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Optimization of 4D Flow MRI Spatial and Temporal Resolution for Examining Complex Hemodynamics in the Carotid Artery Bifurcation

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Abstract

Background

Three-dimensional, ECG-gated, time-resolved, three-directional, velocity-encoded phase-contrast MRI (4D flow MRI) has been applied extensively to measure blood velocity in great vessels but has been much less used in diseased carotid arteries. Carotid artery webs (CaW) are non-inflammatory intraluminal shelf-like projections into the internal carotid artery (ICA) bulb that are associated with complex flow and cryptogenic stroke.

Purpose

Optimize 4D flow MRI for measuring the velocity field of complex flow in the carotid artery bifurcation model that contains a CaW.

Methods

A 3D printed phantom model created from computed tomography angiography (CTA) of a subject with CaW was placed in a pulsatile flow loop within the MRI scanner. 4D Flow MRI images of the phantom were acquired with five different spatial resolutions (0.50–2.00  mm3) and four different temporal resolutions (23–96 ms) and compared to a computational fluid dynamics (CFD) solution of the flow field as a reference. We examined four planes perpendicular to the vessel centerline, one in the common carotid artery (CCA) and three in the internal carotid artery (ICA) where complex flow was expected. At these four planes pixel-by-pixel velocity values, flow, and time average wall shear stress (TAWSS) were compared between 4D flow MRI and CFD.

Hypothesis

An optimized 4D flow MRI protocol will provide a good correlation with CFD velocity and TAWSS values in areas of complex flow within a clinically feasible scan time (~ 10 min).

Results

Spatial resolution affected the velocity values, time average flow, and TAWSS measurements. Qualitatively, a spatial resolution of 0.50  mm3 resulted in higher noise, while a lower spatial resolution of 1.50–2.00  mm3 did not adequately resolve the velocity profile. Isotropic spatial resolutions of 0.50–1.00  mm3 showed no significant difference in total flow compared to CFD. Pixel-by-pixel velocity correlation coefficients between 4D flow MRI and CFD were > 0.75 for 0.50–1.00  mm3 but were < 0.5 for 1.50 and 2.00  mm3. Regional TAWSS values determined from 4D flow MRI were generally lower than CFD and decreased at lower spatial resolutions (larger pixel sizes). TAWSS differences between 4D flow and CFD were not statistically significant at spatial resolutions of 0.50–1.00  mm3 but were different at 1.50 and 2.00 mm3. Differences in temporal resolution only affected the flow values when temporal resolution was > 48.4 ms; temporal resolution did not affect TAWSS values.

Conclusion

A spatial resolution of 0.74–1.00  mm3 and a temporal resolution of 23–48 ms (1–2 k-space segments) provides a 4D flow MRI protocol capable of imaging velocity and TAWSS in regions of complex flow within the carotid bifurcation at a clinically acceptable scan time.

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References

  1. Nayak, K. S., J.-F. Nielsen, M. A. Bernstein, et al. Cardiovascular magnetic resonance phase contrast imaging. J. Cardiovasc. Magn. Reson. 17(1):71, 2015. https://doi.org/10.1186/s12968-015-0172-7.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Markl, M., A. Frydrychowicz, S. Kozerke, M. Hope, and O. Wieben. 4D flow MRI. J. Magn. Reson. Imaging. 36(5):1015–1036, 2012. https://doi.org/10.1002/jmri.23632.

    Article  PubMed  Google Scholar 

  3. Szajer, J., and K. Ho-Shon. A comparison of 4D flow MRI-derived wall shear stress with computational fluid dynamics methods for intracranial aneurysms and carotid bifurcations—a review. J. Magn. Reson. Imaging. 48:62–69, 2018. https://doi.org/10.1016/j.mri.2017.12.005.

    Article  Google Scholar 

  4. Chatzimavroudis, G. P., J. N. Oshinski, R. H. Franch, et al. Evaluation of the precision of magnetic resonance phase velocity mapping for blood flow measurements. J. Cardiovasc. Magn. Reson. 3(1):11–19, 2001. https://doi.org/10.1081/JCMR-100000142.

