KEYWORDS: Simulation of CCA and DLA aggregates, Arteries, Magnetic resonance imaging, Image segmentation, Data modeling, 3D modeling, In vivo imaging, Blood, Error analysis, Computational fluid dynamics
Magnetic resonance imaging is often used as a source for reconstructing vascular anatomy for the purpose of computational fluid dynamics (CFD) analysis. We recently observed large discrepancies in such “image-based” CFD models of the normal common carotid artery (CCA) derived from contrast enhanced MR angiography (CEMRA), when
compared to phase contrast MR imaging (PCMRI) of the same subjects. A novel quantitative comparison of velocity profile shape of N=20 cases revealed an average 25% overestimation of velocities by CFD, attributed to a corresponding underestimation of lumen area in the CEMRA-derived geometries. We hypothesized that this was due to blurring of edges in the images caused by dilution of contrast agent during the relatively long elliptic centric CEMRA acquisitions, and confirmed this with MRI simulations. Rescaling of CFD models to account for the lumen underestimation improved agreement with the velocity levels seen in the corresponding PCMRI images, but discrepancies in velocity profile shape remained, with CFD tending to over-predict velocity profile skewing. CFD simulations incorporating realistic inlet velocity profiles and non-Newtonian rheology had a negligible effect on velocity profile skewing, suggesting a role for other sources of error or modeling assumptions. In summary, our findings suggest that caution should be exercised when using elliptic-centric CEMRA data as a basis for image-based CFD modeling, and emphasize the importance of comparing image-based CFD models against in vivo data whenever possible.
KEYWORDS: Simulation of CCA and DLA aggregates, Hemodynamics, Arteries, Independent component analysis, 3D modeling, Magnetic resonance imaging, 3D image processing, Blood, Magnetic resonance angiography, Optical spheres
Recent work from our group has shown the primacy of the bifurcation area ratio and tortuosity in determining the
amount of disturbed flow at the carotid bifurcation, believed to be a local risk factor for the carotid atherosclerosis. We
have also presented fast and reliable methods of extraction of geometry from routine 3D contrast-enhanced magnetic
resonance angiography, as the necessary step along the way for large-scale trials of such local risk factors. In the present
study, we refine our original geometric variables to better reflect the underlying fluid mechanical principles. Flaring of
the bifurcation, leading to flow separation, is defined by the maximum relative expansion of the common carotid artery
(CCA), proximal to the bifurcation apex. The beneficial effect of curvature on flow inertia, via its suppression of flow
separation, is now characterized by the tortuosity of CCA as it enters the flare region. Based on data from 50 normal
carotid bifurcations, multiple linear regressions of these new independent geometric predictors against the dependent
disturbed flow burden reveals adjusted R2 values approaching 0.5, better than the values closer to 0.3 achieved using the
original variables. The excellent scan-rescan reproducibility demonstrated for our earlier geometric variables is shown to
be preserved for the new definitions. Improved prediction of disturbed flow by robust and reproducible vascular
geometry offers a practical pathway to large-scale studies of local risk factors in atherosclerosis.
KEYWORDS: Particles, 3D modeling, Arteries, Motion models, Independent component analysis, Hemodynamics, Computational fluid dynamics, Turbulence, Data modeling, Simulation of CCA and DLA aggregates
The presence of ulceration in carotid artery plaque is an independent risk factor for thromboembolic stroke. However,
the associated pathophysiological mechanisms - in particular the mechanisms related to the local hemodynamics in the
carotid artery bifurcation - are not well understood. We investigated the effect of carotid plaque ulceration on the local
time-varying three-dimensional flow field using computational fluid dynamics (CFD) models of a stenosed carotid
bifurcation geometry, with and without the presence of ulceration. CFD analysis of each model was performed with a
spatial finite element discretization of over 150,000 quadratic tetrahedral elements and a temporal discretization of 4800
timesteps per cardiac cycle, to adequately resolve the flow field and pulsatile flow, respectively. Pulsatile flow
simulations were iterated for five cardiac cycles to allow for cycle-to-cycle analysis following the damping of initial
transients in the solution. Comparison between models revealed differences in flow patterns induced by flow exiting
from the region of the ulcer cavity, in particular, to the shape, orientation and helicity of the high velocity jet through the
stenosis. The stenotic jet in both models exhibited oscillatory motion, but produced higher levels of phase-ensembled
turbulence intensity in the ulcerated model. In addition, enhanced out-of-plane recirculation and helical flow was
observed in the ulcerated model. These preliminary results suggest that local fluid behaviour may contribute to the
thrombogenic risk associated with plaque ulcerations in the stenotic carotid artery bifurcation.
KEYWORDS: Arteries, Spatial resolution, Hemodynamics, Image segmentation, Data acquisition, Magnetic resonance imaging, 3D image processing, Image resolution, 3D modeling, Data modeling
Recent work from our group has demonstrated that the amount of disturbed flow at the carotid bifurcation, believed to be a local risk factor for carotid atherosclerosis, can be predicted from luminal geometric factors. The next step along the way to a large-scale retrospective or prospective imaging study of such local risk factors for atherosclerosis is to investigate whether these geometric features are reproducible and accurate from routine 3D contrast-enhanced magnetic resonance angiography (CEMRA) using a fast and practical method of extraction. Motivated by this fact, we examined
the reproducibility of multiple geometric features that are believed important in atherosclerosis risk assessment. We
reconstructed three-dimensional carotid bifurcations from 15 clinical study participants who had previously undergone baseline and repeat CEMRA acquisitions. Certain geometric factors were extracted and compared between the baseline and the repeat scan. As the spatial resolution of the CEMRA data was noticeably coarse and anisotropic, we also investigated whether this might affect the measurement of the same geometric risk factors by simulating the CEMRA acquisition for 15 normal carotid bifurcations previously acquired at high resolution. Our results show that the extracted geometric factors are reproducible and faithful, with intra-subject uncertainties well below inter-subject variabilities.
