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Evaluation of magnetic resonance angiography as a possible alternative to rotational angiography or computed tomography angiography for assessing cerebrovascular computational fluid dynamics

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Abstract

The aim of this study was to conduct a flow experiment using a cerebrovascular phantom and investigate whether magnetic resonance angiography (MRA) could replace three-dimensional rotational angiography (RA) and computed tomography angiography (CTA) to construct vascular models for computational fluid dynamics (CFD). We performed MRA and 3D cine phase-contrast (PC) MR imaging with a silicone cerebrovascular phantom of an internal carotid artery-posterior communicating artery aneurysm with blood-mimicking fluid, and controlled flow with a flowmeter. We also obtained RA and CTA data for the phantom. Four analysts constructed vascular models based on the three different modalities. These 12 constructed models used flow information based on 3D cine PC MR imaging for CFD. We compared RA-, CTA-, MRA-based CFD results using the micro-CT-based CFD result as the criterion standard to investigate whether MRA-based CFD was not inferior to RA- or CTA-based CFD. We also analyzed the inter-analyst variability. Wall shear stress (WSS) distributions and streamlines of RA- or MRA-based CFD and those of micro-CT-based CFD were similar, but the vascular models and WSS values were different. Accuracy in measurements of blood vessel diameter, cross-sectional maximum velocity, and spatially averaged WSS was the highest for RA-based CFD, followed by MRA-based and CTA-based CFD using micro-CT-based CFD result as the reference. Except maximum velocity from CTA, all other parameters had good inter-analyst agreement using different modalities. The results demonstrated that non-invasive MRA can be used for cerebrovascular CFD models with good inter-analyst agreements.

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References

  1. Shojima M, Oshima M, Takagi K, Torii R, Hayakawa M, Katada K, Morita A, Kirino T (2004) Magnitude and role of wall shear stress on cerebral aneurysm: computational fluid dynamic study of 20 middle cerebral artery aneurysms. Stroke 35:2500–2505

    Article  Google Scholar 

  2. Meng H, Wang Z, Hoi Y, Gao L, Metaxa E, Swartz DD, Kolega J (2007) Complex hemodynamics at the apex of an arterial bifurcation induces vascular remodeling resembling cerebral aneurysm initiation. Stroke 38:1924–1931

    Article  Google Scholar 

  3. Shimogonya Y, Ishikawa T, Imai Y, Matsuki N, Yamaguchi T (2009) Can temporal fluctuation in spatial wall shear stress gradient initiate a cerebral aneurysm? A proposed novel hemodynamic index, the gradient oscillatory number (GON). J Biomech 42:550–554. https://doi.org/10.1016/j.jbiomech.2008.10.006

    Article  PubMed  Google Scholar 

  4. Chen H, Selimovic A, Thompson H, Chiarini A, Penrose J, Ventikos Y, Watton PN (2013) Investigating the influence of haemodynamic stimuli on intracranial aneurysm inception. Ann Biomed Eng 41:1492–1504. https://doi.org/10.1007/s10439-013-0794-6

    Article  PubMed  Google Scholar 

  5. Meng H, Tutino VM, Xiang J, Siddiqui A (2014) High WSS or low WSS? Complex interactions of hemodynamics with intracranial aneurysm initiation, growth, and rupture: toward a unifying hypothesis. AJNR Am J Neuroradiol 35:1254–1262. https://doi.org/10.3174/ajnr.A3558

    Article  CAS  PubMed  Google Scholar 

  6. Geers AJ, Larrabide I, Radaelli AG, Bogunovic H, Kim M, Gratama van Andel HA, Majoie CB, VanBavel E, Frangi AF (2011) Patient-specific computational hemodynamics of intracranial aneurysms from 3D rotational angiography and CT angiography: an in vivo reproducibility study. AJNR Am J Neuroradiol 32:581–586. https://doi.org/10.3174/ajnr.A2306

