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

Development and validation of a SCORing systEm for pre-thrombectomy diagnosis of IntraCranial Atherosclerotic Disease (Score-ICAD)

BACKGROUND

Early identification of intracranial atherosclerotic disease (ICAD) may impact the management of patients undergoing mechanical thrombectomy (MT). We sought to develop and validate a scoring system for pre-thrombectomy diagnosis of ICAD in anterior circulation large vessel/distal medium vessel occlusion strokes (LVOs/DMVOs).

METHODS

Retrospective analysis of two prospectively maintained comprehensive stroke center databases including patients with anterior circulation occlusions spanning 2010-22 (development cohort) and 2018-22 (validation cohort). ICAD cases were matched for age and sex (1:1) to non-ICAD controls.

RESULTS

Of 2870 MTs within the study period, 348 patients were included in the development cohort: 174 anterior circulation ICAD (6% of 2870 MTs) and 174 controls. Multivariable analysis β coefficients led to a 20 point scale: absence of atrial fibrillation (5); vascular risk factor burden (1) for each of hypertension, diabetes, smoking, and hyperlipidemia; multifocal single artery stenoses on CT angiography (3); absence of territorial cortical infarct (3); presence of borderzone infarct (3); or ipsilateral carotid siphon calcification (2). The validation cohort comprised 56 ICAD patients (4.1% of 1359 MTs): 56 controls. Area under the receiver operating characteristic curve was 0.88 (0.84-0.91) and 0.82 (0.73-0.89) in the development and validation cohorts, respectively. Calibration slope and intercept showed a good fit for the development cohort although with overestimated risk for the validation cohort. After intercept adjustment, the overestimation was corrected (intercept 0, 95% CI -0.5 to -0.5; slope 0.8, 95% CI 0.5 to 1.1). In the full cohort (n=414), ≥11 points showed the best performance for distinguishing ICAD from non-ICAD, with 0.71 (95% CI 0.65 to 0.78) sensitivity and 0.82 (95% CI 0.77 to 0.87) specificity, and 3.92 (95% CI 2.92 to 5.28) positive and 0.35 (95% CI 0.28 to 0.44) negative likelihood ratio. Scores ≥12 showed 90% specificity and 63% sensitivity.

CONCLUSION

The proposed scoring system for preprocedural diagnosis of ICAD LVOs and DMVOs presented satisfactory discrimination and calibration based on clinical and non-invasive radiological data.

Pretreatment parameters associated with hemorrhagic transformation among successfully recanalized medium vessel occlusions

Although pretreatment radiographic biomarkers are well established for hemorrhagic transformation (HT) following successful mechanical thrombectomy (MT) in large vessel occlusion (LVO) strokes, they are yet to be explored for medium vessel occlusion (MeVO) acute ischemic strokes. We aim to investigate pretreatment imaging biomarkers representative of collateral status, namely the hypoperfusion intensity ratio (HIR) and cerebral blood volume (CBV) index, and their association with HT in successfully recanalized MeVOs. A prospectively collected registry of acute ischemic stroke patients with MeVOs successfully recanalized with MT between 2019 and 2023 was retrospectively reviewed. A multivariate logistic regression for HT of any subtype was derived by combining significant univariate predictors into a forward stepwise regression with minimization of Akaike information criterion. Of 60 MeVO patients successfully recanalized with MT, HT occurred in 28.3% of patients. Independent factors for HT included: diabetes mellitus history (p = 0.0005), CBV index (p = 0.0071), and proximal versus distal occlusion location (p = 0.0062). A multivariate model with these factors had strong diagnostic performance for predicting HT (area under curve [AUC] 0.93, p < 0.001). Lower CBV indexes, distal occlusion location, and diabetes history are significantly associated with HT in MeVOs successfully recanalized with MT. Of note, HIR was not found to be significantly associated with HT.

