DWI intensity values predict FLAIR lesions in acute ischemic stroke

Identification of infarct core and ischemic penumbra using computed tomography perfusion and deep learning

Purpose

The size and location of infarct and penumbra are key to decision-making for acute ischemic stroke (AIS) management. CT perfusion (CTP) software estimate infarct and penumbra volume using contralateral hemisphere relative thresholding. This approach is not robust and widely contested by the scientific community. In this study, we investigate the use of deep learning-based algorithms to efficiently locate infarct and penumbra tissue on CTP hemodynamic maps.

Approach

CTP scans were retrospectively collected for 60 and 59 patients in the infarct only and infarct + penumbra substudies respectively. Commercial CTP software was used to generate cerebral blood flow, cerebral blood volume, mean transit time, time to peak, and delay time maps. U-Net-shaped architectures were trained to segment infarct or infarct + penumbra. Test-time-augmentation, ensembling, and watershed segmentation were used as postprocessing techniques. Segmentation performance was evaluated using Dice coefficients (DC) and mean absolute volume errors (MAVE).

Results

The algorithm segmented infarct tissue resulted in DC of 0.64 ± 0.03 (0.63, 0.65), and MAVE of 4.91 ± 0.94 (4.5, 5.32) mL. In comparison, the commercial software predicted infarct with a DC of 0.31 ± 0.17 (0.26, 0.36) and MAVE of 9.77 ± 8.35 (7.12, 12.42) mL. The algorithm was able to segment infarct + penumbra with a DC of 0.61 ± 0.04 (0.6, 0.63), and MAVE of 6.51 ± 1.37 (5.91, 7.11) mL. In comparison, the commercial software predicted infarct + penumbra with a DC of 0.3 ± 0.19 (0.25, 0.35) and MAVE of 9.18 ± 7.55 (7.25, 11.11) mL.

Conclusions

Use of deep learning algorithms to assess severity of AIS in terms of infarct and penumbra volume is precise and outperforms current relative thresholding methods. Such an algorithm would enhance the reliability of CTP in guiding treatment decisions.

Semantic segmentation guided detector for segmentation, classification, and lesion mapping of acute ischemic stroke in MRI images

BACKGROUND AND PURPOSE

MRI images timely and accurately reflect ischemic injuries to the brain tissues and, therefore, can support clinical decision-making of acute ischemic stroke (AIS). To maximize the information provided by the MRI images, we leverage deep learning models to segment, classify, and map lesion distributions of AIS.

METHODS

We evaluated brain MRI images of AIS patients from 2017 to 2020 at a tertiary teaching hospital and developed the Semantic Segmentation Guided Detector Network (SGD-Net), composed of the first U-shaped model for segmentation in diffusion-weighted imaging (DWI) and the second model for binary classification of lesion size (lacune vs. non-lacune) and circulatory territory of lesion location (anterior vs. posterior circulation). Next, we modified the two-stage deep learning model into SGD-Net Plus by automatically segmenting AIS lesions in DWI images and registering the lesion in T1-weighted images and the brain atlases.

RESULTS

The final enrollment (216 patients with 4606 slices) was divided into 80% for model development and 20% for testing. S1 model segmented AIS lesions in DWI images accurately with a pixel accuracy > 99% (Dice 0.806-0.828 and IoU 0.675-707). In comprehensive evaluation of classification performance, the two-stage SGD-Net outperformed the traditional one-stage models in classifying AIS lesion size (accuracy 0.867-0.956 vs. 0.511-0.867, AUROC 0.962-0.992 vs. 0.528-0.937, AUPRC 0.964-0.994 vs. 0.549-0.938) and location (accuracy 0.860-0.930 vs. 0.326-0.721, AUROC 0.936-0.988 vs. 0.493-0.833, AUPRC 0.883-0.978 vs. 0.365-0.695). The precise lesion segmentation at the first stage of the deep learning model was the basis for further application. After that, the modified two-stage model SGD-Net Plus accurately reported the volume, region percentage, and lesion percentage of each region on the selected brain atlas. Its reports provided clear descriptions and quantifications of the AIS-related brain injuries on white matter tracts, Brodmann areas, and cytoarchitectonic areas.

CONCLUSION

Domain knowledge-oriented design of artificial intelligence applications can deepen our understanding of patients' conditions and strengthen the use of MRI for patient care. SGD-Net precisely segments AIS lesions on DWI and accurately classifies the lesions. In addition, SGD-Net Plus maps the AIS lesions and quantifies their occupancy in each brain region. They are practical tools to meet the clinical needs and enrich educational resources of neuroimage.