    Article  CAS  PubMed  Google Scholar 

  5. Nilsson, A., K. M. Bloch, J. Töger, E. Heiberg, and F. Ståhlberg. Accuracy of four-dimensional phase-contrast velocity mapping for blood flow visualizations: a phantom study. Acta Radiol. 54(6):663–671, 2013. https://doi.org/10.1177/0284185113478005.

    Article  PubMed  Google Scholar 

  6. Montalba, C., J. Urbina, J. Sotelo, et al. Variability of 4D flow parameters when subjected to changes in MRI acquisition parameters using a realistic thoracic aortic phantom. Magn. Reson. Med. 79(4):1882–1892, 2018. https://doi.org/10.1002/mrm.26834.

    Article  PubMed  Google Scholar 

  7. Zimmermann, J., D. Demedts, H. Mirzaee, et al. Wall shear stress estimation in the aorta: Impact of wall motion, spatiotemporal resolution, and phase noise. J. Magn. Reson. Imaging. 2018. https://doi.org/10.1002/jmri.26007.

    Article  PubMed  Google Scholar 

  8. Shen, X., S. Schnell, A. J. Barker, et al. Voxel-by-voxel 4D flow MRI-based assessment of regional reverse flow in the aorta. J. Magn. Reson. Imaging. 47(5):1276–1286, 2018. https://doi.org/10.1002/jmri.25862.

    Article  PubMed  Google Scholar 

  9. Callaghan, F. M., R. Kozor, A. G. Sherrah, et al. Use of multi-velocity encoding 4D flow MRI to improve quantification of flow patterns in the aorta. J. Magn. Reson. Imaging. 43(2):352–363, 2016. https://doi.org/10.1002/jmri.24991.

    Article  PubMed  Google Scholar 

  10. Garcia, J., A. J. Barker, and M. Markl. The role of imaging of flow patterns by 4D flow MRI in aortic stenosis. JACC Cardiovasc. Imaging. 12(2):252–266, 2019. https://doi.org/10.1016/j.jcmg.2018.10.034.

    Article  PubMed  Google Scholar 

  11. Puiseux, T., A. Sewonu, O. Meyrignac, et al. Reconciling PC-MRI and CFD: an in-vitro study. NMR Biomed. 32(5):e4063, 2019. https://doi.org/10.1002/nbm.4063.

    Article  PubMed  Google Scholar 

  12. Kweon, J., D. H. Yang, G. B. Kim, et al. Four-dimensional flow MRI for evaluation of post-stenotic turbulent flow in a phantom: comparison with flowmeter and computational fluid dynamics. Eur. Radiol. 26(10):3588–3597, 2016. https://doi.org/10.1007/s00330-015-4181-6.

    Article  PubMed  Google Scholar 

  13. Ngo, M. T., C. I. Kim, J. Jung, et al. Four-dimensional flow magnetic resonance imaging for assessment of velocity magnitudes and flow patterns in the human carotid artery bifurcation: comparison with computational fluid dynamics. Diagnostics. 9(4):223, 2019. https://doi.org/10.3390/diagnostics9040223.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Ngo, M. T., U. Y. Lee, H. Ha, et al. Improving blood flow visualization of recirculation regions at carotid bulb in 4D flow MRI using semi-automatic segmentation with ITK-SNAP. Diagnostics. 11(10):1890, 2021. https://doi.org/10.3390/diagnostics11101890.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Roldán-Alzate, A., S. García-Rodríguez, P. V. Anagnostopoulos, et al. Hemodynamic study of TCPC using in vivo and in vitro 4D flow MRI and numerical simulation. J. Biomech. 48(7):1325–1330, 2015. https://doi.org/10.1016/j.jbiomech.2015.03.009.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Edelstein, W. A., M. Mahesh, and J. A. Carrino. MRI: time is dose–and money and versatility. J. Am. Coll. Radiol. 7(8):650–652, 2010. https://doi.org/10.1016/j.jacr.2010.05.002.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Sajed, P. I., J. N. Gonzalez, C. A. Cronin, et al. Carotid bulb webs as a cause of “cryptogenic” ischemic stroke. AJNR Am. J. Neuroradiol. 38(7):1399–1404, 2017. https://doi.org/10.3174/ajnr.A5208.

