Automatic collateral circulation scoring in ischemic stroke using 4D CT angiography with low-rank and sparse matrix decomposition

Abstract

Purpose

Sufficient collateral blood supply is crucial for favorable outcomes with endovascular treatment. The current practice of collateral scoring relies on visual inspection and thus can suffer from inter and intra-rater inconsistency. We present a robust and automatic method to score cerebral collateral blood supply to aid ischemic stroke treatment decision making. The developed method is based on 4D dynamic CT angiography (CTA) and the ASPECTS scoring protocol.

Methods

The proposed method, ACCESS (Automatic Collateral Circulation Evaluation in iSchemic Stroke), estimates a target patient’s unfilled cerebrovasculature in contrast-enhanced CTA using the lack of contrast agent due to clotting. To do so, the fast robust matrix completion algorithm with in-face extended Frank–Wolfe optimization is applied on a cohort of healthy subjects and a target patient, to model the patient’s unfilled vessels and the estimated full vasculature as sparse and low-rank components, respectively. The collateral score is computed as the ratio of the unfilled vessels to the full vasculature, mimicking existing clinical protocols.

Results

ACCESS was tested with 46 stroke patients and obtained an overall accuracy of 84.78%. The optimal threshold selection was evaluated using a receiver operating characteristics curve with the leave-one-out approach, and a mean area under the curve of 85.39% was obtained.

Conclusion

ACCESS automates collateral scoring to mitigate the shortcomings of the standard clinical practice. It is a robust approach, which resembles how radiologists score clinical scans, and can be used to help radiologists in clinical decisions of stroke treatment.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Notes

  1. 1.

    http://www.world-heart-federation.org/cardiovascular-health/stroke/.

  2. 2.

    http://www.stnava.github.io/ANTs.

References

  1. 1.

    Ashikuzzaman M, Belasso C, Kibria MG, Bergdahl A, Gauthier CJ, Rivaz H (2019) Low rank and sparse decomposition of ultrasound color flow images for suppressing clutter in real-time. IEEE Trans Med Imaging. 39(4):1073–1084

    PubMed  Google Scholar 

  2. 2.

    Avants BB, Epstein CL, Grossman M, Gee JC (2008) Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 12(1):26–41

    CAS  PubMed  Google Scholar 

  3. 3.

    Boers A, Barros RS, Jansen I, Berkhemer O, Beenen L, Menon BK, Dippel D, van der Lugt A, van Zwam W, Roos Y, van Oostenbrugge RJ (2018) Value of quantitative collateral scoring on CT angiography in patients with acute ischemic stroke. Am J Neuroradiol 39(6):1074–1082

    CAS  PubMed  Google Scholar 

  4. 4.

    Cohen J (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20(1):37–46

    Google Scholar 

  5. 5.

    Coutts SB, Hill MD, Demchuk AM, Barber PA, Pexman J, Buchan A, Mak H, Yau K, Chan B (2003) ASPECTS reading requires training and experience. Stroke 34(10):e179

    PubMed  Google Scholar 

  6. 6.

    Cuccione E, Padovano G, Versace A, Ferrarese C, Beretta S (2016) Cerebral collateral circulation in experimental ischemic stroke. Exp Transl Stroke Med. https://doi.org/10.1186/s13231-016-0015-0

    PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27(8):861–874

    Google Scholar 

  8. 8.

    Fleiss JL (1971) Measuring nominal scale agreement among many raters. Psychol Bull 76(5):378

    Google Scholar 

  9. 9.

    Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, Collins DL, Group BDC (2011) Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 54(1):313–327

    PubMed  Google Scholar 

  10. 10.

    Freund RM, Grigas P, Mazumder R (2017) An extended Frank–Wolfe method with “in-face” directions, and its application to low-rank matrix completion. SIAM J Optim 27(1):319–346

    Google Scholar 

  11. 11.

    Frolich AM, Wolff SL, Psychogios MN, Klotz E, Schramm R, Wasser K, Knauth M, Schramm P (2014) Time-resolved assessment of collateral flow using 4D CT angiography in large-vessel occlusion stroke. Eur Radiol 24(2):390–396

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Grotta JC, Chiu D, Lu M, Patel S, Levine SR, Tilley BC, Brott TG, HaleyJr EC, Lyden PD, Kothari R, Franke M, Lewandowski CA, Libman R, Kwiatkowski T, Broderick JP, Marler JR, Corrigan J, Huff S, Mitsias P, Talati S, Tanne D (1999) Agreement and variability in the interpretation of early CT changes in stroke patients qualifying for intravenous rtPA therapy. Stroke 30(8):1528–1533

    CAS  PubMed  Google Scholar 

  13. 13.

    Grunwald IQ, Kulikovski J, Reith W, Gerry S, Namias R, Politi M, Papanagiotou P, Essig M, Mathur S, Joly O, Hussain K, Wagner V, Shah S, Harston G, Vlahovic J, Walter S, Podlasek A, Fassbenderh K (2019) Collateral automation for triage in stroke: evaluating automated scoring of collaterals in acute stroke on computed tomography scans. Cerebrovasc Dis 47(5–6):217–222

    PubMed  PubMed Central  Google Scholar 

  14. 14.

    Huck J, Wanner Y, Fan AP, Jäger AT, Grahl S, Schneider U, Villringer A, Steele CJ, Tardif CL, Bazin PL, Gauthier CJ (2019) High resolution atlas of the venous brain vasculature from 7 T quantitative susceptibility maps. Brain Struct Funct 224(7):2467–2485

    CAS  PubMed  Google Scholar 

  15. 15.

