A Spatiotemporal exploration and 3D modeling of blood flow in healthy carotid artery bifurcation from two modalities: Ultrasound-Doppler and phase contrast MRI.
Summary (3 min read)
- Vascular maladies may be caused by thrombi and can lead to stroke .
- Blood flow analysis can confirm the presence of a local vessel anomaly, its impact on the blood flow pattern and its possible evolution [7, 26, 25, 10].
- Few studies compared velocity measurements in the carotid artery between US-Doppler and PC-MRI [15, 16, 38, 28].
- Section 2 describes the imaging data sets and the analysis methodology.
2. Material and Methods
- The arterial geometrical model was extracted from MR anatomical images and the hemodynamic modeling was performed from the obtained models.
- The velocity waveforms from both PC-MRI and US-Doppler were extracted at three locations: the right CCA, ECA and ICA.
2.1.1. PC-MR imaging
- The PC-MR images were acquired with a clinical 1.5 T Philips system (Ingenia, Philips medical systems, Best, the Netherlands) using 20 channels phased array head neck spine coil.
- Fifty 2D anatomical images were acquired in axial orientation to cover cervical region Arij Debbich et al.: Preprint submitted to Elsevier Page 2 of 16 Figure 3: Growing carotid scanning windows size.
- Key points are located in: CCA at 1.5 cm from the bifurcation, ECA and ICA: at 1 cm from the bifurcation.
- The ideal velocity encoding (VENC) should be high enough to avoid aliasing and as low as possible to reduce velocity noise .
- The total scan time for an exam was approximately 7 minutes.
2.1.2. US-Doppler imaging
- All US-Doppler exams were conducted by a radiologist (B.H) with 10 years’ experience in cardiovascular imaging.
- Clinical General Electric ultrasound systems (LOGIQ E9, GE Healthcare, Milwaukee, WI, USA) with 9 MHz linear probe were used.
- US-Doppler images were matrices of dimension 720x960.
- To extract the velocity waveform, the authors chose a profile of one cardiac cycle , fixed the two profile axes and selected 14 feature points on these profiles including PSV and EDV.
- The USDoppler profile point digitization was performed using the Engauge Digitizer software .
2.2. PC-MRI Velocity profile extraction
- Three velocity waveforms were extracted from PC-MRI at locations CCA, ECA, and ICA during a cardiac cycle which was divided into 14 time points.
- The following values were deduced from the interpolated waveforms: - Velocity variation at a given pixel VMRIpixel .
- - Maximum velocity at a given pixel within its eight neighbors VMRImax.-.
- As the MRI and the US-Doppler examinations were not acquired at the same time, there might be some physiological variations .
2.3. Arterial model reconstruction and computational mesh generation
- The geometrical characteristics of all the vessel segments for the ten subjects are given in Table1.
- The carotid lumen was separated from the rest of the structures by applying an intensity threshold.
- The model was further re-meshed using an Octree surface refinement based on prismatic wall .
- Some studies adopted the rheological Newtonian behavior for the carotid artery (stationary viscosity) because it is simpler than the hemodynamic modeling with a non Newtonian behavior and arguing it has aminor impact on the results .
2.4. Boundary conditions
- One of their objectives was to investigate the impact of the inlet boundary conditions on the simulation results.
- These inlets were pulsed over time and were composed of the two usual physiological phases during a cardiac cycle: systole and diastole.
- Velocity profile for every subject was matched to VMRImax inthe CCA, 3.3 cm from the carotid bifurcation to conform to the CCA dimension in the geometrical model.
- (5) where ℎt is the maximal parabolic magnitude matching the VMRImax or VUS at time t. (xc , yc) and R are respectively thecenter coordinates and the radius of the CCA inlet section.
- All meshes brought velocity profiles close to each other.
2.5. Simulated velocity profiles
- Simulated velocity profiles Vsim were extracted at threelocations of the carotid bifurcation: 1.5 cm from the bifurcation in the CCA and 1 cm up the bifurcation in ECA and ICA.
- The values were extracted using CFD-POST from ANSYS software.
- Velocity waveforms were extracted from PC-MRI (VMRImax, VMRIpixel and VMRImean) and Doppler-US (VUS)imaging data for ten volunteers at the three localizations of the carotid artery.
- The first one, called VSIM_MRI, is based on PC-MRI data VMRImaxprofile used as inlet boundary condition.
- The authors did not integrate the pixel-based PC-MRI velocity VMRIpixel in the tables since its values werebetween those of VMRImax and VMRImean and were noisier.
3.1. Analysis of PC-MRI velocities
- Typical measured velocity waveforms from PC-MRI are presented in Figure 4. ECA and ICA relative to V MRImax for PC-MRI (up) and V US for US-Doppler (down).
- The authors noted that a velocity profile over the CCA diameter at a given location was almost parabolic for all time points . and end diastolic (ED) time points every centimeter from the CCA to the carotid sinus, ICA and ECA .
- There were significant variations de- tected in the carotid sinus site: a progressive decrease of the maximum velocity and a disruption of the profile shape.
3.2. Analysis of PC-MRI and US velocity measurements
- Unlike the difference observed by Harloff et al. for ICA, PSV of PC-MRI was on average less than US-Doppler by about 17.6%.
- The same tendency was observed in CCA PSV (mean difference of 21.8 cm/s), to a much lower extent for CCA EDV .
- Diastolic velocity profiles are less dissimilar except in ICA for the two modalities.
- The mean velocity waveforms of the ten volunteers relative to PC-MRI and US-Doppler in CCA, ECA and ICA are plotted in Figure 10.
3.3. Analysis of simulated velocity waveforms
- Overall, the numerical velocity waveforms were closer to the PC-MRI ones independent of the arterial input function considered (US or MRI).
- The numerical velocities were compared to in vivo measurements through the global and the local indicators defined in section 3 .
- The mean difference between measurements and simulation for PSV and EDV was greater.
- From Tables 2, 3 and 4, it can be noted that the mean PSV of the ten subjects from VSIM_MRImax is lower than that from VSIM_US.
- The PC-MRI velocity behavior in the carotid bifurcation was investigated in time and space.
- It was observed that the velocity estimation increased with the window size up to maximum .
- This can be difficult to achieve when dealing with a PC-MRI and US-Doppler comparative study like ours for two reasons related to US measurements: i) lack of convenient accessibility for radiologists.
- The arterial wall pulsatility has not been considered as it would have brought many additional issues with less mastered modeling methods (Fluid Structure Interaction).
- Locally, the mean difference between VSIM_MRImax and VMRImax varied from 22% to 32% according to the local dif-ference metric at peak systole PSV and from 29% to 37%according to the local difference metric at end diastole EDV.
- The authors investigated blood velocity quantification over the carotid artery bifurcation from PC-MRI and US-Doppler, and 3D velocity modeling from these modalities.
- PC-MRI data analysis showed velocity across the section of the artery follows a parabolic profile except in the sinus region.
- Overall, the authors found numerical velocities based on PC-MRI velocity inlet closer to measurements than those based on Doppler-US velocity inlet.
- Therefore, from their experiments, the PC-MRI-based hemodynamic modeling approach could be reasonably more realistic.
Did you find this useful? Give us your feedback
"A Spatiotemporal exploration and 3D..." refers background in this paper
...Blood flow analysis can confirm the presence of a local vessel anomaly, its impact on the blood flow pattern and its possible evolution [7, 26, 25, 10]....
"A Spatiotemporal exploration and 3D..." refers result in this paper
...This observation agrees with several CFD simulations in previous works [37, 40, 11, 12]....