CT Perfusion Data Research Articles

Deep Learning‐Based Prediction of Final Infarct Core from CT Perfusion Data: A Comparison to the Clinical Standard

Background In the management of acute ischemic stroke, computed tomography perfusion (CTP) is used to define the ischemic core and penumbra to estimate tissue fate after reperfusion therapy. The core/penumbra dichotomy uses single‐value thresholds, which potentially discards valuable data and oversimplifies the complexity of core and penumbral estimation. Advancing the dichotomous CTP output to a probability model has several advantages such as more sophisticated modeling of pathophysiology, supporting reader interpretation, and assessing a greater range of available data to estimate tissue fate. Methods In this retrospective study, we developed a CTP probability model to move away from single perfusion thresholds to estimate tissue fate. All patients from the International Stroke Perfusion Imaging Registry database had baseline CTP and were included in the current study if they had a large vessel occlusion that recanalized fully after thrombectomy and had follow‐up diffusion‐weighted imaging. Data were split into training, validation, and testing groups. Training and validation cohorts were used to develop a deep learning model in project MONAI (Medical Open Network for Artificial Intelligence) and performance metrics were derived from the testing set. Results In total, 243 patients were included in the study. The Attention U‐Net was the best performing deep learning model, producing the best prediction of follow‐up infarct core on the test set (n = 48): mean diverse counterfactual explanations score = 0.430±0.213, mean area under the curve = 0.765±0.095; better than the single‐value thresholding with a diverse counterfactual explanations scoreof 0.247±0.167 (paired t ‐test, P <0.0001) and area under the curveof 0.604±0.074 ( P <0.0001). Conclusion The deep learning probabilistic CTP model outperformed the current clinical standard, providing a more accurate core estimate than single‐threshold‐based measures.

Utility of automated CT perfusion software in acute ischemic stroke with large and medium vessel occlusion.

Early diagnosis of large vessel occlusion (LVO) in acute stroke often requires CT angiography (CTA). Automated CT perfusion (CTP) software, which identifies blood flow abnormalities, enhances LVO diagnosis and patient selection for endovascular thrombectomy (EVT). This study evaluates the sensitivity of automated CTP images in detecting perfusion abnormalities in patients with acute ischemic stroke (AIS) and LVO or medium vessel occlusion (MeVO), compared to CTA. We screened acute ischemic stroke patients presenting within 24 h who underwent CT, CTA, and CTP as per institutional protocol. RAPID AI software processed CTP images, while neuroradiologists reviewed CTA for intracranial arterial occlusions. Sensitivity, specificity, and accuracy of automated CTP maps in detecting occlusions were assessed. Of 790 screened patients, 31 were excluded due to lack of RAPID CTP data or poor-quality scans, leaving 759 for analysis. The median age was 71 years (IQR: 61-81), with 47% female. Among them, 678 had AIS, and 81 had AIS ruled out. CTA identified arterial occlusion in 562 patients (74%), with corresponding CTP abnormalities in 537 patients (Tmax > 6 sec). In the 197 without occlusion, CTP was negative in 161. Automated CTP maps had a sensitivity of 95.55% (CI 95: 93.50-97.10%), specificity of 81.73% (CI 95: 75.61-86.86%), negative predictive value of 98.22% (CI 95: 97.39-98.79%), positive predictive value of 63.54% (CI 95: 56.46-70.09%), and overall accuracy of 85.18% (CI 95: 82.45-87.64%). Automated CTP maps demonstrated high sensitivity and negative predictive value for LVOs and MeVOs, suggesting their usefulness as a rapid diagnostic tool, especially in settings without expert neuroradiologists.

Two-dimensional speckle tracking echocardiography and stress computed tomography myocardial perfusion: potential for early imaging of asymptomatic patients with myocardial bridges

Aim. To evaluate the effectiveness of left ventricular (LV) peak global longitudinal strain and potential of computed tomography myocardial perfusion (CT perfusion) for the early diagnosis of asymptomatic patients with myocardial bridges (MBs).Material and methods. Forty patients were examined (20 with asymptomatic MBs in the area of the left anterior descending (LAD) artery and 20 without MBs) at the S. M. Kirov Military Medical Academy in the period from 2021 to 2023. The patients were divided into 2 equal groups consisting of 20 people (10 with MBs — the main group; 10 without MBs — the comparison group), one of which underwent speckle tracking echocardiography (STE), and the other — stress CT perfusion performed (adenosine triphosphate 160 mg/kg). The results were assessed in accordance with the 17-segment classification of LV proposed by the American Heart Association (2002).Results. When performing STE, 5 (50%) patients with MBs showed a decrease in LV local strain in the segments, the blood supply of which comes from the LAD artery and its branches, while in the comparison group — 1 (10%) (x2=5; p=0,025). When assessing the global longitudinal peak strain (GLPS), a significant difference was revealed among the study groups, amounting to -20,9±1,5% in patients with MBs and -22,1±0,9% in those without MBs (p=0,04). Using ROC analysis, the effectiveness of GLPS in prediction of asymptomatic MBs was calculated as follows: AUC=0,93 (95% confidence interval: 0,819-1,0; p=0,001), the cut-off point — -20,55% (sensitivity 80%, specificity 100%). According to CT perfusion data, impaired myocardial blood supply in the group of asymptomatic patients with MBs was noted in 6 (60%) cases, and in the comparison group — 2 (20%) (x2=9,8; p=0,002).Conclusion. Imaging of initial LV changes according to two-dimensional STE and detection of coronary circulatory disorders according to CT myocardial perfusion in asymptomatic patients with MBs makes it possible to timely suspect intramyocardial course of the coronary artery, as well as to consider further management tactics and carry out early prevention of possible cardiovascular events.

