Name: A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis (Video) | JoVE Journal
Uploaded: 2025-08-12T13:30:00.000+00:00
Duration: 9 min 36 s
Description: Learn a computational MRI protocol for analyzing plaque morphology and hemodynamics in carotid artery stenosis patients. This method uses CRIMSON software and Navier-Stokes simulations to assess stroke risk factors.

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This study was supported by the National Institute of Health F32HL168968 and the Frederick A. Coller Surgical Society.

The authors declare no conflicts of interest.

Here, we presented a protocol to non-invasively characterize the hemodynamic loads and plaque composition across an ICA stenosis, thus providing a more comprehensive assessment of embolic potential than current diagnostic modalities which assess percentage stenosis alone. We begin by obtaining patient imaging and pressure data in both a retrospective and prospective manner, including CTA, PC-MRI, and blood pressure cuff data to inform our CFD models. Moreover, we tune the boundary conditions in our model, specifically the Windkessel model, to correspond to known patient data. As such, this protocol allows for the collection of precise and patient-specific data pertaining to physiologically relevant risk factors for plaque embolism and stroke.
Informing models and boundary conditions with physiologically accurate and patient-specific data is critical for accurate simulation results. Some computational simulations in the cerebrovascular space rely on DUS, numerical methods, or non-patient specific assumptions to derive inflow waveforms21,29,30,31. The use of DUS is appealing given that it is widely available, frequently used in clinical settings, has lower cost, and is readily accessible. However, PC-MRI is generally considered to be a more accurate method for measuring flow32,33,34. PC-MRI can directly quantify velocity at multiple locations within the lumen, thus accommodating for asymmetries within the flow field within a vessel and thus provides a more comprehensive depiction of flow dynamics32,33. PC-MRI is also not subject to operator-specific biases introduced by DUS such as angle of interrogation and measurement selection location. On the other hand, DUS is often operator-dependent and is less precise in capturing vessel area and complex flow patterns often leading to inaccurate flows. Nonetheless, PC-MRI flow measurements are not perfect with an approximate 10% error35,36. Special attention should be made to ensure appropriate vessel encoding, maintaining an image plane that is orthogonal to the vessel axial, appropriate temporal and special resolutions, and minimization of phase offset errors37. Lastly, MRI may overestimate stenosis compared to CTA which should be considered when assessing patient geometry38. Future work, focused on comparing hemodynamic outputs from CFD models informed by DUS-flow waveforms and those informed by PC-MRI flow waveforms are warranted.
The choice of outflow boundary conditions can have a significant influence on velocity and pressure fields in CFD simulations of blood flow. In our approach, we elected to impose a parabolic outflow waveform to the ECA and couple the ICA to a three-element Windkessel model. This approach for boundary condition specification allows for robust enforcement of conservation of mass between inlet and outlets, while also enabling accurate matching of the patient&#39;s blood pressure39. Thus, we felt that this would provide the most accurate depiction of ICA hemodynamics. However, given that we are imposing a flow waveform to one of our model outlets (i.e., the ECA) is important to ensure that the outflow waveform is synchronized to the CCA inflow waveform39. In our approach this was made possible by the collection of our flow data from 2D-cardiac gated PC-MRI. However, in cases where obtaining such data is impractical, a different boundary condition approach may be advantageous (i.e., coupling both the ECA and ICA to three-element Windkessel models) so that assumptions do not have to be made in the temporal alignment of inflow and outflow waveforms39.
There are important limitations of this protocol to keep in mind. First, because this modeling approach only consists of the ipsilateral carotid bifurcation, it does not include the circle of Willis and/or important factors that impact cerebral hemodynamics, such as the presence of collaterals or the extent of contralateral ICA stenosis. Patients with incomplete collateral pathways in the circle of Willis have been demonstrated to have higher rates of severe stroke and worse prognosis after stroke40,41,42. Furthermore, the presence of patent collaterals has been associated with a reduced risk of stroke and transient ischemic attack9,43,44. Additionally, several studies have demonstrated that the presence of a contralateral ICA stenosis (or occlusion) impacts the ipsilateral ICA velocities45,46,47,48. Moreover, our group has recently demonstrated that severe contralateral ICA stenoses and occlusions impact ipsilateral ICA WSS and pressures49. However, modeling of the entire circle of Willis is resource-intensive and limits the clinical utility of our current protocol.
An additional limitation of our model is that we did not allow changes in resistance and compliance at the ICA outlet and thus do not account for cerebral autoregulation which may affect the distribution of blood flow with different stenosis severities. Furthermore, we modeled the vessel walls as rigid, rather than deformable. However, because carotid artery stenosis is associated with increased vessel stiffness, we feel that a rigid wall assumption is reasonable. In addition, there are not well-defined thresholds of WSS and PG for differing levels of ICA stenosis and associations with stroke risk are not yet defined, thus in our current model we have not validated clinical translatability and cannot yet estimate a patient&#39;s stroke risk. Finally, the qMatch MRI sequence is not readily available on standard MRI scanners. qMatch requires a 3T MRI machine and requires manual importing of the sequence as it is not a standard clinical MRI sequence. Moreover, as our protocol specifies qMatch requires complex post-processing in MATLAB which may further limit its generalizability to widespread clinical use.
New metrics to define and assess the hemodynamic impact of ICA stenosis, and better stratify individualized stroke risk are warranted, as evidenced by the current top research priority from the Society of Vascular Surgery: to develop diagnostic tools, imaging techniques, and selection strategies aimed at identifying patients who would benefit from treatment of asymptomatic ICA stenosis50. This protocol is well equipped to non-invasively characterize both the hemodynamic loads and plaque composition across an ICA stenosis, thus providing a more comprehensive assessment of ICA plaque embolic potential than current diagnostic modalities. In our future work, we seek to better define the association of hemodynamics metrics (such as WSS and PG) with risk of ICA plaque embolism and stroke.