Diffusion-Weighted Imaging and Fluid-Attenuated Inversion Recovery Quantification to Predict Diffusion-Weighted Imaging-Fluid-Attenuated Inversion Recovery Mismatch Status in Ischemic Stroke With Unknown Onset

BACKGROUND

Visual rating of diffusion-weighted imaging (DWI)-fluid-attenuated inversion recovery (FLAIR) mismatch can be challenging. We evaluated quantification of DWI and FLAIR to predict DWI-FLAIR mismatch status in ischemic stroke.

METHODS

In screened patients from the WAKE-UP trial (Efficacy and Safety of Magnetic Resonance Imaging-Based Thrombolysis in Wake-Up Stroke), we retrospectively studied relative DWI (rDWI SI) and FLAIR signal intensity (rFLAIR SI). We defined the optimal mean rFLAIR SI and interquartile range of the rDWI SI in the DWI lesion to predict DWI-FLAIR mismatch status. We investigated agreement between each quantitative parameter and the DWI-FLAIR mismatch and the association between both quantitative parameters. We evaluated the predictive value of the quantitative parameters for excellent functional outcome by logistic regression, adjusted for DWI lesion volume, treatment, age, and National Institutes of Health Stroke Scale score.

RESULTS

In the rFLAIR and rDWI SI analysis, 213/369 and 241/421 subjects respectively had a DWI-FLAIR mismatch. A mean rFLAIR SI cutoff of 1.09 and interquartile range rDWI SI cutoff of 0.47 were optimal to predict the DWI-FLAIR mismatch with a sensitivity and specificity of 77% (95% CI, 71%-83%) and 67% (95% CI, 59%-74%), and 76% (95% CI, 70%-81%) and 72% (95% CI, 65%-79%), respectively. For both quantitative parameters, agreement with the DWI-FLAIR mismatch was fair (73%, κ=0.44 [95% CI, 0.35-0.54] for rFLAIR and 74%, κ=0.48 [95% CI, 0.39-0.56] for rDWI). Both quantitative parameters correlated moderately (Pearson R=0.54 [95% CI, 0.46-0.61], P<0.001, n=367). The interquartile range rDWI SI (n=188), but not the mean rFLAIR SI (n=172), was an independent predictor of excellent functional outcome (odds ratio, 0.67 per 0.1 unit increase of interquartile range rDWI SI, 95% CI, 0.51-0.89, P=0.01).

CONCLUSIONS

Agreement between the quantitative and qualitative approach may be insufficient to advocate DWI or FLAIR quantification as alternative for visual rating.

Use of DWI-FLAIR Mismatch to Estimate the Onset Time in Wake-Up Strokes

PURPOSE

To compare the MRI characteristics of patients with wake-up ischemic stroke (WUS) and with ischemic stroke with known onset time (clear-onset-time stroke, COS) to clarify the role of diffusion-weighted imaging-fluid-attenuated inversion recovery (DWI-FLAIR) mismatch in estimating the onset time of WUS patients.

PATIENTS AND METHODS

Two hundred patients with acute ischemic stroke were selected for complete brain MRI within six hours of symptom onset, including DWI and FLAIR sequences. The patients were divided into WUS (n = 78) and COS (n = 122) groups, based on whether the time of onset was known. The general conditions and imaging characteristics were collected to compare the DWI-FLAIR mismatch features between the two groups at different time intervals.

RESULTS

There was no significant difference in the DWI-FLAIR mismatch on MRI within 2 hour after the first found abnormality between the two groups (50.0% vs 71.8%, p = 0.180). With increasing time, the DWI-FLAIR mismatch decreased substantially in the WUS group, while a higher DWI-FLAIR mismatch presence persisted in the COS group within a four-hour interval from the onset of symptoms to the MRI. The DWI-FLAIR mismatch was significantly lower in the WUS group than in the COS group from symptom identification to MRI at 2-3 h, 3-4 h, and 4-5 h intervals (15% vs 60%, 10.5% vs 48%, 6.7% vs 45.4%; p < 0.01).

CONCLUSION

Our results suggest that the presence of DWI-FLAIR mismatch within 2 h of the first found abnormality was not significantly different between WUS and COS. Therefore, Patients with WUS within 2 hours after the first detected abnormality may be suitable for intravenous thrombolysis.