Contrast enhancement boost improves the image quality of CT angiography derived from 80-kVp cerebral CT perfusion data

Rationale and objectiveTo investigate the impact of the contrast enhancement boost (CE-boost) technique on the image quality of CT angiography (CTA) derived from 80-kVp cerebral CT perfusion (CTP) data, and to compare it with conventional CTApeak as well as other currently employed methods for enhancing CTA images, such as CTAtMIP and CTAtAve extracted from CTP.Materials and methodsThe data of forty-seven patients who underwent CTP at 80 kVp were retrospectively collected. Four sets of images: CTApeak, CTAtMIP, CTAtAve, and CE-boost images. The CTApeak image represents the arterial phase at its peak value, captured as a single time point. CTAtMIP and CTAtAve are 4D CTA images that provide maximum density projection and average images from the three most prominent time points. CE-boost is a postprocessing technique used to enhance contrast in the arterial phase at its peak value. We compared the average CT value, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of the internal carotid artery (ICA) and basilar artery (BA) among the four groups. Image quality was evaluated using a 5-point scale.ResultsThe CE-boost demonstrated and CNR in the ICA and BA (all p < 0.001). Compared with the other three CTA reconstructed images, the CE-boost images had the best subjective image quality, with the highest scores of 4.77 ± 0.43 and 4.87 ± 0.34 for each reader (all p < 0.001).ConclusionCompared with other currently used techniques,CE-boost enhances the image quality of CTA derived from 80-kVp CTP data, leading to improved visualization of intracranial arteries.

Combining Early Ischemic Change and Collateral Extent for Functional Outcomes After Endovascular Therapy: An Analysis From AcT Trial.

Early ischemic change and collateral extent are colinear with ischemic core volume (ICV). We investigated the relationship between a combined score using the Alberta Stroke Program Early Computed Tomography Score and multiphase computed tomography angiography (mCTA) collateral extent, named mCTA-ACE score, on functional outcomes in endovascular therapy-treated patients. We performed a post hoc analysis of a subset of endovascular therapy-treated patients from the Alteplase Compared to Tenecteplase trial which was conducted between December 2019 and January 2022 at 22 centers across Canada. Ten-point mCTA collateral corresponding to M2 to M6 regions of the Alberta Stroke Program Early Computed Tomography Score grid was evaluated as 0 (poor), 1 (moderate), or 2 (normal) and additively combined with the 10-point Alberta Stroke Program Early Computed Tomography Score to produce a 20-point mCTA-ACE score. We investigated the association of mCTA-ACE score with modified Rankin Scale score ≤2 and return to prestroke level of function at 90 to 120 days using mixed-effects logistic regression. In the subset of patients who underwent baseline computed tomography perfusion imaging, we compared the mCTA-ACE score and ICV for outcome prediction. Among 1577 intention-to-treat population in the trial, 368 (23%; 179 men; median age, 73 years) were included, with Alberta Stroke Program Early Computed Tomography Score, mCTA collateral, and combination of both (mCTA-ACE score: median [interquartile range], 8 [7-10], 9 [8-10], and 17 [16-19], respectively). The probability of modified Rankin Scale score ≤2 and return to prestroke level of function increased for each 1-point increase in mCTA-ACE score (odds ratio, 1.16 [95% CI, 1.06-1.28] and 1.22 [95% CI, 1.06-1.40], respectively). Among 173 patients in whom computed tomography perfusion data was assessable, the mCTA-ACE score was inversely correlated with ICV (ρ=-0.46; P<0.01). The mCTA-ACE score was comparable to ICV to predict a modified Rankin Scale score ≤2 and return to prestroke level of function (C statistics 0.71 versus 0.69 and 0.68 versus 0.64, respectively). The mCTA-ACE score had a significant positive association with functional outcomes after endovascular therapy and had a similar predictive performance as ICV.