Internal carotid artery (ICA) stenosis is a leading cause of stroke, long-term disability, and death1,2,3,4,5,6,7. Current assessment and management of ICA stenosis is based on the estimation of percentage stenosis via duplex ultrasound (DUS) velocities or cross-sectional anatomy [computed tomography angiogram (CTA) and/or magnetic resonance imaging (MRI)]. However, percentage stenosis does not account for physiologically relevant risk factors for stroke such as plaque vulnerability and hemodynamic loads across the plaque8,9,10,11,12,13,14. Although reduced stroke risk following carotid endarterectomy (CEA) has been demonstrated in symptomatic patients with greater than 50% stenosis, the benefit of CEA in asymptomatic patients is debated3,4. In fact, many surgeons reserve operative intervention for those with stenotic lesions &#62;80% and/or in cases with high-risk (vulnerable) plaque morphology15. Improved methods of determining which ICA stenoses are at risk of plaque embolism and thus would benefit from CEA are warranted.
Quantitative multi-contrast atherosclerosis characterization (qMatch) is an MRI technique that utilizes low-rank modeling to enable high-resolution 3D imaging that provides co-registered multi-contrast dark-blood and bright blood images, and relaxometry images for comprehensive and quantitative assessment of carotid arterial plaques16,17. qMatch has improved 3D isotropic resolution, large anatomic coverage, and quantitative assessment of carotid artery plaque burden compared to conventional MRI. Patient-specific computational fluid dynamics (CFD) can be used to characterize the hemodynamic loads on the plaque, thus providing unique information regarding the hemodynamic and biomechanical risk of cerebrovascular embolic events18,19,20,21,22,23. Knowledge of the composition of the carotid plaque and hemodynamic loads on the plaque could be used to provide a more comprehensive assessment of embolic potential than percentage stenosis alone. In this work, we present a protocol that uses both qMatch MRI and MRI-informed CFD to identify differences in plaque composition and hemodynamics across an ICA stenosis.

