Walter Moeller-Hartmann

Maps of Time to Maximum and Time to Peak for Mismatch Definition in Clinical Stroke Studies Validated With Positron Emission Tomography

Background and Purpose— Perfusion-weighted imaging-derived maps of time-to-maximum (Tmax) are increasingly used to identify the tissue at risk in clinical stroke studies (eg, DEFUSE and EPITHET). Using quantitative positron emission tomography (PET), we evaluated Tmax to define the penumbral flow threshold in stroke patients and compared its performance to nondeconvolved time-to-peak (TTP) maps. Methods— Comparative perfusion-weighted imaging and quantitative 15O-water PET images of acute stroke patients were analyzed using cortical regions of interest. A receiver-operating characteristic curve analysis described the threshold independent performance of Tmax (area under the curve) and identified the best threshold (equal sensitivity and specificity threshold) to identify penumbral flow (<20 mL/100 g/min on PET cerebral blood flow). The results were compared with nondeconvolved TTP and other current perfusion-weighted imaging maps using the Mann–Whitney rank-sum test. Results— In 26 patients (time delay between MRI and PET, 65 minutes), the best threshold for penumbral flow was 5.5 seconds for Tmax (median; interquartile range, 3.9–6.6; sensitivity/specificity, 88%/89%). The area under the curve value was 0.95 (median; interquartile range, 0.93–0.97). Deconvolved Tmax did not perform significantly better than TTP (P=0.34). Conclusion— Maps of Tmax detected penumbral flow but did not perform better than the easy-to-obtain maps of nondeconvolved TTP. Thus, “simple” TTP maps still remain suitable for clinical stroke studies if detailed postprocessing is not feasible.

The performance of MRI-based cerebral blood flow measurements in acute and subacute stroke compared with 15O-water positron emission tomography identification of penumbral flow

Background and Purpose— Perfusion-weighted MRI-based maps of cerebral blood flow (CBFMRI) are considered a good MRI measure of penumbral flow in acute ischemic stroke but are seldom used in clinical routine due to methodical issues. We validated CBFMRI on quantitative CBF measurement by 15O-water positron emission tomography (CBFPET). Material and Methods— Comparative CBFMRI and CBFPET were performed in patients with acute and subacute stroke. In a voxel-based seed-growing technique, predefined CBFMRI thresholds (<40, <30, <20, <10 mL/100 g/min) were applied and the resulting volumes were compared with the hypoperfusion volume detected by the penumbral threshold (<20 mL/100 g/min) on CBFPET. The volumetric comparison was expressed as the C-ratio (volume CBFMRI/volume CBFPET) to identify the best MRI threshold. The influence of vessel pathology, hypoperfusion size, and time point of imaging was described. The proportion of voxels correctly classified as hypoperfused and the proportion of voxel correctly classified as nonhypoperfused of the best CBFMRI threshold was calculated and a Bland-Altman plot illustrated the method-specific differences. Results— In 24 patients (median time MRI to PET: 68 minutes; 16 patients imaged within 24 hours after stroke), the median volume of hypoperfusion <20 mL/100 g/min (CBFPET) was 78.5 cm3. Median hypoperfusion volume on CBFMRI ranged from 245.9 cm3 (<40 mL/100 g/min) to 35.5 cm3 (<10 mL/10 g/min). On visual inspection, an excellent qualitative congruence was found. The quantitative congruence was best for the MRI-CBF threshold <20 mL/100 g/min (median C-ratio: 1.0), reaching a proportion of voxels correctly classified as hypoperfused of 76% and a proportion of voxel correctly classified as nonhypoperfused of 96%, but a wide interindividual range (C-ratio 0.3 to 3.5) was found. Ipsilateral vessel pathology, time point of imaging, and size of hypoperfusion did not significantly influence the C-ratio. The Bland-Altman analysis for the volumetric difference of CBFMRI and CBFPET found a good overall agreement but a large SD. Conclusion— Hypoperfusion areas below the CBFPET penumbral threshold can be well identified by the CBFMRI threshold <20 mL/10 g/min at a group level, but a large individual variance (exceeding 20% of volume in nearly half of the patients) could not be explained. Our results support a prudent use of MRI-based quantitative CBF measurement in clinical routine.