Multicenter comparison using two AI stroke CT perfusion software packages for determining thrombectomy eligibility

BackgroundStroke AI platforms assess infarcted core and potentially salvageable tissue (penumbra) to identify patients suitable for mechanical thrombectomy. Few studies have compared outputs of these platforms, and none have been multicenter or considered NIHSS or scanner/protocol differences. Our objective was to compare volume estimates and thrombectomy eligibility from two widely used CT perfusion (CTP) packages, Viz.ai and RAPID.AI, in a large multicenter cohort. MethodsWe analyzed CTP data of acute stroke patients with large vessel occlusion (LVO) from four institutions. Core and penumbra volumes were estimated by each software and DEFUSE-3 thrombectomy eligibility assessed. Results between software packages were compared and categorized by NIHSS score, scanner manufacturer/model, and institution. ResultsPrimary analysis of 362 cases found statistically significant differences in both software's volume estimations, with subgroup analysis showing these differences were driven by results from a single scanner model, the Canon Aquilion One. Viz.ai provided larger estimates with mean differences of 8cc and 18cc for core and penumbra, respectively (p<0.001). NIHSS subgroup analysis also showed systematically larger Viz.ai volumes (p<0.001). Despite volume differences, a significant difference in thrombectomy eligibility was not found. Additional subgroup analysis showed significant differences in penumbra volume for the Phillips Ingenuity scanner, and thrombectomy eligibility for the Canon Aquilion One scanner at one center (7 % increased eligibility with Viz.ai, p=0.03). ConclusionsDespite systematic differences in core and penumbra volume estimates between Viz.ai and RAPID.AI, DEFUSE-3 eligibility was not statistically different in primary or NIHSS subgroup analysis. A DEFUSE-3 eligibility difference, however, was seen on one scanner at one institution, suggesting scanner model and local CTP protocols can influence performance and cause discrepancies in thrombectomy eligibility. We thus recommend centers discuss optimal scanning protocols with software vendors and scanner manufacturers to maximize CTP accuracy.

Analysis of multimodal fusion strategies in deep learning for ischemic stroke lesion segmentation on computed tomography perfusion data

Analysis of multimodal fusion strategies in deep learning for ischemic stroke lesion segmentation on computed tomography perfusion data

Elevated cortical blood flow insufficiency volume as a predictor of adverse outcomes in aneurysmal subarachnoid hemorrhage: a large prospective quantitative computed tomography perfusion study

PurposeEarly hypoperfusion changes exist in patients with aneurysmal subarachnoid hemorrhage (aSAH). We aimed to investigate a readily obtainable quantitative computed tomography perfusion (CTP) parameter that could assist in quickly identifying patients at risk of delayed cerebral ischemia (DCI) and poor 90-day functional outcomes on admission. MethodsWe prospectively collected data between 2021.04 and 2022.12. Preoperative CTP data were post-processed using RAPID software. The cortical blood flow insufficiency (CBFI) was defined as Time-to-maximum > 4.0 s. Patients were categorized into four groups according to CBFI volume distribution. To minimize differences among the groups, we employed stabilized inverse probability of treatment weighting (sIPTW). The primary outcome was DCI and poor 90-day functional outcomes (modified Rankin Scale, 3–6) was the secondary outcome. Multivariable Cox or Logistic analysis were performed to estimate the association between CBFI volume and the study outcomes, both before and after sIPTW. ResultsAt baseline, the mean (SD) age of the 493 participants was 55.0 (11.8) years, and 299 (60.6%) were female. One hundred and seven participants with DCI and eighty-six participants with poor 90-day functional outcomes were identified. After sIPTW, CBFI volume demonstrated a significant association with DCI (Cox regression: Group 4 versus Group 1, HR 3.69, 95% CI 1.84–7.01) and poor 90-day functional outcomes (Logistic regression: Group 4 versus Group 1, OR 4.61, 95% CI 2.01–12.50). ConclusionIn this study, an elevated preoperative CBFI volume was associated with adverse outcomes in aSAH patients. More well-designed studies are needed to confirm this association.

Large vessel occlusive stroke with milder baseline severity shows better collaterals and reduced harm from thrombectomy transfer delays.