Low relative diffusion weighted image signal intensity can predict good prognosis after endovascular thrombectomy in patients with acute ischemic stroke

BACKGROUND

It is vital to identify a surrogate last-known-well time to perform proper endovascular thrombectomy in acute ischemic stroke; however, no established imaging biomarker can easily and quickly identify eligibility for endovascular thrombectomy and predict good clinical prognosis.

OBJECTIVE

To investigate whether low relative diffusion-weighted imaging (DWI) signal intensity can be used as a predictor of good clinical outcome after endovascular thrombectomy in patients with acute ischemic stroke.

METHODS

We retrospectively identified consecutive patients with acute ischemic stroke who were treated with endovascular thrombectomy within 24 hours of the last-known-well time and achieved successful recanalization (modified Thrombolysis in Cerebral Infarction score ≥2b). Relative DWI signal intensity was calculated as DWI signal intensity in the infarcted area divided by DWI signal intensity in the contralateral hemisphere. Good prognosis was defined as a modified Rankin Scale score of 0-2 at 90 days after stroke onset (good prognosis group).

RESULTS

49 patients were included in the analysis. Relative DWI signal intensity was significantly lower in the group with good prognosis than in the those with poor prognosis (median (IQR) 1.32 (1.27-1.44) vs 1.56 (1.43-1.66); p<0.01), and the critical cut-off value for predicting good prognosis was 1.449 (area under the curve 0.78). Multiple logistic regression analysis revealed association of good prognosis after endovascular thrombectomy with low relative DWI signal intensity (OR=6.84; 95% CI 1.13 to 41.3; p=0.04).

CONCLUSIONS

Low relative DWI signal intensity was associated with good prognosis after endovascular thrombectomy. Its ability to predict good clinical outcome shows potential for determining patient suitability for endovascular thrombectomy.

Use of a convolutional neural network to identify infarct core using computed tomography perfusion parameters

Purpose

Computed tomography perfusion (CTP) is used to diagnose ischemic strokes through contralateral hemisphere comparisons of various perfusion parameters. Various perfusion parameter thresholds have been utilized to segment infarct tissue due to differences in CTP software and patient baseline hemodynamics. This study utilized a convolutional neural network (CNN) to eliminate the need for non-universal parameter thresholds to segment infarct tissue.

Methods

CTP data from 63 ischemic stroke patients was retrospectively collected and perfusion parameter maps were generated using Vitrea CTP software. Infarct ground truth labels were segmented from diffusion-weighted imaging (DWI) and CTP and DWI volumes were registered. A U-net based CNN was trained and tested five separate times using each CTP parameter (cerebral blood flow (CBF), cerebral blood volume (CBV), time-to-peak (TTP), mean-transit-time (MTT), delay time). 8,352 infarct slices were utilized with a 60:30:10 training:testing:validation split and Monte Carlo cross-validation was conducted using 20 iterations. Infarct volumes were reconstructed following segmentation from each CTP slice. Infarct spatial and volumetric agreement was compared between each CTP parameter and DWI.

Results

Spatial agreement metrics (Dice coefficient, positive predictive value) for each CTP parameter in predicting infarct volumes are: CBF=(0.67, 0.76), CBV=(0.44, 0.62), TTP=(0.60, 0.67), MTT=(0.58, 0.62), delay time=(0.57, 0.60). 95% confidence intervals for volume differences with DWI infarct are: CBF=14.3±11.5 mL, CBV=29.6±21.2 mL, TTP=7.7±15.2 mL, MTT=-10.7±18.6 mL, delay time=-5.7±23.6 mL.

Conclusions

CBF is the most accurate CTP parameter in segmenting infarct tissue. Segmentation of infarct using a CNN has the potential to eliminate non-universal CTP contralateral hemisphere comparison thresholds.

