Patient-individualized boundary conditions for CFD simulations using time-resolved 3D angiography

• Published in: International journal for computer assisted radiology and surgery Vol. 11; no. 6; pp. 1061 - 1069
• Main Authors: Boegel, Marco, Gehrisch, Sonja, Redel, Thomas, Rohkohl, Christopher, Hoelter, Philip, Doerfler, Arnd, Maier, Andreas, Kowarschik, Markus
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2016
• Subjects: Blood Flow Velocity; Cerebral Angiography; Computed Tomography Angiography; Computer Imaging; Computer Science; Computer Simulation; Cone-Beam Computed Tomography; Contrast Media; Health Informatics; Hemodynamics; Humans; Imaging; Imaging, Three-Dimensional; Intracranial Aneurysm - diagnostic imaging; Medicine; Medicine & Public Health; Models, Cardiovascular; Original Article; Pattern Recognition and Graphics; Radiology; Surgery; Vision; Flow quantification; Hemodynamics; Angiography; Computational fluid dynamics; Cone beam CT
• ISSN: 1861-6410; 1861-6429
• DOI: 10.1007/s11548-016-1367-6

Purpose Hemodynamic simulations are of increasing interest for the assessment of aneurysmal rupture risk and treatment planning. Achievement of accurate simulation results requires the usage of several patient-individual boundary conditions, such as a geometric model of the vasculature but also individualized inflow conditions. Methods We propose the automatic estimation of various parameters for boundary conditions for computational fluid dynamics (CFD) based on a single 3D rotational angiography scan, also showing contrast agent inflow. First the data are reconstructed, and a patient-specific vessel model can be generated in the usual way. For this work, we optimize the inflow waveform based on two parameters, the mean velocity and pulsatility. We use statistical analysis of the measurable velocity distribution in the vessel segment to estimate the mean velocity. An iterative optimization scheme based on CFD and virtual angiography is utilized to estimate the inflow pulsatility. Furthermore, we present methods to automatically determine the heart rate and synchronize the inflow waveform to the patient’s heart beat, based on time–intensity curves extracted from the rotational angiogram. This will result in a patient-individualized inflow velocity curve. Results The proposed methods were evaluated on two clinical datasets. Based on the vascular geometries, synthetic rotational angiography data was generated to allow a quantitative validation of our approach against ground truth data. We observed an average error of approximately 5.7 % for the mean velocity, 7.1 % for the pulsatility. The heart rate was estimated very precisely with an average error of about 0.8 % , which corresponds to about 6 ms error for the duration of one cardiac cycle. Furthermore, a qualitative comparison of measured time–intensity curves from the real data and patient-specific simulated ones shows an excellent match. Conclusion The presented methods have the potential to accurately estimate patient-specific boundary conditions from a single dedicated rotational scan.