Patients with large vessel occlusion (LVO) stroke presenting with milder baseline clinical severity are common and require endovascular thrombectomy. However, such patients are difficult to recognize using pre-hospital severity-based triage tools and therefore are likely to require a secondary inter-hospital transfer if transported to a non-thrombectomy center. Given the potential for milder severity to represent better underlying cerebrovascular collateral circulation, it is unknown whether transfer delays are still associated with poorer post-stroke outcomes in this patient group. We primarily aimed to examine whether the harmful effect of inter-hospital transfer delay for thrombectomy was different for LVO patients with mild or severe deficits. Secondarily, we also investigated whether imaging markers of collateral circulation were different between severity groups. Registry data from two large Australian thrombectomy centers were used to identify all directly presenting and secondarily transferred LVO patients undergoing thrombectomy, divided into those with lower (NIHSS < 10) and higher (NIHSS ⩾ 10) baseline deficits. The primary outcome was the functional independence or return to baseline defined as modified Rankin Scale 0-2 or baseline at 90 days. Patients with complete baseline CT-perfusion data were analyzed for imaging markers of collateral circulation by baseline severity group. A total of 1210 LVO patients undergoing thrombectomy were included, of which 273 (22.6%) had lower baseline severity. Despite similar thrombolysis and recanalization rates, transferred patients had lower odds of achieving the primary outcome compared to the primary presentation to a thrombectomy center, where baseline severity was higher (adjusted odds ratio (aOR) 0.759 (95% CI 0.576-0.999)), but not when severity was lower (aOR 1.357 (95% CI 0.764-2.409), p-interaction = 0.122). In the imaging analysis of 436 patients, those with milder severity showed smaller median ischemic core volumes (12.6 (IQR 0.0-17.9) vs 27.5 (IQR 6.5-37.1) mL, p < 0.001)), higher median perfusion mismatch ratio (10.8 (IQR 4.8-54.5) vs 6.6 (IQR 3.5-16.5), p < 0.001), and lower median hypoperfusion intensity ratio (0.25 (IQR 0.18-0.38) vs 0.40 (IQR 0.22-0.57), p < 0.001). Patients receiving secondary inter-hospital transfer for thrombectomy had poorer outcomes compared to those presenting directly to a thrombectomy center if baseline deficits were severe, but this difference was not observed when baseline deficits were milder. This result may potentially be due to our secondary findings of significantly improved collateral circulation markers in lower-severity LVO patients. As such, failure of pre-hospital screening tools to detect lower-severity LVO patients for pre-hospital bypass to a thrombectomy center may not necessarily deleteriously affect outcome. Anonymized data not published within this article will be made available on request from any qualified investigator.

Predictive value of whole brain perfusion on admission for shunt-dependent hydrocephalus fllowing aneurysmal subarachnoid hemorrhage

Objective: To explore the value of predicting shunt-dependent hydrocephalus (SDHC) in patients with aneurysmal subarachnoid hemorrhage (aSAH) based on whole brain CT perfusion(CTP) and clinical data within 24 hours at admission. Methods: The clinical and imaging data of aSAH patients who received interventional embolization in our hospital were retrospectively collected from March 2018 to August 2022. All patients underwent one-stop whole brain CT examination within 24 hours after symptom onset, and the qualitative and quantitative CTP parameters were obtained after post-processing. Follow-up was conducted once every 2 months by consulting electronic medical records or by telephone for 6 months. According to whether SDHC occurred or not, the patients were divided into SDHC group and non-SDHC group. The differences between the two groups were compared. Logistic regression model was used to analyze and determine the predictive factors of SDHC, and the SDHC predictive model was established. The effectiveness of the predictive model was evaluated by drawing the receiver operating characteristic (ROC) curve of the subjects. Results: A total of 414 patients were included, including 132 males and 282 females, aged (59±11) years. 17.6%(73/414) patients had SDHC. There were significant differences in the occurrence of acute hydrocephalus, the World Neurosurgical League Scale (WFNS), the Hunt-Hess scale, the modified Fisher score (mFS), and the qualitative and quantitative parameters of CTP between the two groups (both P<0.001). Multivariate logistic regression analysis showed that acute hydrocephalus (OR=8.621, 95%CI: 4.237-17.542),old age (OR=1.107, 95%CI: 1.068-1.148), high mFS and high Hunt-Hess classification (OR=3.740, 95%CI: 1.352-10.342) were the risk factors of SDHC in aSAH patients, and high mean cerebral blood flow (mCBF) (OR=0.931, 95%CI: 0.885-0.980) was a protective factor of SDHC.The area under ROC curve (AUC) of the prediction model constructed by these five variables was 0.923(95%CI: 0.89-0.95), with 84.5% sensitivity and 87.7% specificity. Conclusion: The mCBF and acute hydrocephalus, age, mFS and Hunt-Hess classification within 24 hours at admission can be used to predict SDHC for aSAH patients.

Spatio-temporal physics-informed learning: A novel approach to CT perfusion analysis in acute ischemic stroke.

CT perfusion imaging is important in the imaging workup of acute ischemic stroke for evaluating affected cerebral tissue. CT perfusion analysis software produces cerebral perfusion maps from commonly noisy spatio-temporal CT perfusion data. High levels of noise can influence the results of CT perfusion analysis, necessitating software tuning. This work proposes a novel approach for CT perfusion analysis that uses physics-informed learning, an optimization framework that is robust to noise. In particular, we propose SPPINN: Spatio-temporal Perfusion Physics-Informed Neural Network and research spatio-temporal physics-informed learning. SPPINN learns implicit neural representations of contrast attenuation in CT perfusion scans using the spatio-temporal coordinates of the data and employs these representations to estimate a continuous representation of the cerebral perfusion parameters. We validate the approach on simulated data to quantify perfusion parameter estimation performance. Furthermore, we apply the method to in-house patient data and the public Ischemic Stroke Lesion Segmentation 2018 benchmark data to assess the correspondence between the perfusion maps and reference standard infarct core segmentations. Our method achieves accurate perfusion parameter estimates even with high noise levels and differentiates healthy tissue from infarcted tissue. Moreover, SPPINN perfusion maps accurately correspond with reference standard infarct core segmentations. Hence, we show that using spatio-temporal physics-informed learning for cerebral perfusion estimation is accurate, even in noisy CT perfusion data. The code for this work is available at https://github.com/lucasdevries/SPPINN.