Enhancing performance of a computed tomography perfusion software for improved prediction of final infarct volume in acute ischemic stroke patients

Computed tomography perfusion (CTP) is crucial for acute ischemic stroke (AIS) patient diagnosis. To improve infarct prediction, enhanced image processing and automated parameter selection have been implemented in Vital Images' new CTP+ software. We compared CTP+ with its previous version, commercially available software (RAPID and Sphere), and follow-up diffusion-weighted imaging (DWI). Data from 191 AIS patients between March 2019 and January 2020 was retrospectively collected and allocated into endovascular intervention (n = 81) and conservative treatment (n = 110) cohorts. Intervention patients were treated for large vessel occlusion, underwent mechanical thrombectomy, and achieved successful reperfusion of thrombolysis in cerebral infarction 2b/2c/3. Conservative treatment patients suffered large or small vessel occlusion and did not receive intravenous thrombolysis or mechanical thrombectomy. Infarct and penumbra were assessed using intervention and conservative treatment patients, respectively. Infarct and penumbra volumes were segmented from CTP+ and compared with 24-h DWI along with RAPID, Sphere, and Vitrea. Mean infarct differences (95% confidence intervals) and Spearman correlation coefficients (SCCs) between DWI and each CTP software product for intervention patients are: CTP+  = (5.8 ± 5.9 ml, 0.62), RAPID = (10.0  ± 5.2 ml, 0.73), Sphere = (3.0 ± 6.0 ml, 0.56), Vitrea = (7.2 ± 4.9 ml, 0.66). For conservative treatment patients, mean infarct differences and SCCs are: CTP+ = (-8.0 ± 5.4 ml, 0.64), RAPID = (-25.6 ± 11.5 ml, 0.60), Sphere = (-25.6 ± 8.0 ml, 0.66), Vitrea = (1.3 ± 4.0 ml, 0.72). CTP+ performed similarly to RAPID and Sphere in addition to its semi-automated predecessor, Vitrea, when assessing intervention patient infarct volumes. For conservative treatment patients, CTP+ outperformed RAPID and Sphere in assessing penumbra. Semi-automated Vitrea remains the most accurate in assessing penumbra, but CTP+ provides an improved workflow from its predecessor.

Clinical Evaluations of the Ischemic Core in Acute Ischemic Stroke Using Modified Diffusion-Weighted Imaging-Alberta Stroke Program Early Computed Tomography Scores by Ischemic Reversibility Using the Signal Intensity

Objective

Early recanalization of acute stroke caused by large vessel occlusion (LVO) may improve high signal intensity (HSI) on diffusion-weighted imaging (DWI). In this study, we investigated whether subtraction of reversible ischemic lesions (RIL) from the HSI lesions on DWI improves the diagnostic accuracy for the ischemic core.

Methods

A total of 35 patients from April 2013 and December 2019 were included in this study. These patients presented acute ischemic stroke due to anterior circulation LVO and underwent thrombectomy. All patients underwent DWI within 48 hours after thrombectomy. HSI ratios were calculated, and compared between ischemic lesions and contralateral normal tissue. Ischemic lesions with improvement in the HSI ratio from initial to postoperative DWI were defined as RIL. Based on a receiver operating characteristic (ROC) curve analysis that compared the HSI ratio of all ischemic lesions, the cutoff value of HSI ratio of RILs was calculated.

Results

In all, 127 ischemic lesions were identified in 35 patients. HSI ratios of RILs were significantly lower than those of irreversible ischemic lesions (IILs) (p <0.0001). Based on a ROC curve analysis that compared the HSI ratio of all 127 lesions, the cutoff value of the HSI ratio of RILs was 1.4. After applying this cutoff value to the 127 ischemic lesions of the 35 patients, 20 patients (57%) were identified as having RILs with a HSI ratio of <1.4. In this 20 patients, the postoperative National Institutes of Health Stroke Scale (NIHSS) score at 24 hours was significantly lower (p = 0.007) and improvement in the NIHSS score was significantly higher (p = 0.018) than in the other patients.

Conclusion

A HSI ratio of <1.4 on preoperative DWI may reflect ischemic reversibility. In this study, the HSI ratio correlated with clinical findings associated with cerebral ischemia, and our method may be useful in assessing ischemic cores.

