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Pretreatment CT Perfusion (CTP) parameter rCBV < 42% lesion volume has recently been shown to predict 90-day mRS. In this study, we aim to assess the relationship between rCBV < 42% and a radiographic follow-up infarct volume delineated on FLAIR images. In this retrospective evaluation of our prospectively collected database, we included acute stroke patients triaged by multimodal CT imaging, including CT angiography and perfusion imaging, with confirmed anterior circulation large vessel occlusion between 9 January 2017 and 10 January 2023. Follow-up FLAIR imaging was used to determine the final infarct volume. Student t, Mann-Whitney-U, and Chi-Square tests were used to assess differences. Spearmans rank correlation and linear regression analysis were used to assess associations between rCBV < 42% and follow-up infarct volume on FLAIR. In total, 158 patients (median age: 68 years, 52.5% female) met our inclusion criteria. rCBV < 42% (ρ = 0.56, p < 0.001) significantly correlated with follow-up-FLAIR infarct volume. On multivariable linear regression analysis, rCBV < 42% lesion volume (beta = 0.60, p < 0.001), ASPECTS (beta = -0.214, p < 0.01), mTICI (beta = -0.277, p < 0.001), and diabetes (beta = 0.16, p < 0.05) were independently associated with follow-up infarct volume. The rCBV < 42% lesion volume is independently associated with FLAIR follow-up infarct volume.

Background: The pretreatment CT perfusion (CTP) marker the relative cerebral blood volume (rCBV) < 42% lesion volume has recently been shown to predict 90-day functional outcomes; however, studies assessing correlations of the rCBV < 42% lesion volume with other outcomes remain sparse. Here, we aim to assess the relationship between the rCBV < 42% lesion volume and the reference standard digital subtraction angiography (DSA)-derived American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN) collateral score, hereby referred as the DSA CS. Methods: In this retrospective evaluation of our prospectively collected database, we included acute stroke patients triaged by multimodal CT imaging, including CT angiography and perfusion imaging, with confirmed anterior circulation large vessel occlusion between 1 September 2017 and 1 October 2023. Group differences were assessed using the Students t test, Mann-Whitney U test and Chi-Square test. Spearmans rank correlation and logistic regression analyses were used to assess associations between rCBV < 42% and DSA CS. Results: In total, 222 patients (median age: 69 years, 56.3% female) met our inclusion criteria. In the multivariable logistic regression analysis, taking into account age, sex, race, hypertension, hyperlipidemia, diabetes, atrial fibrillation, prior stroke or transient ischemic attack, the admission National Institute of Health stroke scale, the premorbid modified Rankin score, the Alberta stroke program early CT score (ASPECTS), and segment occlusion, the rCBV < 42% lesion volume (adjusted OR: 0.98, p < 0.05) was independently associated with the DSA CS. Conclusion: The rCBV < 42% lesion volume is independently associated with the DSA CS.

BACKGROUND: Pretreatment CT Perfusion (CTP) parameters serve as reliable surrogates of collateral status (CS). In this study, we aim to assess the relationship between the novel compensation index (CI, Tmax > 4 s/Tmax > 6 s) and already established CTP collateral markers, namely cerebral blood volume (CBV) index and Hypoperfusion Intensity Ratio (HIR), with the reference standard American Society of Interventional and Therapeutic Neuroradiology (ASITN) collateral score (CS) on DSA. METHODS: In this retrospective study, inclusion criteria were the following: (a) CT angiography confirmed anterior circulation large vessel occlusion from 9 January 2017 to 10 January 2023; (b) diagnostic CT perfusion; and (c) underwent mechanical thrombectomy with documented DSA-CS. Student t-test, Mann-Whitney-U-test and Chi-square test were used to assess differences. Spearmans rank correlation and logistic regression analysis were used to assess associations. p ≤ 0.05 was considered significant. RESULTS: In total, 223 patients (mean age: 67.8 ± 15.8, 56% female) met our inclusion criteria. The CI (ρ = 0.37, p < 0.001) and HIR (ρ = -0.29, p < 0.001) significantly correlated with DSA-CS. Whereas the CBV Index (ρ = 0.1, p > 0.05) did not correlate with DSA-CS. On multivariate logistic regression analysis taking into account age, sex, ASPECTS, tPA, premorbid mRS, NIH stroke scale, prior history of TIA, stroke, atrial fibrillation, diabetes mellitus, hyperlipidemia, heart disease and hypertension, only CI was not found to be independently associated with DSA-CS (adjusted OR = 1.387, 95% CI: 1.09-1.77, p < 0.01). CONCLUSION: CI demonstrates a stronger correlation with DSA-CS compared to both the HIR and CBV Index where it may show promise as an additional quantitative pretreatment CS biomarker.