Comparison of two computed tomography perfusion post-processing software to assess infarct volume in patients with acute ischemic stroke

Objectives

We used two automated software commonly employed in clinical practice-Olea Sphere (Olea) and Shukun-PerfusionGo (PerfusionGo)-to compare the diagnostic utility and volumetric agreement of computed tomography perfusion (CTP)-predicted final infarct volume (FIV) with true FIV in patients with anterior-circulation acute ischemic stroke (AIS).

Methods

In all, 122 patients with anterior-circulation AIS who met the inclusion and exclusion criteria were retrospectively enrolled and divided into two groups: intervention group (n = 52) and conservative group (n = 70), according to recanalization of blood vessels and clinical outcome (NIHSS) after different treatments. Patients in both groups underwent one-stop 4D-CT angiography (CTA)/CTP, and the raw CTP data were processed on a workstation using Olea and PerfusionGo post-processing software, to calculate and obtain the ischemic core (IC) and hypoperfusion (IC plus penumbra) volumes, hypoperfusion in the conservative group and IC in the intervention group were used to define the predicted FIV. The ITK-SNAP software was used to manually outline and measure true FIV on the follow-up non-enhanced CT or MRI-DWI images. Intraclass correlation coefficients (ICC), Bland-Altman, and Kappa analysis were used to compare the differences in IC and penumbra volumes calculated by the Olea and PerfusionGo software to investigate the relationship between their predicted FIV and true FIV.

Results

The IC and penumbra difference between Olea and PerfusionGo within the same group (p < 0.001) was statistically significant. Olea obtained larger IC and smaller penumbra than PerfusionGo. Both software partially overestimated the infarct volume, but Olea significantly overestimated it by a larger percentage. ICC analysis showed that Olea performed better than PerfusionGo (intervention-Olea: ICC 0.633, 95%CI 0.439-0.771; intervention-PerfusionGo: ICC 0.526, 95%CI 0.299-0.696; conservative-Olea: ICC 0.623, 95%CI 0.457-0.747; conservative-PerfusionGo: ICC 0.507, 95%CI 0.312-0.662). Olea and PerfusionGo had the same capacity in accurately diagnosing and classifying patients with infarct volume <70 ml.

Conclusion

Both software had differences in the evaluation of the IC and penumbra. Olea's predicted FIV was more closely correlated with the true FIV than PerfusionGo's prediction. Accurate assessment of infarction on CTP post-processing software remains challenging. Our results may have important practice implications for the clinical use of perfusion post-processing software.

A Bayesian estimation method for cerebral blood flow measurement by area-detector CT perfusion imaging

PURPOSE

Bayesian estimation with advanced noise reduction (BEANR) in CT perfusion (CTP) could deliver more reliable cerebral blood flow (CBF) measurements than the commonly used reformulated singular value decomposition (rSVD). We compared the efficacy of CBF measurement by CTP using BEANR and rSVD, evaluating both relative to N-isopropyl-p-[(123) I]- iodoamphetamine (¹²³I-IMP) single-photon emission computed tomography (SPECT) as a reference standard, in patients with cerebrovascular disease.

METHODS

Thirty-one patients with suspected cerebrovascular disease underwent both CTP on a 320 detector-row CT system and SPECT. We applied rSVD and BEANR in the ischemic and contralateral regions to create CBF maps and calculate CBF ratios from the ischemic side to the healthy contralateral side (CBF index). The analysis involved comparing the CBF index between CTP methods and SPECT using Pearson's correlation and limits of agreement determined with Bland-Altman analyses, before comparing the mean difference in the CBF index between each CTP method and SPECT using the Wilcoxon matched pairs signed-rank test.

RESULTS

The CBF indices of BEANR and ¹²³I-IMP SPECT were significantly and positively correlated (r = 0.55, p < 0.0001), but there was no significant correlation between the rSVD method and SPECT (r = 0.15, p > 0.05). BEANR produced smaller limits of agreement for CBF than rSVD. The mean difference in the CBF index between BEANR and SPECT differed significantly from that between rSVD and SPECT (p < 0.001).

