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European Radiology
Published in: European Radiology 12/2020

01-12-2020 | Computed Tomography | Computed Tomography

Post-processing of computed tomography perfusion in patients with acute cerebral ischemia: variability of inter-reader, inter-region of interest, inter-input model, and inter-software

Authors: Zhong-Ping Chen, Zhen-Zhen Shi, Yun-Geng Li, Yan Guo, Dan Tong

Published in: European Radiology | Issue 12/2020

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Abstract

Objective

To determine the reproducibility of quantitative computed tomography perfusion (CTP) parameters generated using different post-processing methods and identify the relative impact of subjective factors on the robustness of CTP parameters in acute ischemic stroke (AIS).

Materials and methods

A total of 80 CTP datasets from patients with AIS or transient ischemic attack (TIA) were retrospectively post-processed by two observers using different regions of interest (ROI) types, input models, and software. The CTP parameters were derived for 10 parenchymal ROIs. The intra-class correlation coefficients (ICCs) were used to assess the reproducibility of the CTP parameters for various post-processing methods. The Spearman correlation test was used to detect potential relationships between software and input models.

Results

The ICCs with 95% confidence intervals (CIs) were 0.94 (0.93–0.96), 0.94 (0.92–0.96), 0.82 (0.79–0.86), and 0.87 (0.85–0.90) for inter-reader agreement by using elliptic ROI, irregular ROI, single-input model, and dual-input model, respectively. The ICCs with 95% CI were 0.98 (0.98–0.98), 0.46 (0.43–0.50), and 0.25 (0.20–0.30) for inter-ROI type, inter-input model, and inter-software agreement, respectively.

Conclusions

Although the CTP parameters were stable when measured using different readers with different ROI types, they varied for different input models and software. The standardization of CTP post-processing is essential to reduce variability of CTP values.

