Skip to main content

Advertisement

SpringerLink
Log in

Correlation between pretreatment and follow-up infarct volume using CT perfusion imaging: the Bayesian versus singular value decomposition method

  • Original Article
  • Published:
Neurological Sciences Aims and scope Submit manuscript

Abstract

Purpose

Pretreatment ischemic core volume is conceptually equal to follow-up infarct volume (FIV) in patients with successful recanalization. However, there is sometimes an absolute volume difference (AD) between pretreatment core volume and FIV. The aim was to compare the AD values between the Bayesian and the singular value decomposition (SVD) methods with time from onset-to-imaging in acute ischemic stroke (AIS) patients undergoing mechanical thrombectomy.

Methods

Consecutive AIS patients were included if they had the following: (1) anterior large vessel occlusion (internal carotid or middle cerebral artery); (2) within 24 h of onset; (3) pretreatment CT perfusion (CTP); (4) successful recanalization (mTICI ≥ 2b); and (5) 24-h diffusion-weighted imaging (DWI). FIV was measured on 24-h DWI. The AD value between FIV and the pretreatment core volume was calculated for Bayesian and SVD methods. Spearman’s rank correlation coefficient (rho) was calculated as appropriate.

Results

In the 47 patients enrolled (25 men; median age 78 years; median baseline National Institutes of Health Stroke Scale, 22), the median time from onset-to-imaging and onset-to-recanalization was 136 and 220 min, respectively. Shorter onset-to-imaging time was correlated with a larger AD value, and more trend was seen in the SVD method (rho =  − 0.28, p = 0.05) compared with the Bayesian method (rho =  − 0.08). A larger pretreatment core volume was correlated with a larger AD value, and this tendency was slightly stronger for the SVD (rho = 0.63, p < 0.01) than for the Bayesian (rho = 0.32, p = 0.03) method.

Conclusions

The Bayesian method might be more correlated with FIV than the SVD method in patients with a large ischemic lesion immediately after stroke onset, but not perfect.

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

Similar content being viewed by others

Data Availability

The anonymized data that support the findings of our study are available from the corresponding author upon reasonable request.

Abbreviations

AIS:

Acute ischemic stroke

AD:

Absolute volume difference

CBF:

Cerebral blood flow

CBV:

Cerebral blood volume

CTP:

Computed tomography perfusion

DT:

Delay time

DWI:

Diffusion-weighted imaging

FIV:

Follow-up infarct volume

MT:

Mechanical thrombectomy

mTICI:

Modified Treatment In Cerebral Infarction

MTT:

Mean transit time

SVD:

Singular value decomposition

TTP:

Time to peak

References

  1. Goyal M, Menon BK, van Zwam WH, Dippel DW, Mitchell PJ, Demchuk AM, Dávalos A, Majoie CB, van der Lugt A, de Miquel MA, Donnan GA, Roos YB, Bonafe A, Jahan R, Diener HC, van den Berg LA, Levy EI, Berkhemer OA, Pereira VM, Rempel J, Millán M, Davis SM, Roy D, Thornton J, Román LS, Ribó M, Beumer D, Stouch B, Brown S, Campbell BC, van Oostenbrugge RJ, Saver JL, Hill MD, Jovin TG, HERMES collaborators (2016) Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials. Lancet 387:1723–1731

    Article  PubMed  Google Scholar 

  2. Nogueira RG, Jadhav AP, Haussen DC, Bonafe A, Budzik RF, Bhuva P, Yavagal DR, Ribo M, Cognard C, Hanel RA, Sila CA, Hassan AE, Millan M, Levy EI, Mitchell P, Chen M, English JD, Shah QA, Silver FL, Pereira VM, Mehta BP, Baxter BW, Abraham MG, Cardona P, Veznedaroglu E, Hellinger FR, Feng L, Kirmani JF, Lopes DK, Jankowitz BT, Frankel MR, Costalat V, Vora NA, Yoo AJ, Malik AM, Furlan AJ, Rubiera M, Aghaebrahim A, Olivot JM, Tekle WG, Shields R, Graves T, Lewis RJ, Smith WS, Liebeskind DS, Saver JL, Jovin TG, DAWN Trial Investigators (2018) Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct. N Engl J Med 378:11–21