MRI Perfusion Maps in Acute Stroke Validated With 15O-Water Positron Emission Tomography

Background and Purpose— Perfusion-weighted imaging maps are used to identify hypoperfusion in acute ischemic stroke. We evaluated maps of cerebral blood flow (CBF), cerebral blood volume, mean transit time, and time to peak (TTP) in acute stroke by comparison with positron emission tomography. Methods— Perfusion-weighted imaging and positron emission tomography were performed in 26 patients with acute ischemic stroke (median 18.5 hours after stroke onset, 65 minutes between MRI and positron emission tomography). The perfusion-weighted imaging-derived maps of CBF, cerebral blood volume, mean transit time, and TTP delay were compared with quantitative positron emission tomography CBF. A receiver-operating characteristic curve analysis identified the best perfusion-weighted imaging map and threshold to identify hypoperfusion <20 mL/100 g/min, a widely used measure of penumbral flow. Results— Individual regression analysis of positron emission tomography CBF and perfusion-weighted imaging values were strong for CBF and TTP delay and weaker for mean transit time and cerebral blood volume, but the pooled analysis showed a large variance. Receiver-operating characteristic curve analysis identified TTP and CBF maps as most predictive (median area under the curve=0.94 and 0.93). Penumbral flow thresholds were <21.7 mL/100 g/min (CBF), <1.5 mL/100 g (cerebral blood volume), >5.3 seconds (mean transit time), and >4.2 seconds (TTP). TTP and CBF maps reached sensitivity/specificity values of 91%/82% and 89%/87%. Conclusion— In our sample, maps of CBF, TTP, and mean transit time yielded a good estimate of penumbral flow. The performance of TTP maps was equivalent to deconvolution techniques using an arterial input function. For all maps, the application of a predefined threshold is mandatory and calibration studies will enhance their use in acute stroke therapy as well as in clinical stroke trials.

A simple positron emission tomography-based calibration for perfusion-weighted magnetic resonance maps to optimize penumbral flow detection in acute stroke.

Background and Purpose— Perfusion-weighted (PW) MRI is increasingly used to identify the tissue at risk. The adequate PW-MRI map and threshold remain controversial due to a considerable individual variation of values. By comparative positron emission tomography, we evaluated a simple MR-based and positron emission tomography-validated calibration of PW maps. Methods— PW-MRI and quantitative positron emission tomography (15O-water) of patients with acute stroke were used to calculate averaged as well as individual thresholds of penumbral flow (positron emission tomography cerebral blood flow (<20 mL/100 g/min) for maps of time to peak, mean transit time, cerebral blood flow, and cerebral blood volume. A linear regression analysis studied the variability of the individual thresholds using 3 different PW reference regions (hemispheric, white matter, gray matter). The best model was used for volumetric analysis to compare averaged and scaled individual thresholds and to calculate look-up tables for PW maps. Results— In 26 patients, the averaged thresholds were (median/interquartile range): cerebral blood flow 21.7 mL/100 g/min (19.9 to 32); cerebral blood volume 1.5 mL/100 g (0.9 to 1.8); mean transit time seconds 5.2 (3.9 to 6.9); and relative time to peak 4.2 seconds (2.8 to 5.8). The large individual variability was best explained by the mean value of the hemispheric reference derived from a region of interest on a level with the basal ganglia of the unaffected hemisphere (R2: cerebral blood flow 0.76, cerebral blood volume 0.55, mean transit time 0.83, time to peak 0.95). Hemispheric reference-corrected thresholds clearly improved the detection of penumbral flow. Look-up tables were calculated to identify the individual thresholds according to the hemispheric reference value. Conclusion— The individual variation of PW values, even if calculated by deconvolution, remains a major obstacle in quantitative PW imaging and can be significantly improved by a simple MR-based calibration. Easily applicable look-up tables identify the individual best threshold for each PW map to optimize mismatch detection.

Influence of the Arterial Input Function on Absolute and Relative Perfusion-Weighted Imaging Penumbral Flow Detection A Validation With 15O-Water Positron Emission Tomography

Background and Purpose— Perfusion-weighted imaging maps are used to identify critical hypoperfusion in acute stroke. However, quantification of perfusion may depend on the choice of the arterial input function (AIF). Using quantitative positron emission tomography we evaluated the influence of the AIF location on maps of absolute and relative perfusion-weighted imaging to detect penumbral flow (PF; <20 mL/100 g/min on positron emission tomographyCBF) in acute stroke. Methods— In 22 patients with acute stroke the AIF was placed at 7 sites (M1, M2, M3 ipsi- and contralateral and internal carotid artery–M1 contralateral to the infarct). Comparative 15O-water positron emission tomography and AIF-dependent perfusion-weighted imaging (cerebral blood flow, cerebral blood volume, mean transit time, and time to maximum) were performed. A receiver operating characteristic curve analysis described the threshold independent performance (area under the curve) of the perfusion-weighted maps for all 7 AIF locations and identified the best AIF-dependent absolute and relative thresholds to identify PF. These results were compared with AIF-independent time-to-peak maps. Results— Quantitative perfusion-weighted imaging maps of cerebral blood flow and time to maximum performed best. For PF detection, AIF placement did significantly influence absolute PF thresholds. However, AIF placement did not influence (1) the threshold independent performance; and (2) the relative PF thresholds. AIF placement in the proximal segment of the contralateral middle cerebral artery (cM1) was preferable for quantification. Conclusions— AIF-based maps of cerebral blood flow and time to maximum were most accurate to detect the PF threshold. The AIF placement significantly altered absolute PF thresholds and showed best agreement with positron emission tomography for the cM1 segment. The performance of relative PF thresholds, however, was not AIF location-dependent and might be along with AIF-independent time-to-peak maps, more suitable than absolute PF thresholds in acute stroke if detailed postprocessing is not feasible.