<table><tbody><tr><td>CRIMSON</td><td>N/A</td><td>N/A</td><td>Open source online software</td></tr><tr><td>Horos</td><td>Horos</td><td>N/A</td><td>Open source online software</td></tr><tr><td>MATLAB version 14</td><td>Mathworks</td><td>N/A</td><td></td></tr><tr><td>Paraview</td><td>N/A</td><td>N/A</td><td>Open source online software</td></tr><tr><td>Siemens 3T VIDA MRI scanner&amp;nbsp;</td><td>Siemens Healthineers</td><td>N/A</td><td></td></tr></tbody></table>

a magnetic resonance imaging-based computational protocol for analysis of plaque morphology and hemodynamics in patients with carotid artery stenosis

A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis

Current assessment and management of internal carotid artery (ICA) stenosis is based on the estimation of percentage stenosis via duplex ultrasound (DUS) or computed tomography angiography (CTA), which does not account for physiologically relevant risk factors for stroke, such as plaque vulnerability and hemodynamics. Knowledge of the composition of the carotid plaque and hemodynamic loads on the plaque can be used to provide a much more complete assessment of the embolic potential of the plaque rather than using percentage stenosis alone. Through pairing magnetic resonance imaging (MRI) and patient-specific computational fluid dynamics (CFD), differences in both hemodynamics across an ICA stenosis and plaque composition can be identified. Quantitative multi-contrast atherosclerosis characterization (qMatch) MRI allows for detailed analysis of plaque composition. CFD models can be created using phase contrast (PC) MRI, which can be used to obtain flow waveforms and CTA and/or time-of-flight (TOF)-MRI anatomy. After creating a 3D geometric model of the carotid bifurcation, PC-MRI derived waveforms are prescribed to the common carotid artery inflow and external carotid artery outflow. A three-element Windkessel model, which is iteratively tuned to match the patient's blood pressure, is then prescribed to the ICA. Finally, solutions to the incompressible Navier-Stokes equations are obtained to provide high-resolution velocity and pressure and thus capture hemodynamics across the carotid bifurcation and ICA stenosis. This article provides a detailed protocol that allows for non-invasive and patient-specific characterization of plaque composition and hemodynamic loads of patients with ICA stenosis.