    Article  PubMed  Google Scholar 

  3. Albers GW, Marks MP, Kemp S, Christensen S, Tsai JP, Ortega-Gutierrez S, McTaggart RA, Torbey MT, Kim-Tenser M, Leslie-Mazwi T, Sarraj A, Kasner SE, Ansari SA, Yeatts SD, Hamilton S, Mlynash M, Heit JJ, Zaharchuk G, Kim S, Carrozzella J, Palesch YY, Demchuk AM, Bammer R, Lavori PW, Broderick JP, Lansberg MG, DEFUSE 3 Investigators (2018) Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging. N Engl J Med. 378:708–718

    Article  PubMed  PubMed Central  Google Scholar 

  4. Bivard A, Levi C, Krishnamurthy V, McElduff P, Miteff F, Spratt NJ, Bateman G, Donnan G, Davis S, Parsons M (2015) Perfusion computed tomography to assist decision making for stroke thrombolysis. Brain 138:1919–1931

    Article  PubMed  PubMed Central  Google Scholar 

  5. Kudo K, Christensen S, Sasaki M, Ostergaard L, Shirato H, Ogasawara K, Wintermark M, Warach S (2013) Accuracy and reliability assessment of CT and MR perfusion analysis software using a digital phantom. Radiology 267:201–211

    Article  PubMed  PubMed Central  Google Scholar 

  6. Boned S, Padroni M, Rubiera M, Tomasello A, Coscojuela P, Romero N, Muchada M, Rodríguez-Luna D, Flores A, Rodríguez N, Juega J, Pagola J, Alvarez-Sabin J, Molina CA, Ribó M (2017) Admission CT perfusion may overestimate initial infarct core: the ghost infarct core concept. J Neurointerv Surg 9:66–69

    Article  PubMed  Google Scholar 

  7. d’Esterre CD, Boesen ME, Ahn SH, Pordeli P, Najm M, Minhas P, Davari P, Fainardi E, Rubiera M, Khaw AV, Zini A, Frayne R, Hill MD, Demchuk AM, Sajobi TT, Forkert ND, Goyal M, Lee TY, Menon BK (2015) Time-dependent computed tomographic perfusion thresholds for patients with acute ischemic stroke. Stroke 46:3390–3397

    Article  PubMed  Google Scholar 

  8. Bivard A, Kleinig T, Miteff F, Butcher K, Lin L, Levi C, Parsons M (2017) Ischemic core thresholds change with time to reperfusion: a case control study. Ann Neurol 82:995–1003

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Bouslama M, Ravindran K, Rodrigues GM, Pisani L, Haussen DC, Frankel MR, Nogueira RG (2023) Falsely normal CT perfusion ischemic core readings are common and often associated with deep infarcts. J Neurointerv Surg 15:183–187

    Article  PubMed  Google Scholar 

  10. Rava RA, Snyder KV, Mokin M, Waqas M, Zhang X, Podgorsak AR, Allman AB, Senko J, Bhurwani MMS, Hoi Y, Davies JM, Levy EI, Siddiqui AH, Ionita CN (2021) Assessment of computed tomography perfusion software in predicting spatial location and volume of infarct in acute ischemic stroke patients: a comparison of Sphere, Vitrea, and RAPID. J Neurointerv Surg 13:130–135

    Article  PubMed  Google Scholar 

  11. Mouridsen K, Friston K, Hjort N, Gyldensted L, Østergaard L, Kiebel S (2006) Bayesian estimation of cerebral perfusion using a physiological model of microvasculature. Neuroimage 33:570–579

    Article  PubMed  Google Scholar 

  12. Boutelier T, Kudo K, Pautot F, Sasaki M (2012) Bayesian hemodynamic parameter estimation by bolus tracking perfusion weighted imaging. IEEE Trans Med Imaging 31:1381–1395

    Article  PubMed  Google Scholar 

  13. Sakai Y, Delman BN, Fifi JT, Tuhrim S, Wheelwright D, Doshi AH, Mocco J, Nael K (2018) Estimation of ischemic core volume using computed tomographic perfusion. Stroke 49:2345–2352

    Article  PubMed  Google Scholar 

  14. Ichikawa S, Yamamoto H, Morita T (2021) Comparison of a Bayesian estimation algorithm and singular value decomposition algorithms for 80-detector row CT perfusion in patients with acute ischemic stroke. Radiol Med 126:795–803