    Article  CAS  PubMed  Google Scholar 

  7. Marzo A, Singh P, Larrabide I, Radaelli A, Coley S, Gwilliam M, Wilkinson ID, Lawford P, Reymond P, Patel U, Frangi A, Hose DR (2011) Computational hemodynamics in cerebral aneurysms: the effects of modeled versus measured boundary conditions. Ann Biomed Eng 39:884–896. https://doi.org/10.1007/s10439-010-0187-z

    Article  PubMed  Google Scholar 

  8. Goubergrits L, Schaller J, Kertzscher U, Petz Ch, Hege HC, Spuler A (2013) Reproducibility of image-based analysis of cerebral aneurysm geometry and hemodynamics: an in-vitro study of magnetic resonance imaging, computed tomography, and three-dimensional rotational angiography. J Neurol Surg A Cent Eur Neurosurg 74:294–302. https://doi.org/10.1055/s-0033-1342937

    Article  CAS  PubMed  Google Scholar 

  9. Ren Y, Chen GZ, Liu Z, Cai Y, Lu GM, Li ZY (2016) Reproducibility of image-based computational models of intracranial aneurysm: a comparison between 3D rotational angiography, CT angiography and MR angiography. Biomed Eng Online 6(15):50. https://doi.org/10.1186/s12938-016-0163-4

    Article  Google Scholar 

  10. Markl M, Chan FP, Alley MT, Wedding KL, Draney MT, Elkins CJ, Parker DW, Wicker R, Taylor CA, Herfkens RJ, Pelc NJ (2003) Time-resolved three-dimensional phase-contrast MRI. J Magn Reson Imaging 17:499–506

    Article  Google Scholar 

  11. Isoda H, Takehara Y, Kosugi T, Terada M, Naito T, Onishi Y, Tanoi C, Amaya K, Sakahara H (2015) MR-based computational fluid dynamics with patient-specific boundary conditions for the initiation of a sidewall aneurysm of a basilar artery. Magn Reson Med Sci 14:139–144

    Article  Google Scholar 

  12. Watanabe T, Isoda H, Takehara Y, Terada M, Naito T, Kosugi T, Onishi Y, Tanoi C, Izumi T (2018) Hemodynamic vascular biomarkers for initiation of paraclinoid internal carotid artery aneurysms using patient-specific computational fluid dynamic simulation based on magnetic resonance imaging. Neuroradiology 60:545–555

    Article  Google Scholar 

  13. Isoda H, Hirano M, Takeda H, Kosugi T, Alley MT, Markl M, Pelc NJ, Sakahara H (2006) Visualization of hemodynamics in a silicon aneurysm model using time-resolved, 3D, phase-contrast MRI. AJNR Am J Neuroradiol 27:1119–1122

    CAS  PubMed  Google Scholar 

  14. Malek AM, Alper SL, Izumo S (1999) Hemodynamic shear stress and its role in atherosclerosis. JAMA 282:2035–2042

    Article  CAS  Google Scholar 

  15. Li L, Zeng L, Lin ZJ, Cazzell M, Liu H (2015) Tutorial on use of intraclass correlation coefficients for assessing intertest reliability and its application in functional near-infrared spectroscopy-based brain imaging. J Biomed Opt 20:50801

    Article  Google Scholar 

  16. Valen-Sendstad K, Bergersen AW, Shimogonya Y, Goubergrits L, Bruening J, Pallares J, Cito S et al (2018) Real-world variability in the prediction of intracranial aneurysm wall shear stress: the 2015 international aneurysm CFD challenge. Cardiovasc Eng Techno 9:544–564. https://doi.org/10.1007/s13239-018-00374-2

    Article  Google Scholar 

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Acknowledgements

This work was supported by JSPS KAKENHI (Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research) (Grant Number 25293264).

Funding

This study was funded by JSPS KAKENHI (Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research) (Grant Number 25293264).