In preparing a case report on Brown-Séquard syndrome for publication, we made the incidental finding that the inexpensive, commercially available three-dimensional (3D) rendering software we were using could produce high quality 3D spinal cord reconstructions from any series of two-dimensional (2D) computed tomography (CT) images. This finding raises the possibility that spinal cord imaging capabilities can be expanded where bundled 2D multi-planar reformats and 3D reconstruction software for CT are not available and in situations where magnetic resonance imaging (MRI) is either not available or appropriate (e.g. metallic implants). Given the worldwide burden of trauma and considering the limited availability of MRI and advanced generation CT scanners, we propose an alternative, potentially useful approach to imaging spinal cord that might be useful in areas where technical capabilities and support are limited. [West J Emerg Med. 2010; 11(5):463-466.]

BackgroundRapid and accurate triage of acute ischemic stroke (AIS) is essential for early revascularization and improved patient outcomes. Response to acute reperfusion therapies varies significantly based on patient-specific cerebrovascular anatomy that governs cerebral blood flow. We present an end-to-end machine learning approach for automatic stroke triage.MethodsEmploying a validated convolutional neural network (CNN) segmentation model for image processing, we extract each patient's cerebrovasculature and its morphological features from baseline non-invasive angiography scans. These features are used to detect occlusion's presence and the site automatically, and for the first time, to estimate collateral circulation without manual intervention. We then use the extracted cerebrovascular features along with commonly used clinical and imaging parameters to predict the 90 days functional outcome for each patient.ResultsThe CNN model achieved a segmentation accuracy of 94% based on the Dice similarity coefficient (DSC). The automatic stroke detection algorithm had a sensitivity and specificity of 92% and 94%, respectively. The models for occlusion site detection and automatic collateral grading reached 96% and 87.2% accuracy, respectively. Incorporating the automatically extracted cerebrovascular features significantly improved the 90 days outcome prediction accuracy from 0.63 to 0.83.ConclusionThe fast, automatic, and comprehensive model presented here can improve stroke diagnosis, aid collateral assessment, and enhance prognostication for treatment decisions, using cerebrovascular morphology.

Patients undergoing standard chemoradiation post-resection had MRIs at radiation planning and fractions 10 and 20 of chemoradiation. MRIs were 1.5T and 3D T2-FLAIR, pre- and post-contrast 3D T1-weighted (T1) and echo planar DWI with three b-values (0, 500, and 1000s/mm2) were acquired. T2-FLAIR was coregistered to T1C images. Non-overlapping T1 contrast-enhancing (T1C) and nonenhancing T2-FLAIR hyperintense regions were segmented, with necrotic/cystic regions, the surgical cavity, and large vessels excluded. The simplified IVIM model was used to calculate voxelwise diffusion coefficient (D) and perfusion fraction (f) maps; ADC was calculated using the natural logarithm of b = 1000 over b = 0 images. T1C and T2-FLAIR segmentations were brought into this space, and medians calculated. MGMT promoter methylation status (MGMTPMS), age at diagnosis, and Eastern Cooperative Oncology Group (ECOG) performance status were extracted from electronic medical records. The data were presented, analyzed, and described in the article, "Intravoxel incoherent motion (IVIM) modeling of diffusion MRI during chemoradiation predicts therapeutic response in IDH wildtype Glioblastoma", published in Radiotherapy and Oncology [1].