DWI intensity values predict FLAIR lesions in acute ischemic stroke.

Background and Purpose In acute stroke, the DWI-FLAIR mismatch allows for the allocation of patients to the thrombolysis window (<4.5 hours). FLAIR-lesions, however, may be challenging to assess. In comparison, DWI may be a useful bio-marker owing to high lesion contrast. We investigated the performance of a relative DWI signal intensity (rSI) threshold to predict the presence of FLAIR-lesions in acute stroke and analyzed its association with time-from-stroke-onset. Methods In a retrospective, dual-center MR-imaging study we included patients with acute stroke and time-from-stroke-onset ≤12 hours (group A: n = 49, 1.5T; group B: n = 48, 3T). DW- and FLAIR-images were coregistered. The largest lesion extent in DWI defined the slice for further analysis. FLAIR-lesions were identified by 3 raters, delineated as regions-of-interest (ROIs) and copied on the DW-images. Circular ROIs were placed within the DWI-lesion and labeled according to the FLAIR-pattern (FLAIR+ or FLAIR−). ROI-values were normalized to the unaffected hemisphere. Adjusted and nonadjusted receiver-operating-characteristics (ROC) curve analysis on patient level was performed to analyze the ability of a DWI- and ADC-rSI threshold to predict the presence of FLAIR-lesions. Spearman correlation and adjusted linear regression analysis was performed to assess the relationship between DWI-intensity and time-from-stroke-onset. Results DWI-rSI performed well in predicting lesions in FLAIR-imaging (mean area under the curve (AUC): group A: 0.84; group B: 0.85). An optimal mean DWI-rSI threshold was identified (A: 162%; B: 161%). ADC-maps performed worse (mean AUC: A: 0.58; B: 0.77). Adjusted regression models confirmed the superior performance of DWI-rSI. Correlation coefficents and linear regression showed a good association with time-from-stroke-onset for DWI-rSI, but not for ADC-rSI. Conclusion An easily assessable DWI-rSI threshold identifies the presence of lesions in FLAIR-imaging with good accuracy and is associated with time-from-stroke-onset in acute stroke. This finding underlines the potential of a DWI-rSI threshold as a marker of lesion age.

Clinical-Radiological Parameters Improve the Prediction of the Thrombolysis Time Window by Both MRI Signal Intensities and DWI-FLAIR Mismatch

Background: With regard to acute stroke, patients with unknown time from stroke onset are not eligible for thrombolysis. Quantitative diffusion weighted imaging (DWI) and fluid attenuated inversion recovery (FLAIR) MRI relative signal intensity (rSI) biomarkers have been introduced to predict eligibility for thrombolysis, but have shown heterogeneous results in the past. In the present work, we investigated whether the inclusion of easily obtainable clinical-radiological parameters would improve the prediction of the thrombolysis time window by rSIs and compared their performance to the visual DWI-FLAIR mismatch. Methods: In a retrospective study, patients from 2 centers with proven stroke with onset <12 h were included. The DWI lesion was segmented and overlaid on ADC and FLAIR images. rSI mean and SD, were calculated as follows: (mean ROI value/mean value of the unaffected hemisphere). Additionally, the visual DWI-FLAIR mismatch was evaluated. Prediction of the thrombolysis time window was evaluated by the area-under-the-curve (AUC) derived from receiver operating characteristic (ROC) curve analysis. Factors such as the association of age, National Institutes of Health Stroke Scale, MRI field strength, lesion size, vessel occlusion and Wahlund-Score with rSI were investigated and the models were adjusted and stratified accordingly. Results: In 82 patients, the unadjusted rSI measures DWI-mean and -SD showed the highest AUCs (AUC 0.86-0.87). Adjustment for clinical-radiological covariates significantly improved the performance of FLAIR-mean (0.91) and DWI-SD (0.91). The best prediction results based on the AUC were found for the final stratified and adjusted models of DWI-SD (0.94) and FLAIR-mean (0.96) and a multivariable DWI-FLAIR model (0.95). The adjusted visual DWI-FLAIR mismatch did not perform in a significantly worse manner (0.89). ADC-rSIs showed fair performance in all models. Conclusions: Quantitative DWI and FLAIR MRI biomarkers as well as the visual DWI-FLAIR mismatch provide excellent prediction of eligibility for thrombolysis in acute stroke, when easily obtainable clinical-radiological parameters are included in the prediction models.