Predicting stroke outcome: A case for multimodal deep learning methods with tabular and CT Perfusion data

Motivation:Acute ischemic stroke is one of the leading causes of morbidity and disability worldwide, often followed by a long rehabilitation period. To improve and personalize stroke rehabilitation, it is essential to provide a reliable prognosis to caregivers and patients. Deep learning techniques might improve the predictions by incorporating different data modalities. We present a multimodal approach to predict the functional status of acute ischemic stroke patients after their discharge based on tabular data and CT perfusion imaging. Methods:We conducted experiments on tabular, imaging, and multimodal deep learning architectures to predict dichotomized mRS scores 3 months after the event. The dataset was collected from a Dutch hospital and includes 98 CVA patients with a visible occlusion on their CT perfusion scan. Tabular data is based on the Dutch Acute Stroke Audit data, and imaging data consists of summed-up CT perfusion maps. Results:On the tabular data, TabNet outperformed our baselines with an AUC of 0.71, while ResNet-10 on the imaging data performed comparably with an AUC of 0.70. Our implementation of the multimodal DAFT architecture outperforms baselines as well as comparable studies by achieving an 0.75 AUC, and 0.80 F1 score. This was achieved with a final model of less than a hundred thousand optimizable parameters, and a dataset less than half the size of reference papers. Conclusion:Overall, we demonstrate the feasibility of predicting the functional outcome for ischemic stroke patients and the usability of multimodal deep learning architectures for this purpose.

The image quality and diagnostic performance of CT perfusion-derived CT angiography versus that of conventional CT angiography.

The combination of computed tomography angiography (CTA) and computed tomography perfusion (CTP) evaluation of cerebral perfusion status and vascular conditions can improve the diagnostic accuracy of infarction, ischemia, and vascular occlusion in stroke patients, as well as a comprehensive assessment of cerebral edema, collateral circulation, and blood perfusion in the lesion area. However, the consequent radiation safety and contrast agent nephropathy have aroused increasing concern. The purpose of this study was to assess the image quality and diagnostic accuracy of CTA images derived from CTP data, and to explore the feasibility of replacing conventional CTA. A total of 31 consecutive patients with suspected acute ischemic stroke were retrospectively analyzed. All patients underwent head and neck CTA and brain CTP examinations. All the CTP images were transmitted to the ShuKun artificial intelligence system, which reconstructs CTA derived from CTP (CTA-DF-CTP). The images were divided into 2 groups, including CTA-DF-CTP (Group A) and conventional CTA (Group B). The CT attenuation values, subjective image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), image quality, CT volume dose index (CTDIvol), dose length product (DLP), and effective radiation dose (ED) were compared between the 2 groups. Moreover, the consistency of vascular stenosis and stenosis degree between the 2 groups were measured and evaluated. There were no significant differences in image noise, SNR, or CNR between Groups A and B (P>0.05). The CT attenuation values of the arteries were higher in Group A than in B [internal carotid artery (ICA) =548±112 vs. 454±85 Hounsfield units (HU), middle cerebral artery (MCA) =453±118 vs. 388±70 HU, and basilar artery (BA) =431±99 vs. 360±83 HU] (P<0.01). The image quality of the 2 groups met the requirement of clinical diagnosis (4.97±0.18 vs. 4.94±0.25). No significant difference was found in subjective evaluation (P>0.05). In Group A compared with Group B, the following reductions were observed: CTDIvol (10.7%; 100.8 vs. 112.9 mGy), DLP (23.0%; 1,613±0 vs. 2,093±88 mGy·cm), and ED (23.0%; 5.00±0.00 vs. 6.49±0.27 mSv). CTA-DF-CTP data provide diagnostic accuracy and image quality similar to those of conventional CTA of head and neck CTA.

Deep Learning Reconstruction Improves the Image Quality of CT Angiography Derived From 80-kVp Cerebral CT Perfusion Data