CONCLUSIONS

BEANR has a better potential utility for CBF measurement in CTP than rSVD compared to SPECT in patients with cerebrovascular disease.

Correlation between the CT Perfusion Parameter Values and Response to Recanalization in Patients with Acute Ischemic Stroke

Objective

CT perfusion (CTP) provides various hemodynamic parameters. However, it is unclear which CTP parameters are useful in predicting clinical outcome in patients with acute ischemic stroke (AIS).

Methods

Between February 2019 and June 2021, patients with anterior circulation large-vessel occlusion who achieved successful recanalization within 8 hours after stroke onset were included. The relative CTP parameter values analyzed by the reformulated singular value decomposition (SVD) method in the affected middle cerebral artery territories compared to those in the unaffected side were calculated. In addition, the ischemic core volume (ICV) was evaluated using a Bayesian Vitrea. The final infarct volume (FIV) was assessed by 24-hour MRI. The correlation between these CTP-derived values and clinical outcome was assessed.

Results

Forty-two patients were analyzed. Among the CTP-related parameters, the ICV, relative cerebral blood volume (rCBV), and relative mean transit time (rMTT) showed a strong correlation with the FIV (ρ = 0.74, p <0.0001; ρ = -0.67, p <0.0001; and ρ = -0.66, p <0.0001, respectively). In multivariate analysis, rCBV, rMTT, and ICV were significantly associated with good functional outcome, which was defined as a modified Rankin Scale score ≤2 (OR, 6.87 [95% CI, 1.20-39.30], p = 0.0303; OR, 11.27 [95% CI, 0.97-130.94], p = 0.0269; and OR, 36.22 [95% CI, 2.78-471.18], p = 0.0061, respectively).

Conclusion

Among the CTP parameters analyzed by the SVD deconvolution algorithms, rCBV and rMTT could be useful imaging predictors of response to recanalization in patients with AIS, and the performances of these variables were similar to that of the ICV calculated by the Bayesian Vitrea.

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

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.

Probability maps classify ischemic stroke regions more accurately than CT perfusion summary maps

OBJECTIVES

To compare single parameter thresholding with multivariable probabilistic classification of ischemic stroke regions in the analysis of computed tomography perfusion (CTP) parameter maps.

METHODS

Patients were included from two multicenter trials and were divided into two groups based on their modified arterial occlusive lesion grade. CTP parameter maps were generated with three methods-a commercial method (ISP), block-circulant singular value decomposition (bSVD), and non-linear regression (NLR). Follow-up non-contrast CT defined the follow-up infarct region. Conventional thresholds for individual parameter maps were established with a receiver operating characteristic curve analysis. Probabilistic classification was carried out with a logistic regression model combining the available CTP parameters into a single probability.

RESULTS

A total of 225 CTP data sets were included, divided into a group of 166 patients with successful recanalization and 59 with persistent occlusion. The precision and recall of the CTP parameters were lower individually than when combined into a probability. The median difference [interquartile range] in mL between the estimated and follow-up infarct volume was 29/23/23 [52/50/52] (ISP/bSVD/NLR) for conventional thresholding and was 4/6/11 [31/25/30] (ISP/bSVD/NLR) for the probabilistic classification.

CONCLUSIONS

Multivariable probability maps outperform thresholded CTP parameter maps in estimating the infarct lesion as observed on follow-up non-contrast CT. A multivariable probabilistic approach may harmonize the classification of ischemic stroke regions.

KEY POINTS

• Combining CTP parameters with a logistic regression model increases the precision and recall in estimating ischemic stroke regions. • Volumes following from a probabilistic analysis predict follow-up infarct volumes better than volumes following from a threshold-based analysis. • A multivariable probabilistic approach may harmonize the classification of ischemic stroke regions.

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

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.