MRI-based mismatch detection in acute ischemic stroke: Optimal PWI maps and thresholds validated with PET

Perfusion-weighted (PW) magnetic resonance imaging (MRI) is used to detect penumbral tissue in acute stroke, but the selection of optimal PW-maps and thresholds for tissue at risk detection remains a matter of debate. We validated the performance of PW-maps with 15O-water-positron emission tomography (PET) in a large comparative PET-MR cohort of acute stroke patients. In acute and subacute stroke patients with back-to-back MRI and PET imaging, PW-maps were validated with 15O-water-PET. We pooled two different cerebral blood flow (CBF) PET-maps to define the critical flow (CF) threshold, (i) quantitative (q)CBF-PET with the CF threshold <20 ml/100 g/min and (ii) normalized non-quantitative (nq)CBF-PET with a CF threshold of <70% (corresponding to <20 ml/100 g/min according to a previously published normogram). A receiver operating characteristic (ROC) curve analysis was performed to specify the accuracy and the optimal critical flow threshold of each PW-map as defined by PET. In 53 patients, (stroke to imaging: 9.8 h; PET to MRI: 52 min) PW-time-to-maximum (Tmax) with a threshold >6.1 s (AUC = 0.94) and non-deconvolved PW-time-to-peak (TTP) >4.8 s (AUC = 0.93) showed the best performance to detect the CF threshold as defined by PET. PW-Tmax with a threshold >6.1 s and TTP with a threshold >4.8 s are the most predictive in detecting the CF threshold for MR-based mismatch definition.

Comparison of the 2 Most Popular Deconvolution Techniques for the Detection of Penumbral Flow in Acute Stroke

Background and Purpose— Dynamic susceptibility–weighted contrast–enhanced (DSC) magnetic resonance imaging (MRI) is used to identify the tissue-at-risk in acute stroke, but the choice of optimal DSC postprocessing in the clinical setting remains a matter of debate. Using 15O-water positron emission tomography (PET), we validated the performance of 2 common deconvolution methods for DSC-MRI. Methods— In (sub)acute stroke patients with consecutive MRI and PET imaging, DSC maps were calculated applying 2 deconvolution methods, standard and block-circulant single value decomposition. We used 2 standardized analysis methods, a region of interest–based and a voxel-based analysis, where PET cerebral blood flow masks of <20 mL/100 g per minute (penumbral flow) and gray matter masks were overlaid on DSC parameter maps. For both methods, receiver operating characteristic curve analysis was performed to identify the accuracy of each DSC-MR map for the detection of PET penumbral flow. Results— In 18 data sets (median time after stroke onset: 18 hours; median time PET to MRI: 101 minutes), block-circulant single value decomposition showed significantly better performance to detect PET penumbral flow only for mean transit time maps. Time-to-maximum (Tmax) had the highest performance independent of the deconvolution method. Conclusions— Block-circulant single value decomposition seems only significantly beneficial for mean transit time maps in (sub)acute stroke. Tmax is likely the most stable deconvolved parameter for the detection of tissue-at-risk using DSC-MRI.

Multiparametric Model for Penumbral Flow Prediction in Acute Stroke

Background and Purpose— Identification of salvageable penumbra tissue by dynamic susceptibility contrast magnetic resonance imaging is a valuable tool for acute stroke patient stratification for treatment. However, prior studies have not attempted to combine the different perfusion maps into a predictive model. In this study, we established a multiparametric perfusion imaging model and cross-validated it using positron emission tomography perfusion for detection of penumbral flow. Methods— In a retrospective analysis of 17 subacute stroke patients with consecutive magnetic resonance imaging and H2O15 positron emission tomography scans, perfusion maps of cerebral blood flow, cerebral blood volume, mean transit time, time-to-maximum, and time-to-peak were constructed and combined using a generalized linear model (GLM). Both the GLM maps and the single perfusion maps alone were cross-validated with positron emission tomography-cerebral blood flow scans to predict penumbral flow on a voxel-wise level. Performance was tested by receiver-operating characteristics curve analysis, that is, the area under the curve, and the models’ fits were compared using the likelihood ratio test. Results— The GLM demonstrated significantly improved model fit compared with each of the single perfusion maps (P<1×e-5) and demonstrated higher performance, with an area under the curve of 0.91. However, the absolute difference between the performance of GLM and the best-performing single perfusion parameter (time-to-maximum) was relatively low (area under the curve difference =0.04). Conclusions— Our results support a dynamic susceptibility contrast magnetic resonance imaging–based GLM as an improved model for penumbral flow prediction in stroke patients. With given perfusion maps, this model is a straightforward and observer-independent alternative for therapy stratification.