Phantom-based experimental validation of computational fluid dynamics simulations on cerebral aneurysms.

PURPOSE: Recently, image-based computational fluid dynamics (CFD) simulation has been applied to investigate the hemodynamics inside human cerebral aneurysms. The knowledge of the computed three-dimensional flow fields is used for clinical risk assessment and treatment decision making. However, the reliability of the application specific CFD results has not been thoroughly validated yet. METHODS: In this work, by exploiting a phantom aneurysm model, the authors therefore aim to prove the reliability of the CFD results obtained from simulations with sufficiently accurate input boundary conditions. To confirm the correlation between the CFD results and the reality, virtual angiograms are generated by the simulation pipeline and are quantitatively compared to the experimentally acquired angiograms. In addition, a parametric study has been carried out to systematically investigate the influence of the input parameters associated with the current measuring techniques on the flow patterns. RESULTS: Qualitative and quantitative evaluations demonstrate good agreement between the simulated and the real flow dynamics. Discrepancies of less than 15% are found for the relative root mean square errors of time intensity curve comparisons from each selected characteristic position. The investigated input parameters show different influences on the simulation results, indicating the desired accuracy in the measurements. CONCLUSIONS: This study provides a comprehensive validation method of CFD simulation for reproducing the real flow field in the cerebral aneurysm phantom under well controlled conditions. The reliability of the CFD is well confirmed. Through the parametric study, it is possible to assess the degree of validity of the associated CFD model based on the parameter values and their estimated accuracy range.

Using flow information to support 3D vessel reconstruction from rotational angiography.

For the assessment of cerebrovascular diseases, it is beneficial to obtain three-dimensional (3D) morphologic and hemodynamic information about the vessel system. Rotational angiography is routinely used to image the 3D vascular geometry and we have shown previously that rotational subtraction angiography has the potential to also give quantitative information about blood flow. Flow information can be determined when the angiographic sequence shows inflow and possibly outflow of contrast agent. However, a standard volume reconstruction assumes that the vessel tree is uniformly filled with contrast agent during the whole acquisition. If this is not the case, the reconstruction exhibits artifacts. Here, we show how flow information can be used to support the reconstruction of the 3D vessel centerline and radii in this case. Our method uses the fast marching algorithm to determine the order in which voxels are analyzed. For every voxel, the rotational time intensity curve (R-TIC) is determined from the image intensities at the projection points of the current voxel. Next, the bolus arrival time of the contrast agent at the voxel is estimated from the R-TIC. Then, a measure of the intensity and duration of the enhancement is determined, from which a speed value is calculated that steers the propagation of the fast marching algorithm. The results of the fast marching algorithm are used to determine the 3D centerline by backtracking. The 3D radius is reconstructed from 2D radius estimates on the projection images. The proposed method was tested on computer simulated rotational angiography sequences with systematically varied x-ray acquisition, blood flow, and contrast agent injection parameters and on datasets from an experimental setup using an anthropomorphic cerebrovascular phantom. For the computer simulation, the mean absolute error of the 3D centerline and 3D radius estimation was 0.42 and 0.25 mm, respectively. For the experimental datasets, the mean absolute error of the 3D centerline was 0.45 mm. Under pulsatile and nonpulsatile conditions, flow information can be used to enable a 3D vessel reconstruction from rotational angiography with inflow and possibly outflow of contrast agent. We found that the most important parameter for the quality of the reconstruction of centerline and radii is the range through which the x-ray system rotates in the time span of the injection. Good results were obtained if this range was at least 135 degrees. As a standard c-arm can rotate 205 degrees, typically one third of the acquisition can show inflow or outflow of contrast agent, which is required for the quantification of blood flow from rotational angiography.

Model-based blood flow quantification from rotational angiography.

For assessment of cerebrovascular diseases, it is beneficial to obtain three-dimensional (3D) information on vessel morphology and haemodynamics. Rotational angiography is routinely used to determine the 3D geometry. In this paper, we propose a method to exploit the same acquisition to determine the blood flow waveform and the mean volumetric flow rate in the large cerebral arteries. The method uses a model of contrast agent dispersion to determine the flow parameters from the spatial and temporal progression of the contrast agent concentration, represented by a flow map. Furthermore, it overcomes artefacts due to the rotation (overlapping vessels and foreshortened vessels at some projection angles) of the C-arm using a reliability map. The method was validated on images from different phantom experiments. We analysed different properties of the flow quantification method, including the influence of noise and the influence of the length of the analysed blood vessel. In most cases, the relative error was between 5% and 10% for the volumetric mean flow rate and between 10% and 15% for the blood flow waveform. The manual interaction took at most one minute and the computational time for the flow quantification was between 4 and 20 min on a PC. From this, we conclude that the method has the potential to give quantitative estimates of blood flow parameters during cerebrovascular interventions.