    Article  Google Scholar 

  18. Haussen, D. C., J. A. Grossberg, S. Koch, et al. Multicenter experience with stenting for symptomatic carotid web. Intervent. Neurol. 2018. https://doi.org/10.1159/000489710.

    Article  Google Scholar 

  19. Haussen, D. C., J. A. Grossberg, M. Bouslama, et al. Carotid web (intimal fibromuscular dysplasia) has high stroke recurrence risk and is amenable to stenting. Stroke. 48(11):3134–3137, 2017. https://doi.org/10.1161/strokeaha.117.019020.

    Article  PubMed  Google Scholar 

  20. Park, C. C., R. El Sayed, B. B. Risk, et al. Carotid webs produce greater hemodynamic disturbances than atherosclerotic disease: a DSA time–density curve study. J. Neurointerv. Surg. 2021. https://doi.org/10.1136/neurintsurg-2021-017588.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Ozaki, D., T. Endo, H. Suzuki, et al. Carotid web leads to new thrombus formation: computational fluid dynamic analysis coupled with histological evidence. Acta Neurochir. (Wien). 162(10):2583–2588, 2020. https://doi.org/10.1007/s00701-020-04272-2.

    Article  PubMed  Google Scholar 

  22. Antonowicz, A., K. Wojtas, Ł Makowski, W. Orciuch, and M. Kozłowski. Particle image velocimetry of 3D-printed anatomical blood vascular models affected by atherosclerosis. Materials. 16(3):1055, 2023.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ford, M. D., H. N. Nikolov, J. S. Milner, et al. PIV-measured versus CFD-predicted flow dynamics in anatomically realistic cerebral aneurysm models. J. Biomech. Eng. 2008. https://doi.org/10.1115/1.2900724.

    Article  PubMed  Google Scholar 

  24. Raschi, M., F. Mut, G. Byrne, et al. CFD and PIV analysis of hemodynamics in a growing intracranial aneurysm. Int. J. Numer. Methods Biomed. Eng. 28(2):214–228, 2012. https://doi.org/10.1002/cnm.1459.

    Article  Google Scholar 

  25. Mitsouras, D., T. C. Lee, P. Liacouras, et al. Three-dimensional printing of MRI-visible phantoms and MR image-guided therapy simulation. Magn. Reson. Med. 77(2):613–622, 2017. https://doi.org/10.1002/mrm.26136.

    Article  CAS  PubMed  Google Scholar 

  26. Object30Pro, Objet30 Pro Key Features: Create parts with the precision, look and feel of real production parts. https://www.javelin-tech.com/3d/stratasys-3d-printer/objet30-pro/.

  27. Summers, P. E., D. W. Holdsworth, H. N. Nikolov, B. K. Rutt, and M. Drangova. Multisite trial of MR flow measurement: phantom and protocol design. J. Magn. Reson. Imaging. 21(5):620–631, 2005. https://doi.org/10.1002/jmri.20311.

    Article  PubMed  Google Scholar 

  28. Wilson, J. S., M. Islam, and J. N. Oshinski. In vitro validation of regional circumferential strain assessment in a phantom aortic model using cine displacement encoding with stimulated echoes MRI. J. Magn. Reson. Imaging. 55(6):1773–1784, 2022. https://doi.org/10.1002/jmri.27972.

    Article  PubMed  Google Scholar 

  29. Wu, S. P., S. Ringgaard, and E. M. Pedersen. Three-dimensional phase contrast velocity mapping acquisition improves wall shear stress estimation in vivo. J. Magn. Reson. Imaging. 22(3):345–351, 2004. https://doi.org/10.1016/j.mri.2004.01.002.

    Article  Google Scholar 

  30. Markl, M., A. Harloff, T. A. Bley, et al. Time-resolved 3D MR velocity mapping at 3T: improved navigator-gated assessment of vascular anatomy and blood flow. J. Magn. Reson. Imaging. 25(4):824–831, 2007. https://doi.org/10.1002/jmri.20871.