    Jerman T, Pernuš F, Likar B, Špiclin Ž (2016) Enhancement of vascular structures in 3D and 2D angiographic images. IEEE Trans Med Imaging 35(9):2107–2118

    PubMed  Google Scholar 

  16. 16.

    Jin M, Hao D, Ding S, Qin B (2018) Low-rank and sparse decomposition with spatially adaptive filtering for sequential segmentation of 2D+t vessels. Phys Med Biol 63(17):17LT01

    PubMed  Google Scholar 

  17. 17.

    Kersten-Oertel M, Alamer A, Fonov V, Lo B, Tampieri D, Collins L (2016) Towards a computed collateral circulation score in ischemic stroke. arXiv preprint arXiv:2001.07169

  18. 18.

    Kuang H, Najm M, Chakraborty D, Maraj N, Sohn SI, Goyal M, Hill M, Demchuk A, Menon B, Qiu W (2018) Automated ASPECTS on non-contrast CT scans in acute ischemic stroke patients using machine learning. Am J Neuroradiol 40:33–38. https://doi.org/10.3174/ajnr.A5889

    PubMed  Article  Google Scholar 

  19. 19.

    Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33:159–174

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Lin Z, Chen M, Ma Y (2010) The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXiv preprint arXiv:1009.5055

  21. 21.

    Meijs M, Patel A, van de Leemput SC, Prokop M, van Dijk EJ, de Leeuw FE, Meijer FJ, van Ginneken B, Manniesing R (2017) Robust segmentation of the full cerebral vasculature in 4D CT of suspected stroke patients. Sci Rep 7(1):1–12

    CAS  Google Scholar 

  22. 22.

    Moccia S, De Momi E, El Hadji S, Mattos LS (2018) Blood vessel segmentation algorithms-review of methods, datasets and evaluation metrics. Comput Methods Programs Biomed 158:71–91

    PubMed  Google Scholar 

  23. 23.

    Pexman JW, Barber PA, Hill MD, Sevick RJ, Demchuk AM, Hudon ME, Hu WY, Buchan AM (2001) Use of the Alberta stroke program early CT score (ASPECTS) for assessing CT scans in patients with acute stroke. Am J Neuroradiol 22(8):1534–1542

    CAS  PubMed  Google Scholar 

  24. 24.

    Piedade GS, Schirmer CM, Goren O, Zhang H, Aghajanian A, Faber JE, Griessenauer CJ (2019) Cerebral collateral circulation: a review in the context of ischemic stroke and mechanical thrombectomy. World Neurosurg 122:33–42

    PubMed  Google Scholar 

  25. 25.

    Rezaei B, Ostadabbas S (2017) Background subtraction via fast robust matrix completion. In: Proceedings of the IEEE international conference on computer vision, pp 1871–1879

  26. 26.

    Shieh Y, Chang CH, Shieh M, Lee TH, Chang YJ, Wong HF, Chin SC, Goodwin S (2014) Computer-aided diagnosis of hyperacute stroke with thrombolysis decision support using a contralateral comparative method of CT image analysis. J Digit Imaging 27(3):392–406

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Tan I, Demchuk A, Hopyan J, Zhang L, Gladstone D, Wong K, Martin M, Symons S, Fox A, Aviv R (2009) CT angiography clot burden score and collateral score: correlation with clinical and radiologic outcomes in acute middle cerebral artery infarct. Am J Neuroradiol 30(3):525–531

    CAS  PubMed  Google Scholar 

  28. 28.

    von Kummer R, Holle R, Gizyska U, Hofmann E, Jansen O, Petersen D, Schumacher M, Sartor K (1996) Interobserver agreement in assessing early CT signs of middle cerebral artery infarction. Am J Neuroradiol 17(9):1743–1748

    Google Scholar 

  29. 29.

    Xiao Y, Alamer A, Fonov V, Lo BW, Tampieri D, Collins DL, Rivaz H, Kersten-Oertel M (2017) Towards automatic collateral circulation score evaluation in ischemic stroke using image decompositions and support vector machines. In: Molecular imaging, reconstruction and analysis of moving body organs, and stroke imaging and treatment. Lecture Notes in Computer Science, vol 10555. Springer, Cham

  30. 30.

    Yang X, Liu C, Le Minh H, Wang Z, Chien A, Cheng KTT (2017) An automated method for accurate vessel segmentation. Phys Med Biol 62(9):3757

    PubMed  Google Scholar 

  31. 31.

    Zhang S, Chen W, Tang H, Han Q, Yan S, Zhang X, Chen Q, Parsons M, Wang S, Lou M (2016) The prognostic value of a four-dimensional CT angiography-based collateral grading scale for reperfusion therapy in acute ischemic stroke patients. PLoS ONE 11(8):e0160502

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This study was funded by NSERC Discovery Grant RGPIN 04136 and Fonds de recherche du Quebec—Nature et technologies (FRQNT Grant F01296). The author Y. Xiao is supported by BrainsCAN and CIHR fellowships. We would like to thank Dr. Ali Alamer and Dr. Johanna Ortiz Jimenez for facilitating data acquisition and annotation.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Mumu Aktar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the University Human Research Ethics Committee.

Informed consent

Informed consent was obtained from all participants included in the study.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Aktar, M., Tampieri, D., Rivaz, H. et al. Automatic collateral circulation scoring in ischemic stroke using 4D CT angiography with low-rank and sparse matrix decomposition. Int J CARS 15, 1501–1511 (2020). https://doi.org/10.1007/s11548-020-02216-w

Download citation

Keywords

  • Ischemic stroke
  • Collateral supply
  • CT angiography
  • Low-rank and sparse