Defining Ischemic Core in Acute Ischemic Stroke Using CT Perfusion: A Multiparametric Bayesian-Based Model

Nael, Kambiz; Tadayon, Ehsan; Wheelwright, Danielle; Metry, A. D.; Fifi, Johanna T; Tuhrim, Stanley; Leacy, Reade De; Doshi, Amish; Chang, Helena L.; Mocco, J

doi:10.3174/ajnr.a6170

Cited by 15 publications

(8 citation statements)

References 42 publications

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“…To solve ( 1 ), an auxiliary variable Z was introduced into the blood flow scale residual function and the alternating direction multiplier method (ADMM) [ 17 ] was used, and the process could be expressed as follows:

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Section: Methodsmentioning

confidence: 99%

Early Diagnosis of Acute Ischemic Stroke by Brain Computed Tomography Perfusion Imaging Combined with Head and Neck Computed Tomography Angiography on Deep Learning Algorithm

Yang

Feng

et al. 2022

Contrast Media & Molecular Imaging

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The purpose of the research was to discuss the application values of deep learning algorithm-based computed tomography perfusion (CTP) imaging combined with head and neck computed tomography angiography (CTA) in the diagnosis of ultra-early acute ischemic stroke. Firstly, 88 patients with acute ischemic stroke were selected as the research objects and performed with cerebral CTP and CTA examinations. In order to improve the effect of image diagnosis, a new deconvolution network model AD-CNNnet based on deep learning was proposed and used in patient CTP image evaluation. The results showed that the peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) of the AD-CNNnet method were significantly higher than those of traditional methods, while the normalized mean square error (NMSE) was significantly lower than that of traditional algorithms ( P < 0.05 ). 80 cases were positive by CTP-CTA, including 16 cases of hyperacute ischemic stroke and 64 cases of acute ischemic stroke. The diagnostic sensitivity was 93.66%, and the specificity was 96.18%. The cerebral blood flow (CBF), cerebral blood volume (CBV), and the mean transit time (MTT) in the infarcted area were significantly greater than those in the corresponding healthy side area, and the time to peak (TTP) was significantly less than that in the corresponding healthy side area ( P < 0.05 ). The cerebral perfusion parameters CBF, TTP, and MTT in the penumbra were significantly different from those in the infarct central area and the corresponding contralateral area, and TTP was the most sensitive ( P < 0.05 ). To sum up, deep learning algorithm-based CTP combined with CTA could find the location of cerebral infarction lesions as early as possible to provide a reliable diagnostic result for the diagnosis of ultra-early acute ischemic stroke.

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…”

Section: Methodsmentioning

confidence: 99%

Early Diagnosis of Acute Ischemic Stroke by Brain Computed Tomography Perfusion Imaging Combined with Head and Neck Computed Tomography Angiography on Deep Learning Algorithm

Yang

Feng

et al. 2022

Contrast Media & Molecular Imaging

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“…The Bayesian‐based deconvolution method has been validated on a physiological model of microvasculature, and it was proven to perform better than any other SVD‐based deconvolution method available to date 5,10–12 . Although these results seem to be confirmed also in the clinical setting, current data are scanty 8,13,14 …”

Section: Discussionmentioning

confidence: 96%

“…5,[10][11][12] Although these results seem to be confirmed also in the clinical setting, current data are scanty. 8,13,14 Differently from oSVD, the Bayesian method estimates CTp data with a statistical-based approach that allows for an evaluation of the uncertainties of each perfusion parameter separately, thus eliminating potential interdependencies and adding robustness to the calculations.…”

Section: Discussionmentioning

confidence: 99%

CT perfusion extended window ischemic core estimation: Bayesian algorithm versus oscillation index singular value decomposition

