polynomial fits an uncertainty of pressure drop calculation was quantified. RESULTS: The calculated pressure drop values varied strongly between the patients. In four patients, pressure drops above and below the clinical threshold of 20 mmHg were found. The median standard deviation of the pressure drop was 2.3 mmHg. The sensitivity of the pressure drop toward changes in the volume flow rate or the stenosis geometry varied between patients. CONCLUSION: The uncertainty of numerical pressure drop calculation introduced by uncertainties during image segmentation and measurement of volume flow rates was comparable to the uncertainty of pressure drop measurements using invasive catheterization. However, in some patients this uncertainty would have led to different treatment decision. Therefore, patient-specific uncertainty assessment might help to better understand the reliability of a numerically calculated biomarker as the pressure drop across an aortic coarctation.
Entities:
Keywords:
Coarctation of the aorta; Computational fluid dynamics; Hemodynamics; Image-based modeling; Non-invasive diagnosis; Uncertainty analysis
Authors: Katharina Vellguth; Jan Brüning; Lennart Tautz; Franziska Degener; Isaac Wamala; Simon Sündermann; Ulrich Kertzscher; Titus Kuehne; Anja Hennemuth; Volkmar Falk; Leonid Goubergrits Journal: Int J Comput Assist Radiol Surg Date: 2019-06-19 Impact factor: 2.924