Using this MRI-informed CFD workflow paired with qMatch MRI allows for identification of the hemodynamic loads across and ICA stenosis and the specific components of the plaque. We first begin with ensuring that we have a high-quality mesh to allow for accurate representation of flow features in critical areas. A final mesh should contain an adequate number of mesh elements with low aspect ratios (Figure 1A). A coarse mesh with high aspect ratios will likely lead to inaccurate simulation results. We then move forward with the specification of our boundary conditions (Figure 1B). After successful completion of the simulation and appropriate boundary condition tuning, non-invasive and patient-specific hemodynamics can be collected.
Specific hemodynamic metrics that can be measured, including but not limited to velocity, flow, pressure (including pressure ratios and pressure gradients), WSS, and OSI. Figure 2 shows a representative velocity profile across the carotid bifurcation and ICA stenosis. Visualization of the maximum velocity profile throughout the cardiac cycle can serve as a surrogate for a DUS-derived velocity waveform. Thus, both PSV and end-diastolic velocity (EDV) can be approximated. Figure 3 shows two representative examples of the pressure (mmHg) across the carotid bifurcation and ICA stenosis. A pressure gradient can be measured by collecting pressure waveforms proximal and distal to the stenosis.
In Figure 3A, there is minimal to no difference in pressure proximal to (red line) and distal to (blue line) the stenosis. However, in Figure 3B, there is a large difference in pressure proximal to (red line) and distal to (blue line) the stenosis. Figure 4 shows two representative examples of the WSS (Pa) mapped across the carotid bifurcation and ICA stenosis. In Figure 4A, there is a low WSS across the stenosis, whereas in Figure 4B, there is a large WSS across the stenosis. Figure 5 shows a comparison of OSI mapped across the carotid bifurcation before (Figure 5A: pre-operative) and after (Figure 5B: post-operative) CEA. Post-operative maps depict areas of higher OSI compared to pre-operative.
After appropriate postprocessing of the qMatch images, a dataset with six sets of DICOMs will be generated, including dark blood, T1-weighted, T2-weighted, MRA, qMatch T1 Map, and qMatch T2 Map sequences. Using these data sets, plaque components including calcium, IPH, LRNC, and fibrous cap thickness and/or rupture can be visualized and quantified (using the T1 map and T2 map sequences). Table 1 depicts the general characteristics of each plaque component on qMatch datasets. Figure 6 shows a representative qMatch dataset from a patient with IPH. The outline of the ICA is depicted with a solid white line, while the flow lumen is depicted with the dashed white line, and the plaque is depicted with the dashed yellow line. Features of IPH (solid red line) demonstrated by hyperintense signal in the T1-weighted image and lowered T1 measurement in the T1 map. Figure 7 shows a representative qMatch dataset from a patient with heavily calcified plaque. The outline of the ICA is depicted with solid white line, while the flow lumen is depicted with the dashed white line. Calcified portion of the plaque (dashed orange line) demonstrated by hypo-intense signal in the dark blood, T1-weighted, and T2-weighted images.
<img alt='"Cardiovascular MRI geometry, mesh, flow analysis; vessel contours, boundary conditions diagram."' class="xfigimg" src="/files/ftp_upload/68447/68447fig1.jpg" data-altupdatedat="1762509930000" data-alt="Figure 1"> 