BackgroundPrediction of early progression in glioblastoma may provide an opportunity to personalize treatment. Simplified intravoxel incoherent motion (IVIM) MRI offers quantitative estimates of diffusion and perfusion metrics. We investigated whether these metrics, during chemoradiation, could predict treatment outcome.Methods38 patients with newly diagnosed IDH-wildtype glioblastoma undergoing 6-week/30-fraction chemoradiation had standardized post-operative MRIs at baseline (radiation planning), and at the 10th and 20th fractions. Non-overlapping T1-enhancing (T1C) and non-enhancing T2-FLAIR hyperintense regions were independently segmented. Apparent diffusion coefficient (ADCT1C, ADCT2-FLAIR) and perfusion fraction (fT1C, fT2-FLAIR) maps were generated with simplified IVIM modelling. Parameters associated with progression before or after 6.9 months (early vs late progression, respectively), overall survival (OS) and progression-free survival (PFS) were investigated.ResultsHigher ADCT2-FLAIR at baseline [Odds Ratio (OR) = 1.06, 95% CI 1.01-1.15, p = 0.025], lower fT2-FLAIR at fraction 10 (OR = 2.11, 95% CI 1.04-4.27, p = 0.018), and lack of increase in ADCT2-FLAIR at fraction 20 compared to baseline (OR = 1.12, 95% CI 1.02-1.22, p = 0.02) were associated with early progression. Combining ADCT2-FLAIR at baseline, fT2-FLAIR at fraction 10, ECOG and MGMT promoter methylation status significantly improved AUC to 90.3% compared to a model with only ECOG and MGMT promoter methylation status (p = 0.001). Using multivariable analysis, neither IVIM metrics were associated with OS but higher fT2-FLAIR at fraction 10 (HR = 0.72, 95% CI 0.56-0.95, p = 0.018) was associated with longer PFS.ConclusionADCT2-FLAIR at baseline, its lack of increase from baseline to fraction 20, or fT2-FLAIR at fraction 10 significantly predicted early progression. fT2-FLAIR at fraction 10 was associated with PFS.

Domain-specific endpoints are assessments that correspond to the output of individual neural systems and are useful for capturing treatment effects on specific behaviors. By contrast, global endpoints combine several attributes into a single score and are useful for capturing broad treatment effects in a summary way. While global endpoints have become the de facto mechanism required to define benefit in stroke trials, they also have important limitations, some of which might be addressed by simultaneously measuring domain-specific endpoints. Substantial opportunity remains to identify quantifiable patient benefit that would otherwise not be captured by global endpoints. Potential advantages of incorporating domain-specific endpoints in acute stroke trials are discussed, such as increased granularity of measurement, improved understanding of how therapies affect the brain between acute treatment and day 90, and optimized therapeutic translation. Potential disadvantages are also considered, including time and cost of administering domain-specific endpoints, as well as statistical implications. Domain-specific endpoints and global endpoints are not mutually exclusive, and both capture clinical benefits to patients. Incorporating a broader set of outcome assessments in stroke trials, including both global and domain-specific endpoints, is warranted.

Background and purposeTo assess whether initial imaging characteristics independently predict 1-year neurological outcomes in childhood arterial ischemic stroke patients.MethodsWe used prospectively collected demographic and clinical data, imaging data, and 1-year outcomes from the VIPS study (Vascular Effects of Infection in Pediatric Stroke). In 288 patients with first-time stroke, we measured infarct volume and location on the acute magnetic resonance imaging studies and hemorrhagic transformation on brain imaging studies during the acute presentation. Neurological outcome was assessed with the Pediatric Stroke Outcome Measure. We used univariate and multivariable ordinal logistic regression models to test the association between imaging characteristics and outcome.ResultsUnivariate analysis demonstrated that infarcts involving uncinate fasciculus, angular gyrus, insular cortex, or that extended from cortex to the subcortical nuclei were significantly associated with poorer outcomes with odds ratios ranging from 1.95 to 3.95. All locations except the insular cortex remained significant predictors of poor outcome on multivariable analysis. When infarct volume was added to the model, the locations did not remain significant. Larger infarct volumes and younger age at stroke onset were significantly associated with poorer outcome, but the strength of the relationships was weak. Hemorrhagic transformation did not predict outcome.ConclusionsIn the largest pediatric arterial ischemic stroke cohort collected to date, we showed that larger infarct volume and younger age at stroke were associated with poorer outcomes. We made the novel observation that the strength of these associations was modest and limits the ability to use these characteristics to predict outcome in children. Infarcts affecting specific locations were significantly associated with poorer outcomes in univariate and multivariable analyses but lost significance when adjusted for infarct volume. Our findings suggest that infarcts that disrupt critical networks have a disproportionate impact upon outcome after childhood arterial ischemic stroke.

