Recently, new techniques for minimally invasive aortic valve implantation have been developed generating a need for planning tools that assess valve anatomy and guidance tools that support implantation under x-ray guidance. Extracting the aortic valve anatomy from CT images is essential for such tools and we present a model-based method for that purpose. In addition, we present a new method for the detection of the coronary ostia that exploits the model-based segmentation and show, how a number of clinical measurements such as diameters and the distances between aortic valve plane and coronary ostia can be derived that are important for procedure planning. Validation results are based on accurate reference annotations of 20 CT images from different patients and leave-one-out tests. They show that model adaptation can be done with a mean surface-to-surface error of 0.5mm. For coronary ostia detection a success rate of 97.5% is achieved. Depending on the measured quantity, the segmentation translates into a root-mean-square error between 0.4 − 1.2mm when comparing clinical measurements derived from automatic segmentation and from reference annotations.
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.
Abstract. For assessment of cerebrovascular diseases, it is beneficial to obtain three-dimensional (3D) information on vessel morphology and hemodynamics. Rotational angiography is routinely used to determine the 3D geometry and we propose a method to exploit the same acquisition to determine the blood flow waveform and the mean volumetric flow rate. The method uses a model of contrast agent dispersion to determine the flow parameters from the spatial and temporal development of the contrast agent concentration, represented by a flow map. Furthermore, it also overcomes artifacts due to the rotation of the c-arm using a newly introduced reliability map. The method was validated on images from a computer simulation and from a phantom experiment. With a mean error of 11.0% for the mean volumetric flow rate and 15.3% for the blood flow waveform from the phantom experiments, we conclude that the method has the potential to give quantitative estimates of blood flow parameters during cerebrovascular interventions.
Medical imaging and computational fluid dynamics (CFD) modeling have been combined to obtain detailed knowledge of local hemodynamics, known to play an important role in cardiovascular diseases. Given the absence of a "gold standard" to measure the blood velocities in vivo, simulation of X-ray angiograms was proposed as a method to indirectly validate the accuracy of image-based CFD analysis. This paper presents a method to simulate the contrast agent transport and the creation of virtual angiograms which takes into account the physics of the contrast agent injection and X-ray transmission. The simulated and acquired angiograms are compared by analyzing the spatial and temporal development of the contrast agent concentration represented by a flow map. This approach was tested with in vitro experiments.Index Terms-X-ray imaging, virtual angiography, medical image-based simulation, blood flow, computational fluid dynamics
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