Figure 1: Overview of computational fluid dynamics modeling method. (A) Creation of patient-specific geometry and meshing as well as (B) specification of boundary conditions. (A) De-identified DICOM image data from CTA is imported into CRIMSON, and the anatomy of interest (including the CCA, ICA, and ECA) is determined. Centerline points are placed along the length of each vessel within the anatomy of interest. The boundaries of the vessel wall are specified by adding contours. Vessel branches are lofted, then combined with a fillet operation. The final geometric model is then discretized into a mesh, consisting of multiple tetrahedral elements with local mesh refinement at the level of the stenosis. (B) A 3-element Windkessel is prescribed to the ICA outlet to allow for variations in pressure and velocity. 2D cardiac-gated PC-MRI is obtained at the level of the CCA at C5 (red circle and ellipse) and above the carotid bifurcation at the proximal ECA (orange circle and ellipse) and mid ICA distal to the lesion (blue circle and ellipse) to measure volumetric blood flow waveforms. A flow waveform is prescribed to the CCA inlet and the ECA outlet. Abbreviations: CTA = computed tomography angiography; CCA = common carotid artery; ICA = internal carotid artery; ECA = external carotid artery; PC = Phase Contrast. <a href="https://www.jove.com/files/ftp_upload/68447/68447fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Blood flow velocity analysis; graph and stenosis diagram, arterial narrowing impact study." class="xfigimg" src="/files/ftp_upload/68447/68447fig2.jpg" data-altupdatedat="1762509930000" data-alt="Figure 2"> 
Figure 2: Velocity information from CFD workflow. Right) Velocity (cm/s) mapped to a model of a carotid bifurcation including the CCA, ECA, and ICA with a severe stenosis in the anterior view. Left) Maximum velocity over time for one cardiac cycle can be visualized, serving as a surrogate for duplex ultrasound. Abbreviations: CCA = common carotid artery; ECA = external carotid artery; ICA = internal carotid artery. <a href="https://www.jove.com/files/ftp_upload/68447/68447fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Blood flow simulation; pressure variation in carotid arteries; diagram and graphs; hemodynamics analysis." class="xfigimg" src="/files/ftp_upload/68447/68447fig3.jpg" data-altupdatedat="1762509930000" data-alt="Figure 3"> 
Figure 3: Representative example of pressure (mmHg) mapped across the carotid bifurcation for two cases in the anterior view. Pressure is mapped to geometric models of the CCA, ECA, and ICA. (A) Case with minimal to no difference in pressure proximal to (red line, red pressure waveform) and distal to (blue line, blue pressure waveform) the ICA stenosis. (B) Case with large difference in pressure proximal to (red line, red pressure waveform) and distal to (blue line, blue pressure waveform) the ICA stenosis. Abbreviations: CCA = common carotid artery; ECA = external carotid artery; ICA = internal carotid artery. <a href="https://www.jove.com/files/ftp_upload/68447/68447fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Carotid artery wall shear stress (WSS) simulation; diagram for vascular analysis, low/high WSS areas." class="xfigimg" src="/files/ftp_upload/68447/68447fig4.jpg" data-altupdatedat="1762509930000" data-alt="Figure 4"> 
Figure 4: Representative example of wall shear stress (Pa) mapped across the carotid bifurcation for two cases in the anterior view. WSS is mapped to geometric models of the CCA, ECA, and ICA. (A) Case with low WSS across the ICA stenosis. (B) Case with large WSS across the ICA stenosis. Abbreviations: WSS = wall shear stress; CCA = common carotid artery; ECA = external carotid artery; ICA = internal carotid artery. <a href="https://www.jove.com/files/ftp_upload/68447/68447fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Carotid artery stenosis simulation, pre- and post-operative models, lesion repair analysis diagram." class="xfigimg" src="/files/ftp_upload/68447/68447fig5.jpg" data-altupdatedat="1762509930000" data-alt="Figure 5"> 