To investigate the impact of the deep learning reconstruction (DLR) technique on the image quality of CT angiography (CTA) derived from 80-kVp cerebral CT perfusion (CTP) data and compare it with hybrid-iterative reconstruction (HIR). Thirty-three patients underwent CTP at 80 kVp were prospectively enrolled. CTP data were reconstructed with HIR and DLR. Four image datasets were reconstructed: HIRpeak and DLRpeak were single arterial phase images derived from the time point showing the peak value, HIRtMIP and HIRtAve were time-resolved maximum intensity projection image and time-resolved average image derived from three time points with the greatest enhancement of HIR. The mean CT values, standard deviation, signal-to-noise ratio, and contrast-to-noise ratio of the internal carotid artery and basilar artery were compared among the four image dataset. Image quality was performed using a five-point rating scale. Arterial stenosis was evaluated. DLRpeak had the highest CT value and contrast-to-noise ratio in the internal carotid artery and basilar artery (all p < 0.001). DLRpeak showed the best subjective image quality and had the highest score (4.93 ± 0.4) compared to the other three HIR CTA images (all p < 0.001). The degree of vascular stenosis was consistent among the four evaluated sequences (HIRtAve, HIRpeak, and HIRtMIP DLRpeak). For CTA derived from 80-kVp cerebral CTP data, images reconstructed with deep learning showed better image quality and improved intracranial artery visualization than those processed with HIR and other currently used techniques.

Multiphase computed tomography angiography derived from computed tomography perfusion data for the differential diagnosis of intracranial aneurysm and infundibular dilation.

As infundibular dilation (ID) is less likely to cause hemorrhage or other clinical sequelae than an intracranial aneurysm (IA) and treating infundibulum itself may put the patient at unnecessary risk for stroke, it is important to distinguish between the ID and IA. Given the limitations of conventional single-phase computed tomography angiography (sCTA) to show small branches of intracranial arteries, the application of multiphase computed tomography angiography (mCTA) for identification seems promising. Our main objective was to evaluate whether using mCTA derived from computed tomography perfusion (CTP) data can improve distinction between IA and ID. A total of 35 patients diagnosed with IA or ID of the posterior communicating artery at its junction with the internal carotid artery junction (ICA-PComA) by sCTA at the 8th Medical Center of Chinese PLA General Hospital between January 2019 and May 2022 were retrospectively selected. All patients underwent CTP. The simulated mCTA was reconstructed from 0.75 mm CTP data for assessment of vascular branches. All data were processed separately by 2 CTA post-processors; 2 observers diagnosed IA and ID by source and volume rendering (VR) images of sCTA and VR images of mCTA, and compared the diagnostic efficacy of source and VR images of sCTA with VR images of mCTA. The quality of the reconstructed images was more consistent between the 2 post-processors mCTA (K=0.856) than sCTA (K=0.648). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the source image for ID identification were 78.9%, 86.7%, 84.2%, 81.3%, and 80.0% for sCTA, 73.7%, 81.2%, 82.3%, 72.2%, and 77.2% for the VR image of sCTA, and 94.7%, 87.5%, 90.0%, 93.3%, and 91.4% for the VR image based on mCTA, respectively. The net reclassification index (NRI) of mCTA for VR and the source image of sCTA was 0.273 and 0.220, respectively. VR base on mCTA was on average better than VR and the source image of sCTA at differentiating ID from IA (P=0.005 and P=0.001, respectively). Compared to sCTA, mCTA is more helpful in improving the distinction of ID and IA.

Potential Approach to Quantifying the Volume of the Ischemic Core in Truncated Computed Tomography Perfusion Scans: A Preliminary Study.

This study aimed to provide an alternative approach for quantifying the volume of the ischemic core (IC) if truncation of computed tomography perfusion (CTP) occurs in clinical practice. Baseline CTP and follow-up diffusion-weighted imaging (DWI) data from 88 patients with stroke were retrospectively collected. CTP source images (CTPSI) from the unenhanced phase to the peak arterial phase (CTPSI-A) or the peak venous phase (CTPSI-V) were collected to simulate the truncation of CTP in the arterial or venous phases, respectively. The volume of IC on CTPSI-A (V CTPSI-A ) or CTPSI-V (V CTPSI-V ) was defined as the volume of the brain tissue with >65% reduction in attenuation compared with that of the normal tissue. The volume of IC on the baseline CTP (V CTP ) was defined as a relative cerebral blood flow of <30% of that in the normal tissue. The volume of the posttreatment infarct on the follow-up DWI (V DWI ) image was manually delineated and calculated. One-way analysis of variance, Bland-Altman plots, and Spearman correlation analyses were used for the statistical analysis. V CTPSI-A was significantly higher than V DWI ( P < 0.001); however, no significant difference was observed between V CTP and V DWI ( P = 0.073) or between V CTPSI-V and V DWI ( P > 0.999). The mean differences between V DWI and V CTPSI-V , V DWI and V CTP , and V DWI and V CTPSI-A were 1.70 mL (limits of agreement [LoA], -56.40 to 59.70), 8.30 mL (LoA, -40.70 to 57.30), and -68.10 mL (LoA, -180.90 to 44.70), respectively. Significant correlations were observed between V DWI and V CTP ( r = 0.68, P < 0.001) and between V DWI and V CTPSI-V ( r = 0.39, P < 0.001); however, no significant correlation was observed between V DWI and V CTPSI-A ( r = 0.20, P = 0.068). V CTPSI-V may be a promising method for quantifying the volume of the IC if truncation of CTP occurs.