More importantly, these geometric risk factors can be extracted consistently and quickly for potential use as disturbed
flow predictors.
KEYWORDS: Angiography, 3D modeling, Hemodynamics, X-rays, Data modeling, Visualization, In vivo imaging, Visual process modeling, Blood circulation, Blood
It has recently become possible to simulate aneurysmal blood flow dynamics in a patient-specific manner via the coupling of 3D X-ray angiography and computational fluid dynamics (CFD). Before such image-based CFD models can be used in a predictive capacity, however, it must be shown that they indeed reproduce the in vivo hemodynamic environment. Motivated by the fact that there is currently no technique for measuring complex blood velocity fields invivo, in this paper we describe how cine X-ray angiograms may be simulated for the purpose of indirectly validating patient-specific CFD models. Mirroring the radiological procedure, a virtual angiogram is constructed by first simulating the time-varying injection of contrast agent into a previously computed patient-specific CFD model. A time-series of images is then constructed by simulating attenuation of X-rays through the simulated 3D contrast-agent flow dynamics. Virtual angiographic images and residence time maps, here derived from an image-based CFD model of a giant aneurysm, are shown to be in excellent agreement with the corresponding clinical images and maps, but only when the interaction between the quasi-steady contrast-agent injection and the pulsatile wash-out are properly accounted for. These virtual angiographic techniques therefore pave the way for validating image-based CFD models against routinely available clinical data, and also provide a means of visualizing complex, 3D blood flow dynamics in a clinically relevant manner. However, they also clearly show how the contrast-agent injection perturbs the normal blood flow dynamics, further highlighting the utility of CFD as a window into the true aneurysmal hemodynamics.
This paper describes our "virtual" Doppler ultrasound (DUS) system, in which colour DUS (CDUS) images and DUS spectrograms are generated on-the-fly and displayed in real-time in response to position and orientation cues provided by a magnetically tracked handheld probe. As the presence of complex flow often confounds the interpretation of Doppler ultrasound data, this system will serve to be a fundamental tool for training sonographers and gaining insight into the relationship between ambiguous DUS images and complex blood flow dynamics. Recently, we demonstrated that DUS spectra could be realistically simulated in real-time, by coupling a semi-empirical model of the DUS physics to a 3-D computational fluid dynamics (CFD) model of a clinically relevant flow field. Our system is an evolution of this approach where a motion-tracking device is used to continuously update the origin and orientation of a slice passing through a CFD model of a stenosed carotid bifurcation. After calibrating our CFD model onto a physical representation of a human neck, virtual CDUS images from an instantaneous slice are then displayed at a rate of approximately 15 Hz by simulating, on-the-fly, an array of DUS spectra and colour coding the resulting spectral mean velocity using a traditional Doppler colour scale. Mimicking a clinical examination, the operator can freeze the CDUS image on-screen, and a spectrogram corresponding to the selected sample volume location is rendered at a higher frame rate of at least 30 Hz. All this is achieved using an inexpensive desktop workstation and commodity graphics card.
Doppler ultrasound (DUS) is widely used to diagnose and plan treatments for vascular diseases, but the relationship between complex blood flow dynamics and the observed DUS signal is not completely understood. In this paper, we demonstrate that Doppler ultrasound can be realistically simulated in a real-time manner via the coupling of a known, previously computed velocity field with a simple model of the ultrasound physics. In the present case a 3D computational fluid dynamics (CFD) model of physiologically pulsatile flow a stenosed carotid bifurcation was interrogated using a sample volume of known geometry and power distribution. Velocity vectors at points within the sample volume were interpolated using a fast geometric search algorithm and, using the specified US probe characteristics and orientation, converted into Doppler shifts for subsequent display as a Doppler spectrogram or color DUS image. The important effect of the intrinsic spectral broadening was simulated by convolving the velocity at each point within the sample volume by a triangle function whose width was proportional to velocity. A spherical sample volume with a Gaussian power distribution was found to be adequate for producing realistic Doppler spectrogram in regions of uniform, jet, and recirculation flow. Fewer than 1000 points seeded uniformly within a radius comprising more than 99% of the total power were required, allowing spectra to be generated from high resolution CFD data at 100Hz frame rates on an inexpensive desktop workstation.
KEYWORDS: Image segmentation, 3D image processing, 3D modeling, Arteries, Ultrasonography, 3D acquisition, 3D metrology, Image processing, Image acquisition, Imaging systems
In this paper, we report on a semi-automatic approach to segmentation of carotid arteries from 3D ultrasound (US) images. Our method uses a deformable model which first is rapidly inflated to approximately find the boundary of the artery, then is further deformed using image-based forces to better localize the boundary. An operator is required to initialize the model by selecting a position in the 3D US image, which is within the carotid vessel. Since the choice of position is user-defined, and therefore arbitrary, there is an inherent variability in the position and shape of the final segmented boundary. We have assessed the performance of our segmentation method by examining the local variability in boundary shape as the initial selected position is varied in a freehand 3D US image of a human carotid bifurcation. Our results indicate that high variability in boundary position occurs in regions where either the segmented boundary is highly curved, or the 3D US image has poorly defined vessel edges.
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