    Article  PubMed  Google Scholar 

  31. Stalder, A. F., M. F. Russe, A. Frydrychowicz, et al. Quantitative 2D and 3D phase contrast MRI: optimized analysis of blood flow and vessel wall parameters. Magn. Reson. Med. 60(5):1218–1231, 2008. https://doi.org/10.1002/mrm.21778.

    Article  CAS  PubMed  Google Scholar 

  32. Ku, D. N., and D. P. Giddens. Laser Doppler anemometer measurements of pulsatile flow in a model carotid bifurcation. J. Biomech. 20:407–421, 1987. https://doi.org/10.1016/0021-9290(87)90048-0.

    Article  CAS  PubMed  Google Scholar 

  33. Ku, D. N., D. P. Giddens, D. J. Phillips, and D. E. Strandness. Hemodynamics of the normal human carotid bifurcation: in vitro and in vivo studies. Ultrasound. Med. Biol. 11(1):13–26, 1985. https://doi.org/10.1016/0301-5629(85)90003-1.

    Article  CAS  PubMed  Google Scholar 

  34. Ku, D. N., D. P. Giddens, C. K. Zarins, and S. Glagov. Pulsatile flow and atherosclerosis in the human carotid bifurcation. Positive correlation between plaque location and low oscillating shear stress. Arteriosclerosis. 5(3):293–302, 1985. https://doi.org/10.1161/01.ATV.5.3.293.

    Article  CAS  PubMed  Google Scholar 

  35. Markl, M., F. Wegent, T. Zech, et al. In vivo wall shear stress distribution in the carotid artery. Circ. Cardiovasc. Imaging. 3(6):647–655, 2010. https://doi.org/10.1161/CIRCIMAGING.110.958504.

    Article  PubMed  Google Scholar 

  36. Frydrychowicz, A., A. Berger, M. F. Russe, et al. Time-resolved magnetic resonance angiography and flow-sensitive 4-dimensional magnetic resonance imaging at 3 Tesla for blood flow and wall shear stress analysis. J. Thoracic. Cardiovasc. Surg. 136(2):400–407, 2008. https://doi.org/10.1016/j.jtcvs.2008.02.062.

    Article  Google Scholar 

  37. Frydrychowicz, A., A. F. Stalder, M. F. Russe, et al. Three-dimensional analysis of segmental wall shear stress in the aorta by flow-sensitive four-dimensional-MRI. J. Magn. Reson. Imaging. 30(1):77–84, 2009. https://doi.org/10.1002/jmri.21790.

    Article  PubMed  Google Scholar 

  38. Harloff, A., T. Zech, F. Wegent, et al. Comparison of blood flow velocity quantification by 4D flow MR imaging with ultrasound at the carotid bifurcation. AJNR Am. J. Neuroradiol. 34(7):1407–1413, 2013. https://doi.org/10.3174/ajnr.A3419.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Medero, R., C. Hoffman, and A. Roldán-Alzate. Comparison of 4D flow MRI and particle image velocimetry using an in vitro carotid bifurcation model. Ann. Biomed. Eng. 46(12):2112–2122, 2018. https://doi.org/10.1007/s10439-018-02109-9.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Cibis, M., W. V. Potters, F. J. Gijsen, et al. The effect of spatial and temporal resolution of cine phase contrast MRI on wall shear stress and oscillatory shear index assessment. PLoS One. 11(9):e0163316–e0163316, 2016. https://doi.org/10.1371/journal.pone.0163316.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Oktar, S. O., C. Yücel, D. Karaosmanoglu, et al. Blood-flow volume quantification in internal carotid and vertebral arteries: comparison of 3 different ultrasound techniques with phase-contrast MR imaging. AJNR Am. J. Neuroradiol. 27(2):363–369, 2006.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Potters, W. V., H. A. Marquering, E. VanBavel, and A. J. Nederveen. Measuring wall shear stress using velocity-encoded MRI. Curr. Cardiovasc. Imaging Rep. 7(4):9257, 2014. https://doi.org/10.1007/s12410-014-9257-1.