Synchronous Superficial Middle Cerebral Vein Outflow Correlates Favorable Tissue Fate After Mechanical Thrombectomy for Acute Ischemic Stroke

The purpose of this study was to determine the association between hemispheric synchrony in venous outflow at baseline and tissue fate after mechanical thrombectomy (MT) for acute ischemic stroke (AIS). A two-center retrospective analysis involving AIS patients who underwent MT was performed. The four cortical veins of interest include the superficial middle cerebral vein (SMCV), sphenoparietal sinus (SS), vein of Labbé (VOL), and vein of Trolard (VOT). Baseline computed tomography perfusion data were used to compare the following outflow parameters between the hemispheres: first filling time (△FFT), time to peak (△TTP) and total filling time (△TFT). Synchronous venous outflow was defined as △FFT=0. Multivariable regression analyses were performed to evaluate the association of venous outflow synchrony with penumbral salvage, infarct growth, and intracranial hemorrhage (ICH) after MT. A total of 151 patients (71.4±13.2 years, 65.6% women) were evaluated. Patients with synchronous SMCV outflow demonstrated significantly greater penumbral salvage (41.3 mL vs. 33.1 mL, P=0.005) and lower infarct growth (9.0 mL vs. 14.4 mL, P=0.015) compared to those with delayed SMCV outflow. Higher △FFTSMCV (β=-1.44, P=0.013) and △TTPSMCV (β=-0.996, P=0.003) significantly associated with lower penumbral salvage, while higher △FFTSMCV significantly associated with larger infarct growth (β=1.09, P=0.005) and increased risk of ICH (odds ratio [OR]=1.519, P=0.047). Synchronous SMCV outflow is an independent predictor of favorable tissue outcome and low ICH risk, and thereby carries the potential as an auxiliary radiological marker aiding the treatment planning of AIS patients.

Pretreatment brain CT perfusion thresholds for predicting final infarct volume in distal medium vessel occlusions.

Quantitative CT perfusion (CTP) thresholds for assessing the extent of ischemia in patients with acute ischemic stroke (AIS) have been established; relative cerebral blood flow (rCBF) <30% is typically used for estimating estimated ischemic core volume and Tmax (time to maximum) >6seconds for critical hypoperfused volume in AIS patients with large vessel occlusion (LVO). In this study, we aimed to identify the optimal threshold values for patients presenting with AIS secondary to distal medium vessel occlusions (DMVOs). In this retrospective study, consecutive AIS patients with anterior circulation DMVO who underwent pretreatment CTP and follow-up MRI/CT were included. The CTP data were processed by RAPID (iSchemaView, Menlo Park, CA) to generate estimated ischemic core volumes using rCBF <20%, <30%, <34%, and <38% and critical hypoperfused volumes using Tmax (seconds) >4, >6, >8, and >10. Final infarct volumes (FIVs) were obtained from follow-up MRI/CT within 5days of symptom onset. Diagnostic performance between CTP thresholds and FIV was assessed in the successfully and unsuccessfully recanalized groups. Fifty-five patients met our inclusion criteria (32 female [58.2%], 68.0±12.1years old [mean±SD]). Recanalization was attempted with intravenous tissue-type plasminogen activator and mechanical thrombectomy in 27.7% and 38.1% of patients, respectively. Twenty-five patients (45.4%) were successfully recanalized. In the successfully recanalized patients, no CTP threshold significantly outperformed what is used in LVO setting (rCBF<30%). All rCBF CTP thresholds demonstrated fair diagnostic performances for predicting FIV. In unsuccessfully recanalized patients, all Tmax CTP thresholds strongly predicted FIV with relative superiority of Tmax >10seconds (area under the receiver operating characteristic curve=.875, p=.001). In AIS patients with DMVOs, longer Tmax delays than Tmax >6seconds, most notably, Tmax >10seconds, best predict FIV in unsuccessfully recanalized patients. No CTP threshold reliably predicts FIV in the successfully recanalized group nor significantly outperformed rCBF<30%.

Assessing the Relationship between LAMS and CT Perfusion Parameters in Acute Ischemic Stroke Secondary to Large Vessel Occlusion.

