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Subjects may change the position of structures in the upper airway such as the vocal folds and soft palate to widen/narrow the airway in order to control inhalation flow rate. As such, small changes in the position of these structures may explain the observation of smaller high velocity regions in the CFD velocity maps, with larger recirculation regions behind these structures. The registration process compares normalized intensities in the high- and low-resolution images and then minimizes the image dissimilarity by moving regions of voxels in one image. The movement of the voxels to provide the lowest image dissimilarity results in a deformation map.
Since it is creep flow, the flows solved were relatively smooth; almost no backflow or recirculation was observed. To evaluate the agreement between 129Xe gas PC MRI and CFD-derived velocity maps over coarser spatial regions, PC MRI data were down-sampled by applying a 3×3 averaging function to the velocity maps. CFD data were correspondingly re-sampled from the initial high-resolution 3D mesh and the down-sampled 2D spatial maps were compared regionally and correlation and histogram plots were generated . This procedure illustrated an improved agreement in spatial flow patterns and Pearson’s correlation coefficients for vFH. The CFD mesh is generated throughout the 3D airway; cells on a sagittal plane are shown here.
To compare CFD simulation results and PC MRI velocimetry, CFD simulations were created with boundary conditions (i.e., airway anatomy and airflow rates) that matched the subjects’ anatomy and physiology as they underwent PC MRI velocimetry. To that end, subject anatomies were also obtained from proton MRI in the same imaging session as 129Xe PC MRI velocimetry. Details of PC MRI, CFD, and methods to ensure CFD boundary conditions represent the conditions of the PC MRI velocimetry are given below. Respiratory CFD simulations produce quantitative results based on boundary conditions which can be obtained non-invasively; e.g. via medical imaging and external respiratory airflow measurements [21–23].
The spread of OSI points was relatively symmetric, demonstrating no significant proportional differences. The average RRT difference was 0.48 (dynes cm−2)−1 for the STB and 0.35 (dynes cm−2)−1 for the CFD in the basilar tip aneurysm, and 1.83 and 0.51 (dynes cm−2)−1 for the STB and CFD, respectively, in the ICA aneurysm. A proportional difference was observed with the RRT, but in this case, the RRT of the VA datasets was larger in magnitude than that of the full resolution. Thus, figure 7 confirms the varying behaviour and sensitivity of each metric to spatial resolution. Higher resolution spatial comparisons between PC MRI and CFD, while desirable, are limited by the spatio-temporal and SNR encoding efficiency of PC MRI acquisition techniques.
CFD simulations were carried out in those models using patient-specific flow conditions extracted from MR velocity measurements of flow in the inlet vessels. The simulation results computed for slices through the vasculature of interest were compared with in-plane velocity measurements acquired with phase-contrast MR imaging in vivo. The sensitivity of the flow fields to inlet flow ratio variations was assessed by simulating five different inlet flow scenarios for each of the basilar aneurysm models. In the majority of cases, altering the inlet flow ratio caused major changes in the flow fields predicted in the aneurysm. A good agreement was found between the flow fields measured in vivo using the in-plane MR velocimetry technique and those predicted with CFD simulations.
- Patient-specific computational modeling of cerebral aneurysms with multiple avenues of flow from 3D rotational angiography images.
- Furthermore, the design of the bioreactor allows for a wide physiological shear rate range.
- Fluid near a wall moves much more slowly than fluid in the center of the conduit; this slow-moving region is often termed the “boundary layer”.
- These modality-specific assumptions and limitations affect the treatment of near-wall velocities and subsequent hemodynamic analysis, likely contributing to the conflicting results reported.
- However, the average OSI bias error caused by voxel averaging was 0.04, a 50% error when normalized by the mean OSI across all datasets.
For the u-velocity component, 95% confidence interval ranges were ±11.4 cm s−1 for 4D flow, ±10.5 cm s−1 for STB and ±10.6 cm s−1 for CFD. For the v-velocity component, the 95% CI ranges were ±23.7, ±22.9 and ±22.6 cm s−1 for 4D flow, STB and CFD, respectively. The ranges for the w-velocity were ±13.9 cm s−1 for 4D flow, ±14.7 cm s−1 for STB and ±14.5 cm s−1 for CFD. In the ICA aneurysm, all modalities maintained similar distribution shapes; however, STB maintained lower magnitude ranges of the velocity distributions than 4D flow and CFD.
Factor D cleaves factor B when the latter is complexed with factor C3b, activating the C3bbb complex, which then becomes the C3 convertase of the alternate pathway. Impact of data base structure in a successful in vitro-in vivo correlation for pharmaceutical products. CFD and PTV steady flow investigation in an anatomically accurate renesource capital abdominal aortic aneurysm. Identification of intra-individual variation in intracranial arterial flow by MRI and the effect on computed hemodynamic descriptors. Models are available as assay kits, starter kits or chips and can be purchased separately or used in contract research based assay development and screening services.