Investigation of convolutional neural networks using multiple computed tomography perfusion maps to identify infarct core in acute ischemic stroke patients

Purpose: To assess acute ischemic stroke (AIS) severity, infarct is segmented using computed tomography perfusion (CTP) software, such as RAPID, Sphere, and Vitrea, relying on contralateral hemisphere thresholds. Since this approach is potentially patient dependent, we investigated whether convolutional neural networks (CNNs) could achieve better performances without the need for contralateral hemisphere thresholds. Approach: CTP and diffusion-weighted imaging (DWI) data were retrospectively collected for 63 AIS patients. Cerebral blood flow (CBF), cerebral blood volume (CBV), time-to-peak, mean-transit-time (MTT), and delay time maps were generated using Vitrea CTP software. U-net shaped CNNs were developed, trained, and tested for 26 different input CTP parameter combinations. Infarct labels were segmented from DWI volumes registered with CTP volumes. Infarct volumes were reconstructed from two-dimensional CTP infarct segmentations. To remove erroneous segmentations, conditional random field (CRF) postprocessing was applied and compared with prior results. Spatial and volumetric infarct agreement was assessed between DWI and CTP (CNNs and commercial software) using median infarct difference, median absolute error, dice coefficient, positive predictive value. Results: The most accurate combination of parameters for CNN segmenting infarct using CRF postprocessing was CBF, CBV, and MTT (4.83 mL, 10.14 mL, 0.66, 0.73). Commercial software results are: RAPID = (2.25 mL, 21.48 mL, 0.63, 0.70), Sphere = (7.57 mL, 17.74 mL, 0.64, 0.70), Vitrea = (6.79 mL, 15.28 mL, 0.63, 0.72). Conclusions: Use of CNNs with multiple input perfusion parameters has shown to be accurate in segmenting infarcts and has the ability to improve clinical workflow by eliminating the need for contralateral hemisphere comparisons.

Assessment of a Bayesian Vitrea CT Perfusion Analysis to Predict Final Infarct and Penumbra Volumes in Patients with Acute Ischemic Stroke: A Comparison with RAPID

BACKGROUND AND PURPOSE

Brain CTP is used to estimate infarct and penumbra volumes to determine endovascular treatment eligibility for patients with acute ischemic stroke. We aimed to assess the accuracy of a Bayesian CTP algorithm in determining penumbra and final infarct volumes.

MATERIALS AND METHODS

Data were retrospectively collected for 105 patients with acute ischemic stroke (55 patients with successful recanalization [TICI 2b/2c/3] and large-vessel occlusions and 50 patients without interventions). Final infarct volumes were calculated using DWI and FLAIR 24 hours following CTP imaging. RAPID and the Vitrea Bayesian CTP algorithm (with 3 different settings) predicted infarct and penumbra volumes for comparison with final infarct volumes to assess software performance. Vitrea settings used different combinations of perfusion maps (MTT, TTP, CBV, CBF, delay time) for infarct and penumbra quantification. Patients with and without interventions were included for assessment of predicted infarct and penumbra volumes, respectively.

RESULTS

RAPID and Vitrea default setting had the most accurate final infarct volume prediction in patients with interventions ([Spearman correlation coefficient, mean infarct difference] default versus FLAIR: [0.77, 4.1 mL], default versus DWI: [0.72, 4.7 mL], RAPID versus FLAIR: [0.75, 7.5 mL], RAPID versus DWI: [0.75, 6.9 mL]). Default Vitrea and RAPID were the most and least accurate in determining final infarct volume for patients without an intervention, respectively (default versus FLAIR: [0.76, -0.4 mL], default versus DWI: [0.71, -2.6 mL], RAPID versus FLAIR: [0.68, -49.3 mL], RAPID versus DWI: [0.65, -51.5 mL]).

CONCLUSIONS

Compared with RAPID, the Vitrea default setting was noninferior for patients with interventions and superior in penumbra estimation for patients without interventions as indicated by mean infarct differences and correlations with final infarct volumes.

Multimodal magnetic resonance imaging to identify stroke onset within 6 h in patients with large vessel occlusions

Introduction

Mechanical thrombectomy within 6 h after stroke onset improves the outcome in patients with large vessel occlusions. The aim of our study was to establish a model based on diffusion weighted and perfusion weighted imaging to provide an accurate prediction for the 6 h time-window in patients with unknown time of stroke onset.

Patients and methods

A predictive model was designed based on data from the DEFUSE 2 study and validated in a subgroup of patients with large vessel occlusions from the AXIS 2 trial.

Results

We constructed the model in 91 patients from DEFUSE 2. The following parameters were independently associated with <6 h time-window and included in the model: interquartile range and median relative diffusion weighted imaging, hypoperfusion intensity ratio, core volume and the interaction between median relative diffusion weighted imaging and hypoperfusion intensity ratio as predictors of the 6 h time-window. The area under the curve was 0.80 with a positive predictive value of 0.90 (95%CI 0.79-0.96). In the validation cohort (N = 90), the area under the curve was 0.73 (P for difference = 0.4) with a positive predictive value of 0.85 (95%CI 0.69-0.95).