    Article  PubMed  Google Scholar 

  15. Rava RA, Snyder KV, Mokin M, Waqas M, Allman AB, Senko JL, Podgorsak AR, Shiraz Bhurwani MM, Hoi Y, Siddiqui AH, Davies JM, Levy EI, Ionita CN (2020) Assessment of a Bayesian Vitrea CT perfusion analysis to predict final infarct and penumbra volumes in patients with acute ischemic stroke: a comparison with RAPID. AJNR Am J Neuroradiol 41:206–212

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Benson J, Payabvash S, Salazar P, Jagadeesan B, Palmer CS, Truwit CL, McKinney AM (2015) Comparison of CT perfusion summary maps to early diffusion-weighted images in suspected acute middle cerebral artery stroke. Eur J Radiol 84:682–689

    Article  PubMed  Google Scholar 

  17. Goyal M, Fargen KM, Turk AS, Mocco J, Liebeskind DS, Frei D, Demchuk AM (2014) 2C or not 2C: defining an improved revascularization grading scale and the need for standardization of angiography outcomes in stroke trials. J Neurointerv Surg 6:83–86

    Article  PubMed  Google Scholar 

  18. Sasaki M, Kudo K, Boutelier T, Pautot F, Christensen S, Uwano I, Goodwin J, Higuchi S, Ito K, Yamashita F (2013) Assessment of the accuracy of a Bayesian estimation algorithm for perfusion CT by using a digital phantom. Neuroradiology 55:1197–1203

    Article  PubMed  Google Scholar 

  19. Kudo K, Boutelier T, Pautot F, Honjo K, Hu JQ, Wang HB, Shintaku K, Uwano I, Sasaki M (2014) Bayesian analysis of perfusion-weighted imaging to predict infarct volume: comparison with singular value decomposition. Magn Reson Med Sci 13:45–50

    Article  PubMed  Google Scholar 

  20. Lansberg MG, O’Brien MW, Tong DC, Moseley ME, Albers GW (2001) Evolution of cerebral infarct volume assessed by diffusion-weighted magnetic resonance imaging. Arch Neurol 58:613–617

    Article  CAS  PubMed  Google Scholar 

  21. Wheeler HM, Mlynash M, Inoue M, Tipirnini A, Liggins J, Bammer R, Lansberg MG, Kemp S, Zaharchuk G, Straka M, Albers GW, DEFUSE 2 Investigators (2015) The growth rate of early DWI lesions is highly variable and associated with penumbral salvage and clinical outcomes following endovascular reperfusion. Int J Stroke 10:723–729

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Stroke and Cerebrovascular Medicine, Kyorin University, 6-20-2, Shinkawa, Tokyo, Mitaka, 181-8611, Japan

    Hiroyuki Kawano, Mikito Saito, Tatsuo Amano & Teruyuki Hirano

  2. Department of Radiology, Kyorin University Hospital, 6-20-2, Shinkawa, Tokyo, Mitaka, 181-8611, Japan

    Takuya Adachi

  3. Department of Radiology, Kyorin University, 6-20-2, Shinkawa, Tokyo, Mitaka, 181-8611, Japan

    Miho Gomyo & Kenichi Yokoyama

  4. Department of Neurosurgery, Kyorin University, 6-20-2, Shinkawa, Tokyo, Mitaka, 181-8611, Japan

    Yoshiaki Shiokawa

Authors
  1. Hiroyuki Kawano
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Takuya Adachi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mikito Saito
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tatsuo Amano
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Miho Gomyo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kenichi Yokoyama
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yoshiaki Shiokawa
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Teruyuki Hirano
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hiroyuki Kawano.

Ethics declarations

Ethical approval and Informed consent

This study protocol was approved by the Faculty of Medicine Research Ethics Committee, Kyorin University, with waiver of informed consent.

Competing interests

This study was supported by the Japan Society for the Promotion of Science KAKENHI (JP21K07468) and SENSHIN Medical Research Foundation. The authors have no competing interests to declare that are relevant to the content of this article.

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

Kawano, H., Adachi, T., Saito, M. et al. Correlation between pretreatment and follow-up infarct volume using CT perfusion imaging: the Bayesian versus singular value decomposition method. Neurol Sci 44, 2041–2047 (2023). https://doi.org/10.1007/s10072-023-06627-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10072-023-06627-w

Keywords

Search

Navigation