Comparison of a Bayesian estimation algorithm and singular value decomposition algorithms for 80-detector row CT perfusion in patients with acute ischemic stroke

• Published in: Radiologia medica Vol. 126; no. 6; pp. 795 - 803
• Main Authors: Ichikawa, Shota, Yamamoto, Hiroyuki, Morita, Takumi
• Format: Journal Article
• Language: English
• Published: Milan Springer Milan 01.06.2021; Springer Nature B.V
• Subjects: Acute Disease; Adult; Aged; Aged, 80 and over; Algorithms; Bayes Theorem; Bayesian analysis; Blood flow; Blood volume; Computed Tomography; Diagnostic Radiology; Female; Humans; Imaging; Interventional Radiology; Ischemic Stroke - diagnosis; Male; Medicine; Medicine & Public Health; Middle Aged; Multidetector Computed Tomography - methods; Neuroradiology; Radiology; Regression analysis; Retrospective Studies; Singular value decomposition; Statistical analysis; Stroke; Transit time; Ultrasound; Singular value decomposition algorithm; Infarction volume; Computed tomography perfusion; Acute ischemic stroke; Bayesian estimation algorithm
• ISSN: 0033-8362; 1826-6983; 1826-6983
• DOI: 10.1007/s11547-020-01316-6

Purpose A variety of postprocessing algorithms for CT perfusion are available, with substantial differences in terms of quantitative maps. Although potential advantages of a Bayesian estimation algorithm are suggested, direct comparison with other algorithms in clinical settings remains scarce. We aimed to compare performance of a Bayesian estimation algorithm and singular value decomposition (SVD) algorithms for the assessment of acute ischemic stroke using an 80-detector row CT perfusion. Methods CT perfusion data of 36 patients with acute ischemic stroke were analyzed using the Vitrea implemented a standard SVD algorithm, a reformulated SVD algorithm and a Bayesian estimation algorithm. Correlations and statistical differences between affected and contralateral sides of quantitative parameters (cerebral blood volume [CBV], cerebral blood flow [CBF], mean transit time [MTT], time to peak [TTP] and delay) were analyzed. Agreement of the CT perfusion-estimated and the follow-up diffusion-weighted imaging-derived infarct volume were evaluated by nonparametric Passing–Bablok regression analysis. Results CBF and MTT of the Bayesian estimation algorithm were substantially different and showed a better correlation with the standard SVD algorithm ( ρ  = 0.78 and 0.80, p  < 0.001) than with the reformulated SVD algorithm ( ρ  = 0.59 and 0.39, p  < 0.001). There is no significant difference in MTT only when using the reformulated SVD algorithm ( p  = 0.217). Regarding the regression lines, the slope and intercept were nearly ideal with the Bayesian estimation algorithm ( y  = 2.42 x- 6.51; ρ  = 0.60, p  < 0.001) in comparison with the SVD algorithms. Conclusions The Bayesian estimation algorithm can lead to a better performance compared with the SVD algorithms in the assessment of acute ischemic stroke because of better delineation of abnormal perfusion areas and accurate estimation of infarct volume.