Dynamic similarity–comparing air and xenon
These low velocity regions will generate less MR signal than high moving flow for several reasons. 1) The partial volume effect, which is a well-known source of error in near-wall MRI flow measurements . The finite grid of the MR image is not perfectly conformal with the complex airway anatomy and thus near-wall voxels will comprise tissue, reducing the local signal and potentially skewing the velocity measurements.
The CFD velocities are averaged both across the PC-MRI slice thickness in the right-left direction, and within the sagittal plane. This process is shown in Fig 3C. The temporal mean over the period of each dynamic PC MR image was also calculated in each cell. All 4D flow data were corrected for noise, velocity aliasing and phase offset errors caused by eddy currents and concomitant gradient terms.
Typical approaches to patient-specific hemodynamic studies of cerebral aneurysms use image based computational fluid dynamics and seek to statistically correlate parameters such as wall shear stress and oscillatory shear index to risk of growth and rupture. However, such studies have reported contradictory results, emphasizing the need for in-depth comparisons of volumetric experiments and CFD. In this work, we conducted tomographic particle velocimetry experiments using two patient-specific cerebral aneurysm models under pulsatile flow conditions and processed the particle images using Shake the Box , a particle tracking what works on wall street review method. Although qualitative agreement of flow pathlines across modalities was observed, each modality maintained notably unique spatiotemporal distributions of low normalized WSS regions. Analysis of time averaged WSS , OSI, and Relative Residence Time demonstrated that non-dimensional parameters, such as OSI, may be more robust to the varying assumptions, limitations, and spatial resolutions of each subject and modality. These results suggest a need for further multi-modality analysis as well as development of non-dimensional hemodynamic parameters and correlation of such metrics to aneurysm risk of growth and rupture.
CFD has been adopted clinically for the assessment of hemodynamics [24–27] and is approved for use by the US Food and Drug Administration ; however, adoption in respiratory medicine has been limited to date by difficulty in validating the results. Reynolds’ number was determined to be 0.005 using fluid properties and dimensions of a rat embryonic bronchial tube, classifying the flow as creep flow. The shape and velocity of the annulus were varied, as well as the elasticity of the lung cavity, which was modeled as an expanding bulb at the end of the bronchial tube based on a percentage of the volume displaced by the annulus. After programming the movement of the annulus and tube into a computational fluid dynamics modeling program, velocity and pressure cross-sections were generated for simulations of differing parameters of annulus constriction, bulb expansion, and frequency of contraction.
Most comparisons between CFD simulations and physical airflow measurements have instead been performed in vitro, often yielding good agreement between the methods . However, the limitation of in vitro studies is that experimental setups often capture only a subset of physiology, incorporating anatomical or idealized airway shape and size, but neglecting airway motion, heating and humidification. Therefore, a method to validate CFD simulations in vivo is necessary to take into account the natural respiratory physiology.
In a previous study, we suggested that the cause of thrombus formation depends on not only blood flow velocity but also flow behavior, based on comparison of the results of in vivo experiments and velocity distribution analysis of Computational Fluid Dynamics . However, in order to further improve antithrombogenicity, it is necessary to elucidate the mechanisms of thrombus formation in the artificial lung. In this study, we predicted thrombus formation and its mechanism by recognizing the low flow velocity domains to be thrombus. 129Xe has a higher density and dynamic viscosity than air and therefore the flow patterns and velocities observed during an inhalation will differ.
Material and methods
The spatial averaging process performed to combine CFD results across the PC MRI slice (see Comparing Velocities from PC MRI and CFD–Alignment and Resolution) also has some effect on the velocity profiles in the foot-head and anterior-posterior directions. In addition, CFD simulations afford a short temporal resolution, whereas we calculated the temporal mean of the CFD results over the acquisition period of each dynamic PC MR image to provide a fair comparison. However, this intrinsically loses information about the dynamics of the flow profile within that period. For example, during the acquisition of the first few PC MR images, fine temporal resolution CFD results allow us to track the front of inhaled xenon passing through the airways and visualize the effects of the overall changes in flow-rate during that period. To date, the vast majority of studies use computational fluid dynamics with patient-specific geometries.
Therefore, this choice of geometry should yield a complex and heterogeneous velocity field, allowing a robust comparison between methods. The sagittal plane was chosen for PC MRI acquisitions, as this best captures regions with differing expected velocities within a single imaging plane; CFD was performed in 3D and a sagittal plane was extracted for comparison to PC MRI as discussed below. (Left-Bottom) Average error in wall shear stress , oscillatory shear index , and profitable forex scalping strategy pdf relative residence time caused by virtual voxel averaging of PIV and CFD data. Bland-Altman analysis of WSS, OSI, RRT comparing in vivo 4D flow MRI with all in vitro data. In this work, we used in vivo 4D Flow MRI to inform pulsatile volumetric PIV and CFD modalities in two patient-specific aneurysms. This study reports the first volumetric PIV experiment using patient-specific geometries and pulsatile flow and, thus, the first multi-modality comparison of its kind.
