Skip to main content

Advertisement

SpringerLink
Log in

Comparison of 1.5 T and 3 T magnetic resonance angiography for detecting cerebral aneurysms using deep learning-based computer-assisted detection software

  • Diagnostic Neuroradiology
  • Published:
Neuroradiology Aims and scope Submit manuscript

Abstract

Purpose

To compare the diagnostic performance of 1.5 T versus 3 T magnetic resonance angiography (MRA) for detecting cerebral aneurysms with clinically available deep learning-based computer-assisted detection software (EIRL aneurysm® [EIRL_an]), which has been approved by the Japanese Pharmaceuticals and Medical Devices Agency. We also sought to analyze the causes of potential false positives.

Methods

In this single-center, retrospective study, we evaluated the MRA scans of 90 patients who underwent head MRA (1.5 T and 3 T in 45 patients each) in clinical practice. Overall, 51 patients had 70 aneurysms. We used MRI from a vendor not included in the dataset used to create the EIRL_an algorithm. Two radiologists determined the ground truth, the accuracy of the candidates noted by EIRL_an, and the causes of false positives. The sensitivity, number of false positives per case (FPs/case), and the causes of false positives were compared between 1.5 T and 3 T MRA. Pearson’s χ2 test, Fisher’s exact test, and the Mann‒Whitney U test were used for the statistical analyses as appropriate.

Results

The sensitivity was high for 1.5 T and 3 T MRA (0.875‒1), but the number of FPs/case was significantly higher with 3 T MRA (1.511 vs. 2.578, p < 0.001). The most common causes of false positives (descending order) were the origin/bifurcation of vessels/branches, flow-related artifacts, and atherosclerosis and were similar between 1.5 T and 3 T MRA.

Conclusion

EIRL_an detected significantly more false-positive lesions with 3 T than with 1.5 T MRA in this external validation study. Our data may help physicians with limited experience with MRA to correctly diagnose aneurysms using EIRL_an.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Abbreviations

3D:

Three-dimensional

ACA:

Anterior cerebral artery

AI:

Artificial intelligence

ARSS:

Abierto Reading Support Solution

CAD:

Computer-assisted detection

CNN:

Convolutional neural network

EIRL_an:

EIRL aneurysm

FOV:

Field of view

FPs/case:

False positives per case

ICA:

Internal carotid artery

MCA:

Middle cerebral artery

MIP:

Maximum intensity projection

MRA:

Magnetic resonance angiography

MRI:

Magnetic resonance imaging

PMDA:

Pharmaceuticals and Medical Devices Agency

TE:

Echo time

TOF:

Time-of-flight

TR:

Repetition time

SAH:

Subarachnoid hemorrhage

References

  1. Ajiboye N, Chalouhi N, Starke RM et al (2015) Unruptured cerebral aneurysms: evaluation and management. Sci World J 2015:954954

    Article  Google Scholar 

  2. Macdonald RL, Schweizer TA (2017) Spontaneous subarachnoid haemorrhage. Lancet 389:655–666

    Article  PubMed  Google Scholar 

  3. Rinkel GJE, Algra A (2011) Long-term outcomes of patients with aneurysmal subarachnoid haemorrhage. Lancet Neurol 10:349–356

    Article  PubMed  Google Scholar 

  4. Sailer AMH, Wagemans BAJM, Nelemans PJ et al (2014) Diagnosing intracranial aneurysms with MR angiography: systematic review and meta-analysis. Stroke 45:119–126

    Article  PubMed  Google Scholar 

  5. Okahara M, Kiyosue H, Yamashita M et al (2002) Diagnostic accuracy of magnetic resonance angiography for cerebral aneurysms in correlation with 3D-digital subtraction angiographic images: a study of 133 aneurysms. Stroke 33:1803–1808

    Article  PubMed  Google Scholar 

  6. Din M, Agarwal S, Grzeda M et al (2023) Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis. J Neurointerv Surg 15:262–271

  7. Shi Z, Hu B, Schoepf UJ et al (2020) Artificial intelligence in the management of intracranial aneurysms: current status and future perspectives. AJNR Am J Neuroradiol 41:373–379

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Claux F, Baudouin M, Bogey C, Rouchaud A (2023) Dense, deep learning-based intracranial aneurysm detection on TOF MRI using two-stage regularized U-Net. J Neuroradiol 50:9–15

    Article  PubMed  Google Scholar 

  9. Lei X, Yang Y (2022) Deep learning-based magnetic resonance imaging in diagnosis and treatment of intracranial aneurysm. Comput Math Methods Med 2022:1683475