Discussion

After validation in a larger independent dataset the model can be considered to select patients for endovascular treatment in whom stroke onset is unknown.

Conclusion

In patients with large vessel occlusion and unknown time of stroke onset an automated multivariate imaging model is able to select patients who are likely within the 6 h time-window.

Mapping a Reliable Stroke Onset Time Course Using Signal Intensity on DWI Scans

BACKGROUND AND PURPOSE

Identifying a last known well (LKW) time surrogate for acute stroke is vital to increase stroke treatment. Diffusion-weighted imaging (DWI) signal intensity initially increases from onset of stroke but mapping a reliable time course to the signal intensity has not been demonstrated.

METHODS

We retrospectively reviewed stroke code patients between 1/2016 and 6/2017 from the prospective; Institutional review board (IRB) approved University of California San Diego Stroke Registry. Patients who had magnetic resonance imaging of brain from onset, with or without intervention, are included. All ischemic strokes were confirmed and timing from onset to imaging was calculated. Raw DWI intensity is measured using IMPAX software and compared to contralateral side for control for a relative DWI intensity (rDWI). LKW and magnetic resonance imaging (MRI) time were collected by chart review. Correlation is assessed using Pearson correlation coefficient between DWI intensity, rDWI, and time to MRI imaging. 1.5T, 3T, and combined modalities were examined.

RESULTS

Seventy-eight patients were included in this analysis. Overall, there was statistically significant positive correlation (.53, P < .001) between DWI intensity and LKW time irrespective of scanner strength. Using 1.5T analyses, there was good correlation (.46, P < .001). 3T MRI analysis further showed comparatively stronger positive correlation (.66, P < .001).

CONCLUSIONS

There is good correlation between DWI intensity and minutes from onset to MRI. This suggests a time-dependent DWI intensity response and supports the potential use of DWI intensity measurements to extrapolate an LKW time. Further studies are being pursued to increase both experience and generalizability.

Automated DWI analysis can identify patients within the thrombolysis time window of 4.5 hours

Objective

To develop an automated model based on diffusion-weighted imaging (DWI) to detect patients within 4.5 hours after stroke onset and compare this method to the visual DWI-FLAIR (fluid-attenuated inversion recovery) mismatch.

Methods

We performed a subanalysis of the “DWI-FLAIR mismatch for the identification of patients with acute ischemic stroke within 4.5 hours of symptom onset” (PRE-FLAIR) and the “AX200 for ischemic stroke” (AXIS 2) trials. We developed a prediction model with data from the PRE-FLAIR study by backward logistic regression with the 4.5-hour time window as dependent variable and the following explanatory variables: age and median relative DWI (rDWI) signal intensity, interquartile range (IQR) rDWI signal intensity, and volume of the core. We obtained the accuracy of the model to predict the 4.5-hour time window and validated our findings in an independent cohort from the AXIS 2 trial. We compared the receiver operating characteristic curve to the visual DWI-FLAIR mismatch.

Results

In the derivation cohort of 118 patients, we retained the IQR rDWI as explanatory variable. A threshold of 0.39 was most optimal in selecting patients within 4.5 hours after stroke onset resulting in a sensitivity of 76% and specificity of 63%. The accuracy was validated in an independent cohort of 200 patients. The predictive value of the area under the curve of 0.72 (95% confidence interval 0.64–0.80) was similar to the visual DWI-FLAIR mismatch (area under the curve = 0.65; 95% confidence interval 0.58–0.72; p for difference = 0.18).

Conclusions

An automated analysis of DWI performs at least as good as the visual DWI-FLAIR mismatch in selecting patients within the 4.5-hour time window.

Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

MRI provides a sensitive and specific imaging tool to detect acute ischemic stroke by means of a reduced diffusion coefficient of brain water. In a rat model of ischemic stroke, differences in quantitative T₁ and T₂ MRI relaxation times (qT₁ and qT₂) between the ischemic lesion (delineated by low diffusion) and the contralateral non-ischemic hemisphere increase with time from stroke onset. The time dependency of MRI relaxation time differences is heuristically described by a linear function and thus provides a simple estimate of stroke onset time. Additionally, the volumes of abnormal qT₁ and qT₂ within the ischemic lesion increase linearly with time providing a complementary method for stroke timing. A (semi)automated computer routine based on the quantified diffusion coefficient is presented to delineate acute ischemic stroke tissue in rat ischemia. This routine also determines hemispheric differences in qT₁ and qT₂ relaxation times and the location and volume of abnormal qT₁ and qT₂ voxels within the lesion. Uncertainties associated with onset time estimates of qT₁ and qT₂ MRI data vary from ± 25 min to ± 47 min for the first 5 hours of stroke. The most accurate onset time estimates can be obtained by quantifying the volume of overlapping abnormal qT₁ and qT₂ lesion volumes, termed 'V^overlap' (± 25 min) or by quantifying hemispheric differences in qT₂ relaxation times only (± 28 min). Overall, qT₂ derived parameters outperform those from qT₁. The current MRI protocol is tested in the hyperacute phase of a permanent focal ischemia model, which may not be applicable to transient focal brain ischemia.

Simple quantitative measurement based on DWI to objectively judge DWI-FLAIR mismatch in a canine stroke model

PURPOSE

Diffusion-weighted imaging (DWI) - fluid attenuated inversion recovery (FLAIR) mismatch was proven useful to time the onset of wake-up stroke; however, identifying the status of FLAIR imaging has been mostly subjective. We aimed to evaluate the value of relative DWI signal intensity (rDWI), and relative apparent diffusion coefficient (rADC) in identifying the FLAIR status in the acute period.

METHODS

Autologous clot was used to embolize left middle cerebral artery in 20 dogs. Magnetic resonance imaging was performed 3-6 hours and 24 hours after embolization. DWI-FLAIR mismatch was defined as hyperintense signal detected on DWI, but not on FLAIR. The mean values of rDWI or rADC of FLAIR- and FLAIR+ lesions were compared and the critical cutoff values of rDWI and rADC for identifying the FLAIR status were determined.

RESULTS

Stroke models were successfully established in all animals. DWI+ lesions were found in all 20 dogs from three hours, while FLAIR+ lesions were found in three, 11, 16, 19, and 20 dogs at five time points after embolization, respectively. The mean rDWI values were significantly different between FLAIR- and FLAIR+ lesions (P < 0.001), but rADC values were not (P = 0.73). Using rDWI=1.90 as the threshold value, excellent diagnostic efficacy was achieved (AUC, 0.88; sensitivity, 0.77; specificity, 0.88). However, rADC appeared not useful (AUC, 0.48; sensitivity, 0.52; specificity, 0.58) in identifying the FLAIR status.

CONCLUSION

In our embolic canine stroke model, rDWI was useful to identify FLAIR imaging status in the acute period, while rADC was not.

Prediction of Stroke Onset Is Improved by Relative Fluid-Attenuated Inversion Recovery and Perfusion Imaging Compared to the Visual Diffusion-Weighted Imaging/Fluid-Attenuated Inversion Recovery Mismatch

Background and Purpose—

Acute stroke patients with unknown time of symptom onset are ineligible for thrombolysis. The diffusion-weighted imaging and fluid-attenuated inversion recovery (FLAIR) mismatch is a reasonable predictor of stroke within 4.5 hours of symptom onset, and its clinical usefulness in selecting patients for thrombolysis is currently being investigated. The accuracy of the visual mismatch rating is moderate, and we hypothesized that the predictive value of stroke onset within 4.5 hours could be improved by including various clinical and imaging parameters.

Methods—

In this study, 141 patients in whom magnetic resonance imaging was obtained within 9 hours after symptom onset were included. Relative FLAIR signal intensity was calculated in the region of nonreperfused core. Mean T max was calculated in the total region with T max >6 s. Mean relative FLAIR, mean T max , lesion volume with T max >6 s, age, site of arterial stenosis, core volume, and location of infarct were analyzed by logistic regression to predict stroke onset time before or after 4.5 hours.

Results—

Receiver-operating characteristic curve analysis revealed an area under the curve of 0.68 (95% confidence interval 0.59–0.78) for the visual diffusion-weighted imaging/FLAIR mismatch, thereby correctly classifying 69% of patients with an onset time before or after 4.5 hours. Age, relative FLAIR, and T max increased the accuracy significantly ( P <0.01) to an area under the curve of 0.82 (95% confidence interval 0.74–0.89). This new predictive model correctly categorized 77% of patients according to stroke onset before versus after 4.5 hours.