Carotid artery plaque is a measure of atherosclerosis and is associated with future risk of atherosclerotic cardiovascular disease (ASCVD), which encompasses coronary, cerebrovascular, and peripheral arterial diseases. With advanced imaging techniques, computerized tomography (CT) and magnetic resonance imaging (MRI) have shown their potential superiority to routine ultrasound to detect features of carotid plaque vulnerability, such as intraplaque hemorrhage (IPH), lipid-rich necrotic core (LRNC), fibrous cap (FC), and calcification. The correlation between imaging features and histological changes of carotid plaques has been investigated. Imaging of carotid features has been used to predict the risk of cardiovascular events. Other techniques such as nuclear imaging and intra-vascular ultrasound (IVUS) have also been proposed to better understand the vulnerable carotid plaque features. In this article, we review the studies of imaging specific carotid plaque components and their correlation with risk scores.

The medial temporal lobe is one of the most well-studied brain regions affected by Alzheimer's disease (AD). Although the spread of neurofibrillary pathology in the hippocampus throughout the progression of AD has been thoroughly characterized and staged using histology and other imaging techniques, it has not been precisely quantified in vivo at the subfield level using simultaneous positron emission tomography (PET) and magnetic resonance imaging (MRI). Here, we investigate alterations in metabolism and volume using [18F]fluoro-deoxyglucose (FDG) and simultaneous time-of-flight (TOF) PET/MRI with hippocampal subfield analysis of AD, mild cognitive impairment (MCI), and healthy subjects. We found significant structural and metabolic changes within the hippocampus that can be sensitively assessed at the subfield level in a small cohort. While no significant differences were found between groups for whole hippocampal SUVr values (p = 0.166), we found a clear delineation in SUVr between groups in the dentate gyrus (p = 0.009). Subfield analysis may be more sensitive for detecting pathological changes using PET-MRI in AD compared to global hippocampal assessment.

BackgroundPediatric cerebral sinovenous thrombosis is a treatable cause of brain injury, acute symptomatic seizures, and remote epilepsy. Our objective was to prospectively study epilepsy and outcomes in neonates and children one year after cerebral sinovenous thrombosis diagnosis.MethodsPatients with cerebral sinovenous thrombosis were enrolled prospectively from 21 international sites through the Seizures in Pediatric Stroke Study. Clinical data, including acute symptomatic seizures and cerebral sinovenous thrombosis risk factors, were collected at diagnosis. A neuroradiologist who was unaware of the diagnosis reviewed acute imaging. At one year, outcomes including seizure recurrence, epilepsy diagnosis, antiepileptic drug use, and modified Engel score were collected. Outcomes were assessed using the modified Rankin score and the King's Outcome Scale for Childhood Head Injury.ResultsTwenty-four participants with cerebral sinovenous thrombosis were enrolled (67% male, 21% neonates). Headache was the most common presenting symptom in non-neonates (47%, nine of 19). Nine (37.5%) presented with acute symptomatic seizures. Six (25%; 95% confidence interval, 10% to 47%) developed epilepsy by one-year follow-up. No clinical predictors associated with epilepsy were identified. King's Outcome Scale for Childhood Head Injury and modified Rankin scores at one year were favorable in 71%. Half of the patients who developed epilepsy (three of six) did not have infarcts, hemorrhage, or seizures identified during the acute hospitalization.ConclusionOur study provides a prospective estimate that epilepsy occurs in approximately one-quarter of patients by one year after diagnosis of cerebral sinovenous thrombosis. Later epilepsy can develop in the absence of acute seizures or parenchymal injury associated with the acute presentation.