Key Points

• The CTP parameters derived by different readers with different ROI types have agreements that range from good to excellent.
• The CTP parameters derived from different input models and software programs have poor agreement but significant correlations.
Appendix
Available only for authorised users
Literature
1.
Powers WJ, Rabinstein AA, Ackerson T et al (2018) 2018 Guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 49:e46–e110CrossRefPubMed
2.
Nogueira RG, Jadhav AP, Haussen DC et al (2018) Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct. N Engl J Med 378:11–21CrossRef
3.
Albers GW, Marks MP, Kemp S et al (2018) Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging. N Engl J Med 378:708–718CrossRefPubMed
4.
5.
Arenillas JF, Cortijo E, Garcia-Bermejo P et al (2018) Relative cerebral blood volume is associated with collateral status and infarct growth in stroke patients in SWIFT PRIME. J Cereb Blood Flow Metab 38:1839–1847CrossRefPubMed
6.
Furlanis G, Ajcevic M, Stragapede L et al (2018) Ischemic volume and neurological deficit: correlation of computed tomography perfusion with the National Institutes of Health Stroke Scale score in acute ischemic stroke. J Stroke Cerebrovasc Dis 27:2200–2207CrossRefPubMed
7.
Biggs D, Silverman ME, Chen F, Walsh B, Wynne P (2019) How should we treat patients who wake up with a stroke? A review of recent advances in management of acute ischemic stroke. Am J Emerg Med 37:954–959CrossRefPubMed
8.
Giles MF, Albers GW, Amarenco P et al (2011) Early stroke risk and ABCD2 score performance in tissue- vs time-defined TIA: a multicenter study. Neurology 77:1222–1228CrossRefPubMed
9.
Easton JD, Saver JL, Albers GW et al (2009) Definition and evaluation of transient ischemic attack: a scientific statement for healthcare professionals from the American Heart Association/American Stroke Association Stroke Council; Council on Cardiovascular Surgery and Anesthesia; Council on Cardiovascular Radiology and Intervention; Council on Cardiovascular Nursing; and the Interdisciplinary Council on Peripheral Vascular Disease. The American Academy of Neurology affirms the value of this statement as an educational tool for neurologists. Stroke 40:2276–2293CrossRefPubMed
10.
Tung CE, Olivot JM, Albers GW (2014) Radiological examinations of transient ischemic attack. Front Neurol Neurosci 33:115–122CrossRefPubMed
11.
Kamalian S, Kamalian S, Konstas AA et al (2012) CT perfusion mean transit time maps optimally distinguish benign oligemia from true “at-risk” ischemic penumbra, but thresholds vary by postprocessing technique. AJNR Am J Neuroradiol 33:545–549CrossRefPubMed
12.
Kolossvary M, De Cecco CN, Feuchtner G, Maurovich-Horvat P (2019) Advanced atherosclerosis imaging by CT: radiomics, machine learning and deep learning. J Cardiovasc Comput Tomogr 13:274–280CrossRefPubMed
13.
Goyal M, Jadhav AP, Bonafe A et al (2016) Analysis of workflow and time to treatment and the effects on outcome in endovascular treatment of acute ischemic stroke: results from the SWIFT PRIME randomized controlled trial. Radiology 279:888–897CrossRefPubMed
14.
Murase K, Nanjo T, Ii S et al (2005) Effect of x-ray tube current on the accuracy of cerebral perfusion parameters obtained by CT perfusion studies. Phys Med Biol 50:5019–5029CrossRefPubMed
15.
Wintermark M, Smith WS, Ko NU, Quist M, Schnyder P, Dillon WP (2004) Dynamic perfusion CT: optimizing the temporal resolution and contrast volume for calculation of perfusion CT parameters in stroke patients. AJNR Am J Neuroradiol 25:720–729PubMed
16.
Van der Schaaf I, Vonken EJ, Waaijer A, Velthuis B, Quist M, van Osch T (2006) Influence of partial volume on venous output and arterial input function. AJNR Am J Neuroradiol 27:46–50PubMed
17.
Zussman BM, Boghosian G, Gorniak RJ et al (2011) The relative effect of vendor variability in CT perfusion results: a method comparison study. AJR Am J Roentgenol 197:468–473CrossRefPubMed
18.
Koopman MS, Berkhemer OA, Geuskens R et al (2019) Comparison of three commonly used CT perfusion software packages in patients with acute ischemic stroke. J Neurointerv Surg 11:1249–1256CrossRefPubMed
19.
Sakai Y, Delman BN, Fifi JT et al (2018) Estimation of ischemic core volume using computed tomographic perfusion. Stroke 49:2345–2352CrossRefPubMed
20.
Kao YH, Mu Huo Teng M, Kao YT et al (2014) Automatic measurements of arterial input and venous output functions on cerebral computed tomography perfusion images: a preliminary study. Comput Biol Med 51:51–60CrossRefPubMed
21.
Soares BP, Dankbaar JW, Bredno J et al (2009) Automated versus manual post-processing of perfusion-CT data in patients with acute cerebral ischemia: influence on interobserver variability. Neuroradiology 51:445–451CrossRefPubMed
22.
Calamante F (2013) Arterial input function in perfusion MRI: a comprehensive review. Prog Nucl Magn Reson Spectrosc 74:1–32CrossRef
23.
Kudo K, Sasaki M, Yamada K et al (2010) Differences in CT perfusion maps generated by different commercial software: quantitative analysis by using identical source data of acute stroke patients. Radiology 254:200–209CrossRefPubMed
24.
Koo TK, Li MY (2016) A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 15:155–163CrossRefPubMed
25.
Waaijer A, van der Schaaf IC, Velthuis BK et al (2007) Reproducibility of quantitative CT brain perfusion measurements in patients with symptomatic unilateral carotid artery stenosis. AJNR Am J Neuroradiol 28:927–932PubMed
26.
Riordan AJ, Bennink E, Viergever MA, Velthuis BK, Dankbaar JW, de Jong HW (2013) CT brain perfusion protocol to eliminate the need for selecting a venous output function. AJNR Am J Neuroradiol 34:1353–1358CrossRefPubMed
27.
Bennink E, Oosterbroek J, Horsch AD et al (2015) Influence of thin slice reconstruction on CT brain perfusion analysis. PLoS One 10:e0137766CrossRefPubMed
28.
Fieselmann A, Kowarschik M, Ganguly A, Hornegger J, Fahrig R (2011) Deconvolution-based CT and MR brain perfusion measurement: theoretical model revisited and practical implementation details. Int J Biomed Imaging 2011:467563CrossRefPubMed
29.
Campbell BC, Christensen S, Levi CR et al (2011) Cerebral blood flow is the optimal CT perfusion parameter for assessing infarct core. Stroke 42:3435–3440CrossRefPubMed
30.
Kasasbeh AS, Christensen S, Parsons MW, Campbell B, Albers GW, Lansberg MG (2019) Artificial neural network computer tomography perfusion prediction of ischemic core. Stroke 50:1578–1581CrossRefPubMed
Metadata
Title
Post-processing of computed tomography perfusion in patients with acute cerebral ischemia: variability of inter-reader, inter-region of interest, inter-input model, and inter-software
Authors
Zhong-Ping Chen
Zhen-Zhen Shi
Yun-Geng Li
Yan Guo
Dan Tong
Publication date
01-12-2020
Publisher
Springer Berlin Heidelberg
Published in
European Radiology / Issue 12/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-020-07000-7

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