Conclusions—

In patients with unknown stroke onset, the accuracy of predicting time from symptom onset within 4.5 hours is improved by obtaining relative FLAIR and perfusion imaging.

Determining Stroke Onset Time Using Quantitative MRI: High Accuracy, Sensitivity and Specificity Obtained from Magnetic Resonance Relaxation Times

Many ischaemic stroke patients are ineligible for thrombolytic therapy due to unknown onset time. Quantitative MRI (qMRI) is a potential surrogate for stroke timing. Rats were subjected to permanent middle cerebral artery occlusion and qMRI parameters including hemispheric differences in apparent diffusion coefficient, T₂-weighted signal intensities, T₁ and T₂ relaxation times (qT₁, qT₂) and f₁, f₂ and V_overlap were measured at hourly intervals at 4.7 or 9.4 T. Accuracy and sensitivity for identifying strokes scanned within and beyond 3 h of onset was determined. Accuracy for V_overlap, f₂ and qT₂ (>90%) was significantly higher than other parameters. At a specificity of 1, sensitivity was highest for V_overlap (0.90) and f₂ (0.80), indicating promise of these qMRI indices in the clinical assessment of stroke onset time.

Stroke onset time estimation from multispectral quantitative magnetic resonance imaging in a rat model of focal permanent cerebral ischemia

Background

Quantitative T2 relaxation magnetic resonance imaging allows estimation of stroke onset time.

Aims

We aimed to examine the accuracy of quantitative T1 and quantitative T2 relaxation times alone and in combination to provide estimates of stroke onset time in a rat model of permanent focal cerebral ischemia and map the spatial distribution of elevated quantitative T1 and quantitative T2 to assess tissue status.

Methods

Permanent middle cerebral artery occlusion was induced in Wistar rats. Animals were scanned at 9.4T for quantitative T1, quantitative T2, and Trace of Diffusion Tensor (Dav) up to 4 h post-middle cerebral artery occlusion. Time courses of differentials of quantitative T1 and quantitative T2 in ischemic and non-ischemic contralateral brain tissue (ΔT1, ΔT2) and volumes of tissue with elevated T1 and T2 relaxation times ( f1, f2) were determined. TTC staining was used to highlight permanent ischemic damage.

Results

ΔT1, ΔT2, f1, f2, and the volume of tissue with both elevated quantitative T1 and quantitative T2 (VOverlap) increased with time post-middle cerebral artery occlusion allowing stroke onset time to be estimated. VOverlap provided the most accurate estimate with an uncertainty of ±25 min. At all times-points regions with elevated relaxation times were smaller than areas with Dav defined ischemia.

Conclusions

Stroke onset time can be determined by quantitative T1 and quantitative T2 relaxation times and tissue volumes. Combining quantitative T1 and quantitative T2 provides the most accurate estimate and potentially identifies irreversibly damaged brain tissue.

Can MRI quantification help evaluate stroke age?

BACKGROUND

Diffusion-weighted imaging (DWI) fluid-attenuated inversion recovery (FLAIR) mismatch has a proven ability to estimate stroke-to-magnetic resonance imaging (MRI) delay. We evaluated the possibility of enhancing this estimation by quantifying MRI (DWI and FLAIR) signals, and compared this approach to the visual evaluation of DWI-FLAIR mismatch.

MATERIALS AND METHODS

This retrospective study included 194 patients presenting an ischemic stroke in the middle cerebral artery territory that had been explored with 3T MRI within 12h. According to the study design, written informed consent was waived and patient information was anonymized and de-identified prior to analysis. DWI-FLAIR mismatch was visually estimated by two radiologists and a quantification of MRI signals based on a manual segmentation of stroke lesion volume was performed. Using their receiver operating curve and area under the curve (AUC), we identified the variables of MRI quantification that were predictive of stroke-to-MRI delay, then compared their performance against visual classification.

RESULTS

The quantitative variables identified as predictive of stroke-to-MRI delay were: 1st quartile, 3rd quartile and median values of B0; 1st quartile, 3rd quartile, median and relative values of B1000; 1st quartile and relative values of the apparent diffusion coefficient. FLAIR was not found to be predictive. The AUC values of these variables ranged between 0618±0.053 and 0.683±0.048. The relative value of B1000 appeared to be the best predictive quantitative variable, with predictive values comparable to visual classification.

CONCLUSIONS

The quantification of MRI signal may be a helpful tool for stroke dating but cannot outperform the visual estimation of stroke lesion age.