    Article  PubMed  PubMed Central  Google Scholar 

  10. Terasaki Y, Yokota H, Tashiro K et al (2021) Multidimensional deep learning reduces false-positives in the automated detection of cerebral aneurysms on time-of-flight magnetic resonance angiography: a multi-center study. Front Neurol 12:742126

    Article  PubMed  Google Scholar 

  11. Ishihara M, Shiiba M, Maruno H et al (2023) Detection of intracranial aneurysms using deep learning-based CAD system: usefulness of the scores of CNN’s final layer for distinguishing between aneurysm and infundibular dilatation. Jpn J Radiol 41:131–141

    Article  PubMed  Google Scholar 

  12. Nakao T, Hanaoka S, Nomura Y et al (2018) Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography. J Magn Reson Imaging 47:948–953

    Article  PubMed  Google Scholar 

  13. Tajima T, Akai H, Yasaka K et al (2023) Usefulness of deep learning-based noise reduction for 1.5 T MRI brain images. Clin Radiol 78:e13–e21

    Article  CAS  PubMed  Google Scholar 

  14. Ueda D, Yamamoto A, Nishimori M et al (2019) Deep learning for MR angiography: automated detection of cerebral aneurysms. Radiology 290:187–194

    Article  PubMed  Google Scholar 

  15. Hayashi N, Masutani Y, Masumoto T et al (2003) Feasibility of a curvature-based enhanced display system for detecting cerebral aneurysms in MR angiography. Magn Reson Med Sci 2:29–36

    Article  PubMed  Google Scholar 

  16. Runge VM, Heverhagen JT (2022) The clinical utility of magnetic resonance imaging according to field strength, specifically addressing the breadth of current state-of-the-art systems, which include 0.55 T, 1.5 T, 3 T, and 7 T. Invest Radiol 57:1–12

    Article  PubMed  Google Scholar 

  17. Yoon NK, McNally S, Taussky P, Park MS (2016) Imaging of cerebral aneurysms: a clinical perspective. Neurovasc Imaging 2:1–7

    Article  Google Scholar 

  18. Gibbs GF, Huston J, Bernstein MA et al (2004) Improved image quality of intracranial aneurysms: 3.0-T versus 1.5-T time-of-flight MR angiography. AJNR Am J Neuroradiol 25:84–87

    PubMed  PubMed Central  Google Scholar 

  19. Ozsarlak O, Van Goethem JW, Maes M, Parizel PM (2004) MR angiography of the intracranial vessels: technical aspects and clinical applications. Neuroradiology 46:955–972

    Article  PubMed  Google Scholar 

Download references

Funding

This study was technically and financially supported by Canon Medical Systems Corporation. Any data and information included in this study were not controlled by Canon Medical Systems Corporation.

Author information

Authors and Affiliations

  1. Department of Radiology, International University of Health and Welfare Mita Hospital, 1-4-3 Mita, Minato-Ku, Tokyo, 108-8329, Japan

    Taku Tajima & Akira Kunimatsu

  2. Department of Radiology, International University of Health and Welfare Narita Hospital, 852 Hatakeda, Narita, Chiba, 286-0124, Japan

    Taku Tajima, Hiroyuki Akai, Koichiro Yasaka, Akira Kunimatsu, Naoki Yoshioka, Masaaki Akahane & Shigeru Kiryu

  3. Department of Radiology, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-Ku, Tokyo, 108-8639, Japan

    Hiroyuki Akai

  4. Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-0033, Japan

    Koichiro Yasaka & Osamu Abe

  5. International University of Health and Welfare, 2600-1 Kitakanamaru, Otawara, Tochigi, 324-8501, Japan

    Kuni Ohtomo

Authors
  1. Taku Tajima
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hiroyuki Akai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Koichiro Yasaka
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Akira Kunimatsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Naoki Yoshioka
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Masaaki Akahane
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kuni Ohtomo
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Osamu Abe
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Shigeru Kiryu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shigeru Kiryu.

Ethics declarations

Conflict of interest

The author declares a conflict of interest: Shigeru Kiryu got research grants from Canon Medical Systems Corporation.

Ethical approval

This retrospective study of MRI data analysis using AI was approved by our Institutional Review Board (approval no. 20-Nr-056).

Informed consent

Written informed consent was waived by the Institutional Review Board.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tajima, T., Akai, H., Yasaka, K. et al. Comparison of 1.5 T and 3 T magnetic resonance angiography for detecting cerebral aneurysms using deep learning-based computer-assisted detection software. Neuroradiology 65, 1473–1482 (2023). https://doi.org/10.1007/s00234-023-03216-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00234-023-03216-8

Keywords

Search

Navigation