Morelli

Immovilli

Giacopazzi

et al. 2023

Journal of Neuroimaging

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Background and Purpose Ischemic core estimation by CT perfusion (CTp) is a diagnostic challenge, mainly because of the intrinsic noise associated with perfusion data. However, an accurate and reliable quantification of the ischemic core is critical in the selection of patients for reperfusion therapies. Our study aimed at assessing the diagnostic accuracy of two different CTp postprocessing algorithms, that is, the Bayesian Method and the oscillation index singular value decomposition (oSVD). Methods All the consecutive stroke patients studied in the extended time window (>4.5 hours from stroke onset) by CTp and diffusion‐weighted imaging (DWI), between October 2019 and December 2021, were enrolled. The agreement between both algorithms and DWI was assessed by the Bland‐Altman plot, Wilcoxon signed‐rank test, Spearman's rank correlation coefficient, and the intraclass correlation coefficient (ICC). Results Twenty‐four patients were enrolled (average age: 72 ± 15 years). The average National Institutes of Health Stroke Scale was 14.42 ± 6.75, the median Alberta Stroke Program Early CT score was 8.50 (interquartile range [IQR] = 7.75‐9), and median time from stroke onset to neuroimaging was 7.5 hours (IQR = 6.5‐8). There was an excellent correlation between DWI and oSVD (ρ = .87, p‐value < .001) and DWI and Bayesian algorithm (ρ = .94, p‐value < .001). There was a stronger ICC between DWI and Bayesian algorithm (.97, 95% confidence interval [CI]: .92‐.99, p‐value < .001) than between DWI and oSVD (.59, 95% CI: .26‐.8, p‐value < .001). Discussion The agreement between Bayesian algorithm and DWI was greater than between oSVD and DWI in the extended window. The more accurate estimation of the ischemic core offered by the Bayesian algorithm may well play a critical role in the accurate selection of patients for reperfusion therapies.

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“…Immediately after CTP scan, target mismatch associated perfusion parameters, including volume of ischemic core (defined as the volume of the region with CBF <30% of contralateral hemisphere (21,22)) ischemic area (defined as the volume of the region with T max >6 s (7)), mismatch volume (V ischemic area – V ischemic core ), and mismatch ratio (V ischemic area / V ischemic core ) could be automatically obtained from RAPID software. In this study, target mismatch was defined according to the inclusion criteria of the Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke (DEFUSE 3) trial (ischemic core <70 mL, mismatch volume ≥15 mL, mismatch ratio ≥1.8) and the Extending the Time for Thrombolysis in Emergency Neurological Deficits-Intra-Arterial (EXTEND-IA) trial (ischemic core <70 mL, mismatch volume >10 mL, mismatch ratio >1.2) (1,2), respectively.…”

Section: Methodsmentioning

confidence: 99%

Use of ABC/2 method for rapidly estimating the target mismatch on computed tomography perfusion imaging in patients with acute ischemic stroke

Shen

Chu

et al. 2021

Acta Radiol

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Background Target mismatch (ischemic core, mismatch volume and mismatch ratio) in patients with acute ischemic stroke (AIS) highly relies on the automated perfusion analysis software. Purpose To evaluate the feasibility and accuracy of using the ABC/2 method to rapidly estimate the target mismatch on computed tomography perfusion (CTP) imaging in patients with AIS, using RAPID results as a reference. Material and Methods In total, 243 patients with anterior circulation AIS who underwent CTP imaging were retrospectively reviewed. Target mismatch associated perfusion parameters were derived from RAPID results and calculated using the ABC/2 method. Paired t-test was used to assess the difference of volumetric parameters between the two methods. The ability of using the ABC/2 method to predict the important cutoff volumetric metrics was also evaluated. Result There was no significant difference in the volumes of ischemic core ( P = 0.068), ischemic area ( P = 0.209), and mismatch volume ( P = 0.518) between ABC/2 and RAPID. Using RAPID results as reference, the ABC/2 method showed high accuracy for predicting perfusion parameters (70 mL and 90 mL: sensitivity=98.5% and 98.5%, specificity=100% and 100%, positive predictive value [PPV]=100% and 100%, negative predictive value [NPV]=93.8% and 92.9%; 10 mL and 15mL: sensitivity=99.6% and 99.5%, specificity=55.6% and 50.0%, PPV=96.6% and 94.8%, NPV=90.9% and 92.3%; 1.2 and 1.8: sensitivity=99.6% and 94.8%, specificity=75.0% and 96.9%, PPV=98.7% and 99.5%, NPV=90.0% and 73.8%). Conclusion The ABC/2 method may be a feasible alternative to RAPID for estimation of target mismatch parameters on CTP in patients with AIS.

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Defining Ischemic Core in Acute Ischemic Stroke Using CT Perfusion: A Multiparametric Bayesian-Based Model

Cited by 15 publications

References 42 publications

Early Diagnosis of Acute Ischemic Stroke by Brain Computed Tomography Perfusion Imaging Combined with Head and Neck Computed Tomography Angiography on Deep Learning Algorithm

Early Diagnosis of Acute Ischemic Stroke by Brain Computed Tomography Perfusion Imaging Combined with Head and Neck Computed Tomography Angiography on Deep Learning Algorithm

CT perfusion extended window ischemic core estimation: Bayesian algorithm versus oscillation index singular value decomposition

Use of ABC/2 method for rapidly estimating the target mismatch on computed tomography perfusion imaging in patients with acute ischemic stroke

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