    Article  Google Scholar 

  43. Petersson, S., P. Dyverfeldt, and T. Ebbers. Assessment of the accuracy of MRI wall shear stress estimation using numerical simulations. J. Magn. Reson. Imaging. 36(1):128–138, 2012. https://doi.org/10.1002/jmri.23610.

    Article  PubMed  Google Scholar 

  44. Potters, W.V., P. van Ooij, H. Marquering, E. vanBavel, and A.J. Nederveen, Volumetric arterial wall shear stress calculation based on cine phase contrast MRI. J. Magn. Reson. Imaging 2015 41(2):505–516. https://doi.org/10.1002/jmri.24560.

  45. Bae, T., J. H. Ko, and J. Chung. Turbulence intensity as an indicator for ischemic stroke in the carotid web. World Neurosurg. 2021. https://doi.org/10.1016/j.wneu.2021.07.049.

    Article  PubMed  Google Scholar 

  46. Choi, P. M. C., D. Singh, A. Trivedi, et al. Carotid webs and recurrent ischemic strokes in the era of CT angiography. AJNR Am. J. Neuroradiol. 36(11):2134–2139, 2015. https://doi.org/10.3174/ajnr.A4431.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Compagne, K. C. J., K. Dilba, E. J. Postema, et al. Flow patterns in carotid webs: a patient-based computational fluid dynamics study. AJNR Am. J. Neuroradiol. 40(4):703–708, 2019. https://doi.org/10.3174/ajnr.A6012.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Kumar, D. R., E. Hanlin, I. Glurich, J. J. Mazza, and S. H. Yale. Virchow’s contribution to the understanding of thrombosis and cellular biology. Clin. Med. Res. 8(3–4):168–172, 2010. https://doi.org/10.3121/cmr.2009.866.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Lee, B. K. Computational fluid dynamics in cardiovascular disease. Korean Circ. J. 41(8):423–430, 2011. https://doi.org/10.4070/kcj.2011.41.8.423.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Iffrig, E., L. H. Timmins, R. El Sayed, W. R. Taylor, and J. N. Oshinski. A new method for quantifying abdominal aortic wall shear stress using phase contrast magnetic resonance imaging and the Womersley solution. J. Biomech. Eng. 2022. https://doi.org/10.1115/1.4054236.

    Article  PubMed  Google Scholar 

  51. Katritsis, D., L. Kaiktsis, A. Chaniotis, et al. Wall shear stress: theoretical considerations and methods of measurement. Prog. Cardiovasc. Dis. 49(5):307–329, 2007. https://doi.org/10.1016/j.pcad.2006.11.001.

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors would like to acknowledge Paul Lee, Ph.D. at Emory University for the help in operating Object 30 3D printer.

Funding

This work was funded by the American Heart Association Grant Nos. 0000065426 (Sharifi) and 19IPLOI34760670 (Allen), National Institutes of Health Grant Nos. R21NS114603 (Allen and Oshinski) and R01EB027774 (Oshinski). Additionally, this material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. 1937971 (El Sayed). Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Authors and Affiliations

  1. Department of Biomedical Engineering, The Wallace H. Coulter, Emory University and Georgia Institute of Technology, Atlanta, GA, USA

    Retta El Sayed, Jason W. Allen & John N. Oshinski

  2. Department of Radiology & Imaging Sciences, Emory University, 1364 Clifton Rd, Atlanta, GA, 30322, USA

    Alireza Sharifi, Charlie C. Park, Jason W. Allen & John N. Oshinski

  3. Department of Neurology, Emory University, Atlanta, GA, USA

    Diogo C. Haussen, Jason W. Allen & John N. Oshinski

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  1. Retta El Sayed
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Correspondence to John N. Oshinski.

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El Sayed, R., Sharifi, A., Park, C.C. et al. Optimization of 4D Flow MRI Spatial and Temporal Resolution for Examining Complex Hemodynamics in the Carotid Artery Bifurcation. Cardiovasc Eng Tech 14, 476–488 (2023). https://doi.org/10.1007/s13239-023-00667-1

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