Comparing velocities from PC MRI and CFD–alignment and resolution
However, in in vivo experiments thrombus was confirmed largely on the hollow fiber membrane surface. In the inflow part, the flow stream from the thrombus on the surface of the housing wall tended to pass through the hollow fiber membrane. The distal end of the pneumotachograph was connected to a Tedlar® bag containing hyperpolarized 129Xe during imaging, and open to room air in-between scans. Each subject was asked to inhale the 1L of 129Xe via their nose as slowly as possible to give a long period (10–20 s) for imaging.
The shaded 4D flow inflow rates highlight the difference between the computed inflow and outflow rates and demonstrate the uncertainty of prescribed in vitro boundary conditions. All reported 4D flow inflow rates are based on the average of the total inflow and outflow rates. The maximum inlet flow rate in the basilar tip aneurysm was 4.81, 4.97 and 5.61 ml s−1 for the 4D flow and full resolution STB and CFD, respectively. The average inlet flow rate was 3.09 ml s−1 for the 4D flow, 3.58 ml s−1 for STB and 3.51 ml s−1 for CFD. The trend of the inflow waveforms was similar for all modalities, with STB maintaining the largest temporal variability as expected. For the ICA aneurysm, the maximum inflow rate was 5.81, 6.40 and 5.26 ml s−1 for the 4D flow, and full resolution STB and CFD, respectively.
We can also perform more sophisticated turbulence modeling, such as Large Eddy Simulation , for customers. In vivo validation of numerical prediction for turbulence intensity in an aortic coarctation. Dive into the research topics of ‘In vivo validation of numerical prediction for turbulence intensity in an aortic coarctation’. It is an incurable genetic disease that causes patients to have a variety of gastrointestinal and respiratory problems due to the buildup of mucus in all epithelial tissues.
CFD ELISA Kit
CFD is inherently limited by modeling assumptions, the uncertainty of geometries and boundary conditions obtained from medical images, and validation methods. Other modalities used include 4D Flow MRI measurements, which are under-resolved, noisy, and have a limited dynamic range, as well as particle image velocimetry , which has primarily only been done in 2D and maintains experimental and segmentation limitations. These modality-specific assumptions and limitations affect the treatment of near-wall velocities and subsequent hemodynamic analysis, likely contributing to the conflicting results reported. PC MRI velocimetry of hyperpolarized 129Xe was performed on a 3T MRI scanner using a flexible 129Xe radiofrequency coil positioned around the neck and head. A 1L bag of hyperpolarized 129Xe gas–polarized to ~30% using a Polarean 9820 polarizer (Polarean Imaging Ltd, Durham, NC.)–was inhaled over a period of 10–20 seconds a described above, while a time series of PC MR images was acquired.
Computational fluid dynamics has been the predominant methodology used to study haemodynamics in cerebral aneurysms . However, limited CFD validation and disputed CFD assumptions such as laminar flow remain issues limiting its clinical acceptance . It has also been shown that CFD results can vary significantly based on solver parameters, even when similar geometries and boundary conditions are used . Velocity fields obtained from both in vivo and in vitro 4D flow MRI have also been used, but the low spatio-temporal resolution is a major limitation of this modality . Although high-resolution 4D flow MRI has been shown to capture complex flow patterns , studies have demonstrated that the low resolution causes an underestimation of velocity and WSS magnitudes, particularly when compared with CFD or particle image velocimetry . Among such studies, planar and stereo PIV (2D-2 velocity component) have been the most common .
However, the density of MR-detectable nuclei in air is insufficient for imaging and thus the airway lumina do not exhibit signal on conventional MRI. To overcome this barrier, hyperpolarized noble gas nuclei–most commonly 3He and 129Xe, wherein the nuclear spin polarization is increased by several orders of magnitude–can be used as inhaled contrast agents for airway PC MRI [37–39]. PC MRI of inhaled hyperpolarized gases provides a direct measurement of gas flow in vivo which can be compared to CFD simulation results to quantify systematic differences. The feasibility of comparison between airway CFD and hyperpolarized 3He gas-and water- based PC MRI measurements has been demonstrated in vitro using realistic airway models derived from imaging data [40–43].
Patient-specific computational modeling of cerebral aneurysms with multiple avenues of flow from 3D rotational angiography images. M.C.B. conducted the particle velocimetry experiments, carried out data analysis and drafted the manuscript; S.R. Obtained and pre-processed all in vivo data; and V.L.R. and P.P.V. designed and oversaw the study and all data analysis. All authors contributed to data analysis, critically reviewed and edited the manuscript and gave final approval for publication.