Figure 5: Comparison of oscillatory shear index before (preoperative) and after (postoperative) carotid endarterectomy, including both anterior and posterior views. OSI is mapped to geometric models of the CCA, ECA, and ICA. Lesion and repaired lesion (segments where OSI are compared) are highlighted. Post-operatively maps depict areas of higher OSI compared to pre-operatively. Abbreviations: OSI = oscillatory shear index; CCA = common carotid artery; ECA = external carotid artery; ICA = internal carotid artery; CEA = carotid endarterectomy. <a href="https://www.jove.com/files/ftp_upload/68447/68447fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Cardiac MRI: Dark blood, T1/T2-weighted images, MRA, T1/T2 maps; analysis of heart structure." class="xfigimg" src="/files/ftp_upload/68447/68447fig6.jpg" data-altupdatedat="1762509930000" data-alt="Figure 6"> 
Figure 6: A representative qMatch dataset from a patient with intraplaque hemorrhage. (A) Dark blood, (B) T1-weighted, (C) T2-weighted, (D) MRA, (E) qMatch T1 Map, and (F) qMatch T2 Map sequences. The outline of the ICA is depicted with a solid white line, while the flow lumen is depicted with the dashed white line, and the plaque is depicted with the dashed yellow line. Features of IPH (solid red line) demonstrated by hyperintense signal in the T1-weighted image and lowered T1 measurement in the T1 map. Abbreviations: IPH = intraplaque hemorrhage. <a href="https://www.jove.com/files/ftp_upload/68447/68447fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="MRI differentials: dark blood, T1, T2, MRA, T1 and T2 maps highlighting heart structures." class="xfigimg" src="/files/ftp_upload/68447/68447fig7.jpg" data-altupdatedat="1762509930000" data-alt="Figure 7"> 
Figure 7: A representative qMatch dataset from a patient with calcified plaque. (A) Dark blood, (B) T1-weighted, (C) T2-weighted, (D) MRA, (E) qMatch T1 Map, and (F) qMatch T2 Map sequences. The outline of the ICA is depicted with solid white line, while the flow lumen is depicted with the dashed white line. Calcified portion of the plaque (dashed orange line) demonstrated by hypo-intense signal in the dark blood, T1-weighted, and T2-weighted images. <a href="https://www.jove.com/files/ftp_upload/68447/68447fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Plaque Component</td>
			<td>MRA</td>
			<td>Dark Blood</td>
			<td>T1w</td>
			<td>T2w</td>
			<td>T1-map</td>
			<td>T2-map</td>
		</tr>
		<tr>
			<td>IPH</td>
			<td>+</td>
			<td></td>
			<td>+</td>
			<td></td>
			<td>Used for Quantification</td>
			<td>Used for Quantification</td>
		</tr>
		<tr>
			<td>Calcium</td>
			<td></td>
			<td>-</td>
			<td>-</td>
			<td>-</td>
			<td>Used for Quantification</td>
			<td>Used for Quantification</td>
		</tr>
		<tr>
			<td>LRNC</td>
			<td>=</td>
			<td></td>
			<td></td>
			<td>-</td>
			<td>Used for Quantification</td>
			<td>Used for Quantification</td>
		</tr>
		<tr>
			<td>Fibrous Cap</td>
			<td>-/=</td>
			<td></td>
			<td>-/=</td>
			<td>-</td>
			<td>Used for Quantification</td>
			<td>Used for Quantification</td>
		</tr>
	</tbody>
</table>
Table 1: Characteristics of plaque components on qMatch datasets. Abbreviations: MRA = Magnetic resonance angiography; T1w = T1 weighted; T2w = T2 weighted; IPH = intraplaque hemorrhage; LRNC = lipid-rich necrotic core; + = hyper-intense; - = hypo-intense; (=) iso-intense.