The Los Angeles Motor Scale (LAMS) is a rapid pre-hospital scale used to predict stroke severity which has also been shown to accurately predict large vessel occlusions (LVOs). However, to date there is no study exploring whether LAMS correlates with the computed tomography perfusion (CTP) parameters in LVOs. Patients with LVO between September 2019 and October 2021 were retrospectively reviewed and included if the CTP data and admission neurologic exams were available. The LAMS was documented based on emergency personnel exams or scored retrospectively using an admission neurologic exam. The CTP data was processed by RAPID (IschemaView, Menlo Park, CA, USA) with an ischemic core volume (relative cerebral blood flow [rCBF] < 30%), time-to-maximum (Tmax) volume (Tmax > 6 s delay), hypoperfusion index (HI), and cerebral blood volume (CBV) index. Spearman's correlations were performed between the LAMS and CTP parameters. A total of 85 patients were included, of which there were 9 intracranial internal carotid artery (ICA), 53 proximal M1 branch middle cerebral artery M1, and 23 proximal M2 branch occlusions. Overall, 26 patients had LAMS 0-3, and 59 had LAMS 4-5. In total, LAMS positively correlated with CBF < 30% (Correlation Coefficient (CC): 0.32, p < 0.01), Tmax > 6 s (CC:0.23, p < 0.04), HI (CC:0.27, p < 0.01), and negatively correlated with the CBV index (CC:-0.24, p < 0.05). The relationships between LAMS and CBF were < 30% and the HI was more pronounced in M1 occlusions (CC:0.42, p < 0.01; 0.34, p < 0.01 respectively) and proximal M2 occlusions (CC:0.53, p < 0.01; 0.48, p < 0.03 respectively). The LAMS also correlated with a Tmax > 6 s in M1 occlusions (CC:0.42, p < 0.01), and negatively correlated with the CBV index in M2 occlusions (CC:-0.69, p < 0.01). There were no significant correlations between the LAMS and intracranial ICA occlusions. The results of our preliminary study indicate that the LAMS is positively correlated with the estimated ischemic core, perfusion deficit, and HI, and negatively correlated with the CBV index in patients with anterior circulation LVO, with stronger relationships in the M1 and M2 occlusions. This is the first study showing that the LAMS may be correlated with the collateral status and estimated ischemic core in patients with LVO.

System-wide implementation of Computed Tomography Perfusion (CTP) Imaging for Acute Stroke: Impact on Quality, Safety and Outcomes (P7-5.009)

<h3>Objective:</h3> This study aims to assess the impact of CTP acquisition in regional hospitals on quality metrics, functional and safety outcomes in patients with LVO. <h3>Background:</h3> The utility of CTP imaging in spoke hospitals to determine eligibility for MT remains controversial. <h3>Design/Methods:</h3> Consecutive adult patients treated for LVO after initially presenting to regional spokes between Jan 2021 to Dec 2021 were included. CTP was added to acute stroke imaging protocol in April 2021. Demographics, NIHSS, quality metrics, imaging parameters, and functional outcomes were compared between patients who did vs. did not undergo CTP at a regional hospital. Core volume growth and rate (ml/min) was assessed for patients with CTP data both at the regional and at the CSC. <h3>Results:</h3> CTP studies were performed on 1083 in regional patients and LVO was confirmed in 29 (2.6%) patients. Of these, 14 were transferred to CSC without CTP at the regional and 15 were transferred with CTP at regional hospital. Age (p=0.44), NIHSS (p=0.08), door to thrombolysis (p=0.13), door-in-door-out (p=0.17) times, successful reperfusion (TICI IIb/III; p=0.83) and hemorrhagic transformation (ECASS III; p=0.49) did not differ significantly between the groups. Rates of mRS 0–2 were similar in both groups (p=0.85). In subgroup analysis of patients who underwent CTP both at the regional facility and CSC (n=15), median core volume grew significantly but slowly (0[0, 7] ml vs. 7 [0, 14] ml, p=0.022), at a median rate of 0 [0, 0.07] ml/min. No patients were excluded from thrombectomy due to core growth. <h3>Conclusions:</h3> Our study suggests that regional site perfusion imaging does not have a significant impact on process times, quality metrics and outcomes. Core volume grows during interfacility transfers without an impact on eligibility for MT. A larger study is needed to assess the role perfusion imaging in regional spokes. <b>Disclosure:</b> Miss Abarca has nothing to disclose. Dr. Potluri has nothing to disclose. Dr. Miller has nothing to disclose. Mrs. Stowe has nothing to disclose. Dr. Wideman has nothing to disclose. Dr. Tsai has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Cerenovus. Tricia Tubergen has received personal compensation in the range of $0-$499 for serving as a Consultant for Medtronic. Todd Mulderink has nothing to disclose. Dr. Min has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Abbott . Dr. Min has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Medtronic . Dr. Wees has nothing to disclose. Dr. Khan has nothing to disclose. Dr. Ahrar has nothing to disclose. Justin Singer has received personal compensation in the range of $50,000-$99,999 for serving as a Consultant for Stryker. Justin Singer has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Medtronic. Justin Singer has received personal compensation in the range of $5,000-$9,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Ceronovus. Justin Singer has received personal compensation in the range of $5,000-$9,999 for serving on a Speakers Bureau for Nico. The institution of Justin Singer has received research support from Stryker Grant. Paul Mazaris has nothing to disclose. John Oostema has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Life EMS. The institution of John Oostema has received research support from Spectrum Health. John Oostema has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant with MDHHS. The institution of Dr. Khan has received research support from NINDS. The institution of Dr. Khan has received research support from Genentech. The institution of Dr. Khan has received research support from Spectrum Health-MSU alliance. Dr. Khan has received research support from NIH.