Author information

Authors and Affiliations

  1. Department of Radiological and Medical Laboratory Sciences, Nagoya University Graduate School of Medicine, 1-20, Daikominami 1-chome, Higashi-ku, Nagoya, Aichi, 461-8673, Japan

    Yuya Yoneyama, Haruo Isoda, Kenta Ishiguro, Roshani Perera, Takashi Mizuno, Atsushi Fukuyama, Kazuya Takiguchi & Tomoya Watanabe

  2. Brain & Mind Research Center, Nagoya University, 1-20, Daikominami 1-chome, Higashi-ku, Nagoya, Aichi, 461-8673, Japan

    Haruo Isoda

  3. Biomedical Imaging Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, 1-20, Daikominami 1-chome, Higashi-ku, Nagoya, Aichi, 461-8673, Japan

    Haruo Isoda

  4. Department of Diagnostic Radiological Technology, Iwata City Hospital, 512-3 Okubo, Iwata, Shizuoka, 438-8550, Japan

    Masaki Terada, Masaki Kamiya & Kenichi Otsubo

  5. Department of Radiology, Hamamatsu University Hospital, 1-20-1 Handayama, Higashi-ku, Hamamatsu, Shizuoka, 431-3192, Japan

    Masaki Kamiya

  6. Department of Radiological Technology, Nagoya University Hospital, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan

    Takashi Mizuno

  7. Renaissance of Technology Corporation, 1-2 Nakazawacho Nakaku, Hamamatsu, Shizuoka, 430-0904, Japan

    Takafumi Kosugi

  8. Siemens Healthcare K.K, Gate City Osaki West Tower, 1-11-1 Osaki, Shinagawa-ku, Tokyo, 141-8644, Japan

    Yoshiaki Komori

  9. Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan

    Shinji Naganawa

  10. Department of Radiology, Fujita Health University Hospital, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan

    Yuya Yoneyama

  11. Department of Radiological Technology, Kariya Toyota General Hospital, 5-15 Sumiyoshi-cho, Kariya, Aichi, 448-8505, Japan

    Kenta Ishiguro

  12. Department of Radiological Sciences, Faculty of Health Sciences, Japan Health Care College, 434-1 Shinei, Kiyota-ku, Sapporo, Hokkaido, 004-0839, Japan

    Atsushi Fukuyama

  13. Department of Radiological Technology, Kitakyushu Municipal Medical Center, 1-1, Bashaku 2-chome, Kokurakita-ku, Kitakyushu, Fukuoka, 802-0077, Japan

    Kazuya Takiguchi

  14. Division of Clinical Radiology Service, Kyoto University Hospital, 54 Kawaharacho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan

    Tomoya Watanabe

Authors
  1. Yuya Yoneyama
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  2. Haruo Isoda
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  3. Kenta Ishiguro
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  4. Masaki Terada
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  5. Masaki Kamiya
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  6. Kenichi Otsubo
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  7. Roshani Perera
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  8. Takashi Mizuno
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  9. Atsushi Fukuyama
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  10. Kazuya Takiguchi
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  11. Tomoya Watanabe
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  12. Takafumi Kosugi
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  13. Yoshiaki Komori
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  14. Shinji Naganawa
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Corresponding author

Correspondence to Haruo Isoda.

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Conflict of interest

Haruo Isoda has received JSPS KAKENHI (Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research) (Grant Number 25293264).Takafumi Kosugi is an employee of Renaissance of Technology Corporation, Hamamatsu, Japan. Yoshiaki Komori is an employee of Siemens Healthcare K.K., Tokyo, Japan. The remaining authors declare that they have no conflict of interest.

Ethical approval

Our research project has been approved by our institutional review board (IRB). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Consent to participate

Informed consent was obtained from all individual participants as analysts included in the study. The image data used to create the silicone model was a secondary use of the data used in the relevant previous work already done with informed consent.

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Yoneyama, Y., Isoda, H., Ishiguro, K. et al. Evaluation of magnetic resonance angiography as a possible alternative to rotational angiography or computed tomography angiography for assessing cerebrovascular computational fluid dynamics. Phys Eng Sci Med 43, 1327–1337 (2020). https://doi.org/10.1007/s13246-020-00936-6

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  • DOI: https://doi.org/10.1007/s13246-020-00936-6

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