Assessment of internal carotid artery (ICA) stenosis is based on the estimation of percentage stenosis, which does not account for physiologically relevant risk factors for stroke such as plaque composition and hemodynamics. This protocol leverages quantitative magnetic resonance imaging and computational fluid dynamics to characterize ICA plaque composition and hemodynamics.

Current assessment and management of internal carotid artery (ICA) stenosis is based on the estimation of percentage stenosis via duplex ultrasound (DUS) ...

Our research assesses physiologically relevant risk factors for plaque embolism and stroke in patients with internal carotid artery stenosis. Specifically, we look at how plaque morphology and its hemodynamic environment differ amongst patients with carotid artery stenosis. In the field of cerebrovascular research, recent efforts have aimed to identify predictors of stroke in asymptomatic carotid artery stenosis patients with a specific focus on patient-specific risk factors, imaging characteristics, and hemodynamic parameters, which can be linked to increased stroke risk.Computational fluid dynamics enables non-invasive patient-specific analysis of blood flow, and it's increasingly used alongside magnetic resonance imaging and photon counting CTA to assess carotid plaque structure and composition. Current magnetic resonance imaging is limited by long scan times, complex image interpretation, and repositioning errors. And on the other hand, computational fluid dynamics suffers from limited patient-specific data and poor model tuning, thus reducing its accuracy.Our group has demonstrated that patients with similar degrees of internal carotid artery stenosis narrowing exhibit distinct hemodynamic profiles and that in patients with bilateral stenosis, the degree of stenosis severity influences each side's hemodynamics and flow characteristics. And this really underscores the complex cerebrovascular and hemodynamic interactions in these patients. To begin, launch the CRIMSON software on a computer system.Import de-identified DICOM image data for patient-specific anatomy into CRIMSON by using the import button in the data manager. Using the geometry modeling window, select vessel path editing and create a vessel tree containing the common carotid artery, external carotid artery, and internal carotid artery. Begin the common carotid artery center line at the level of C5 where the PC MRI flow waveform was obtained.Place the internal carotid artery center line so it ends one to two centimeters distal to the stenosis, matching the PC MRI waveform location. Then place the external carotid artery center line endpoint proximal to the first order branches, matching the PC MRI waveform acquisition location. Now use the vessel re-slice window to visualize the center line and cross-sectional views perpendicular to the center line after adding at least two points along each vessel.Using the same window, add vessel contours to specify the boundaries of the vessel wall. The left side of the vessel re-slice window displays the original image while the right side displays the image gradient for defining contours. Place contours frequently enough to capture vessel curvature and changing geometry without overfitting.After defining all contours, use the loft button in the vessel contour modeling window to generate a combined 3-dimensional solid model via lofting. Then use the vessel blending window to select the fillet algorithm for blending the vessel model into a single solid geometry. Open the meshing and solver setup window and click the meshing button to view meshing options to configure mesh parameters.In the global options window, set the global element size to an absolute value between 0.5 millimeter and 0.75 millimeter. Then set boundary layer type to geometric growth, the total number of layers to three, the first layer thickness to 0.2 millimeter and total layer thickness of one millimeter. Then apply curvature refinement to add mesh elements at high curvature regions such as the stenosis.Right-click on the mesh and click on the mesh information button to review mesh metrics including element count, aspect ratios, and distribution. To specify boundary conditions, click on the meshing and solver setup window. Select the solver setup icon, then add a boundary condition set using the BC icon.View the boundary conditions currently available in CRIMSON. Click the BC icon again, select no slip to implement rigid, non-deformable walls, and apply this to all walls using the apply to all walls option. Then select prescribed velocity.Import the previously defined inflow waveform and map a parabolic velocity profile to the CCA inlet. Similarly, import the ECA pulsatile outflow waveform mapping a parabolic velocity profile to the ECA outlet. Now click the BC icon, choose RCR, and populate a three element Windkessel model, consisting of proximal resistance, distal resistance, and capacitor.Map the RCR to the ICA outlet based on patient-specific calculations. To prepare solver parameters, navigate to the meshing and solver window, click on the solver setup icon, then choose solver parameters. Set the step size to 0.1 millisecond for four cardiac cycles, requires residual to 10 to the power of minus four, and blood density to 1, 060 kilograms per cubic meter.Use solver setup to generate all simulation input files, including the flow data, inlet flow at each time step, the mesh and boundary conditions, face on which each boundary condition is applied, the first time step number of the simulation, three element Windkessel data, pressure and velocity at every point in the mesh, and the instructions for the flow solver. Add the Carreau-Yasuda model into the solver. inp and add to the simulation files to allow for blood to be modeled as a non-Newtonian fluid.Now run simulation in the steady pane of the solver setup window to run the CRIMSON Navier-Stokes flow solver. Specify the number of processors in the command window. When the solver starts running, the output file histor.dat will be printed in the command line and saved in a new directory, n-procs-case. Use the Linux prompt tail f histor. dat to observe the file in real time.The first column of the file corresponds to time step. The second column is the elapsed time. The third column is the non-linear residual, and the fourth column is the log residual value.A high quality mesh with low aspect ratio elements was generated to accurately represent the carotid artery bifurcation geometry. A representative velocity profile across the carotid bifurcation and ICA stenosis was simulated, showing a maximum flow velocity of approximately 275 centimeters per second at peak systole across the stenosis. Pressure mapping showed negligible pressure gradient across the stenosis in one case with proximal and distal pressures nearly overlapping throughout the cardiac cycle.In a contrasting case, pressure proximal to the stenosis was significantly higher than distal pressure, revealing a prominent pressure drop. Wall shear stress was low across the bifurcation in the non-stenotic model, especially in the outer walls of the internal and external carotid arteries. In the stenotic model, high wall shear stress was concentrated at the internal carotid artery stenosis.Oscillatory shear index mapping showed lesion-associated OSI values before surgery were lower compared to postoperative values. HU match imaging identified intraplaque hemorrhage through hyperintense signal on T1 weighted imaging and lowered values on the T1 map. Calcified plaque was identified by consistently hypointense signal on dark blood, T1 weighted, and T2 weighted sequences.

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Learn a computational MRI protocol for analyzing plaque morphology and hemodynamics in carotid artery stenosis patients. This method uses CRIMSON software and Navier-Stokes simulations to assess stroke risk factors.

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