A higher degree of angiogenesis is associated with shortened survival in glioblastoma. Feasible morphometric parameters for analyzing vascular networks in brain tumors in clinical practice are lacking. We investigated whether the macrovascular network classified by the number of vessel-like structures (nVS) visible on three-dimensional T1-weighted contrast⁻enhanced (3D-T1CE) magnetic resonance imaging (MRI) could improve survival prediction models for newly diagnosed glioblastoma based on clinical and other imaging features. Ninety-seven consecutive patients (62 men; mean age, 58 ± 15 years) with histologically proven glioblastoma underwent 1.5T-MRI, including anatomical, diffusion-weighted, dynamic susceptibility contrast perfusion, and 3D-T1CE sequences after 0.1 mmol/kg gadobutrol. We assessed nVS related to the tumor on 1-mm isovoxel 3D-T1CE images, and relative cerebral blood volume, relative cerebral flow volume (rCBF), delay mean time, and apparent diffusion coefficient in volumes of interest for contrast-enhancing lesion (CEL), non-CEL, and contralateral normal-appearing white matter. We also assessed Visually Accessible Rembrandt Images scoring system features. We used ROC curves to determine the cutoff for nVS and univariate and multivariate cox proportional hazards regression for overall survival. Prognostic factors were evaluated by Kaplan-Meier survival and ROC analyses. Lesions with nVS > 5 were classified as having highly developed macrovascular network; 58 (60.4%) tumors had highly developed macrovascular network. Patients with highly developed macrovascular network were older, had higher volumeCEL, increased rCBFCEL, and poor survival; nVS correlated negatively with survival (r = -0.286; p = 0.008). On multivariate analysis, standard treatment, age at diagnosis, and macrovascular network best predicted survival at 1 year (AUC 0.901, 83.3% sensitivity, 93.3% specificity, 96.2% PPV, 73.7% NPV). Contrast-enhanced MRI macrovascular network improves survival prediction in newly diagnosed glioblastoma.

Purpose To investigate ferumoxytol-enhanced MRI as a noninvasive imaging biomarker of macrophages in adults with high-grade gliomas. Materials and Methods In this prospective study, adults with high-grade gliomas were enrolled between July 2015 and July 2017. Each participant was administered intravenous ferumoxytol (5 mg/kg) and underwent 3.0-T MRI 24 hours later. Two sites in each tumor were selected for intraoperative sampling on the basis of the degree of ferumoxytol-induced signal change. Susceptibility and the relaxation rates R2* (1/T2*) and R2 (1/T2) were obtained by region-of-interest analysis by using the respective postprocessed maps. Each sample was stained with Prussian blue, CD68, CD163, and glial fibrillary acidic protein. Pearson correlation and linear mixed models were performed to assess the relationship between imaging measurements and number of 400× magnification high-power fields with iron-containing macrophages. Results Ten adults (four male participants [mean age, 65 years ± 9 {standard deviation}; age range, 57-74 years] and six female participants [mean age, 53 years ± 12 years; age range, 32-65 years]; mean age of all participants, 58 years ± 12 [age range, 32-74 years]) with high-grade gliomas were included. Significant positive correlations were found between susceptibility, R2*, and R2' and the number of high-power fields with CD163-positive (r range, 0.64-0.71; P < .01) and CD68-positive (r range, 0.55-0.57; P value range, .01-.02) iron-containing macrophages. No significant correlation was found between R2 and CD163-positive (r = 0.33; P = .16) and CD68-positive (r = 0.24; P = .32) iron-containing macrophages. Similar significance results were obtained with linear mixed models. At histopathologic analysis, iron particles were found only in macrophages; none was found in glial fibrillary acidic protein-positive tumor cells. Conclusion MRI measurements of susceptibility, R2*, and R2' (R2* - R2) obtained after ferumoxytol administration correlate with iron-containing macrophage concentration, and this shows their potential as quantitative imaging markers of macrophages in malignant gliomas. © RSNA, 2018 Online supplemental material is available for this article.

PurposeThe aim of this study was to compare coronary and carotid artery imaging and determine which one shows the strongest association with atherosclerotic cardiovascular disease (ASCVD) score.Patients and methodsTwo separate series patients who underwent either coronary computed tomography angiography (CTA) or carotid CTA were included. We recorded the ASCVD scores and assessed the CTA imaging. Two thirds were used to build predictive models, and the remaining one third generated predicted ASCVD scores. The Bland-Altman analysis analyzed the concordance.ResultsA total of 110 patients were included in each group. There was no significant difference between clinical characteristics. Three imaging variables were included in the carotid model. Two coronary models (presence of calcium or Agatston score) were created. The bias between true and predicted ASCVD scores was 0.37 ± 5.72% on the carotid model, and 2.07 ± 7.18% and 2.47 ± 7.82% on coronary artery models, respectively.ConclusionsBoth carotid and coronary artery imaging features can predict ASCVD score. The carotid artery was more associated to the ASCVD score than the coronary artery.