Nils Daniel Forkert

Influence of Stroke Infarct Location on Functional Outcome Measured by the Modified Rankin Scale

Background and Purpose— In the early days after ischemic stroke, information on structural brain damage from MRI supports prognosis of functional outcome. It is rated widely by the modified Rankin Scale that correlates only moderately with lesion volume. We therefore aimed to elucidate the influence of lesion location from early MRI (days 2–3) on functional outcome after 1 month using voxel-based lesion symptom mapping. Methods— We analyzed clinical and MRI data of patients from a prospective European multicenter stroke imaging study (I-KNOW). Lesions were delineated on fluid-attenuated inversion recovery images on days 2 to 3 after stroke onset. We generated statistic maps of lesion contribution related to clinical outcome (modified Rankin Scale) after 1 month using voxel-based lesion symptom mapping. Results— Lesion maps of 101 patients with middle cerebral artery infarctions were included for analysis (right-sided stroke, 47%). Mean age was 67 years, median admission National Institutes of Health Stroke Scale was 11. Mean infarct volumes were comparable between both sides (left, 37.5 mL; right, 43.7 mL). Voxel-based lesion symptom mapping revealed areas with high influence on higher modified Rankin Scale in regions involving the corona radiata, internal capsule, and insula. In addition, asymmetrically distributed impact patterns were found involving the right inferior temporal gyrus and left superior temporal gyrus. Conclusions— In this group of patients with stroke, characteristic lesion patterns in areas of motor control and areas involved in lateralized brain functions on early MRI were found to influence functional outcome. Our data provide a novel map of the impact of lesion localization on functional stroke outcome as measured by the modified Rankin Scale.

Quantitative measurements of relative fluid-attenuated inversion recovery (FLAIR) signal intensities in acute stroke for the prediction of time from symptom onset

In acute stroke magnetic resonance imaging, a ‘mismatch’ between visibility of an ischemic lesion on diffusion-weighted imaging (DWI) and missing corresponding parenchymal hyperintensities on fluid-attenuated inversion recovery (FLAIR) data sets was shown to identify patients with time from symptom onset ≤4.5 hours with high specificity. However, moderate sensitivity and suboptimal interpreter agreement are limitations of a visual rating of FLAIR lesion visibility. We tested refined image analysis methods in patients included in the previously published PREFLAIR study using refined visual analysis and quantitative measurements of relative FLAIR signal intensity (rSI) from a three-dimensional, segmented stroke lesion volume. A total of 399 patients were included. The rSI of FLAIR lesions showed a moderate correlation with time from symptom onset (r = 0.382, P < 0.001). A FLAIR rSI threshold of <1.0721 predicted symptom onset ≤4.5 hours with slightly increased specificity (0.85 versus 0.78) but also slightly decreased sensitivity (0.47 versus 0.58) as compared with visual analysis. Refined visual analysis differentiating between ‘subtle’ and ‘obvious’ FLAIR hyperintensities and classification and regression tree algorithms combining information from visual and quantitative analysis also did not improve diagnostic accuracy. Our results raise doubts whether the prediction of stroke onset time by visual image judgment can be improved by quantitative rSI measurements.

Territorial and Microvascular Perfusion Impairment in Brain Arteriovenous Malformations

BACKGROUND AND PURPOSE: Both the existence and clinical relevance of a steal phenomenon in brain arteriovenous malformations (AVMs) remains a matter of debate. This study aimed to assess perfusion in the brain adjacent to brain AVMs and to relate these to macrovascular blood flow in a single measurement. MATERIALS AND METHODS: Twenty consecutive patients with AVMs with a median age of 37 years were evaluated by 3T MR imaging by using 3D time-resolved MR angiography to determine blood flow and perfusion patterns. Cerebral perfusion was estimated by using an arterial spin-labeling technique in vascular territories around the nidus and in symmetric regions of interest in the ipsilateral and contralateral hemispheres. Mapping of concentric shells around the nidus was used to define the immediate and adjacent brain and relative perfusion reductions >20% of baseline, termed perinidal dip (PND). RESULTS: A significant reduction in perfusion ratios between ipsilateral and contralateral hemispheres remote to the AVMs was demonstrated in the middle and posterior cerebral artery territories. PND was detected in 5 patients, and 17 patients overall showed reduced perfusion in the perinidal region on visual inspection. There was a negative correlation of the hemispheric territorial perfusion with the affected/nonaffected inflow time ratio (R = −0.402, P = .015). CONCLUSIONS: The perfusion impairment in vascular territories adjacent to brain AVMs that we identified as PND may reflect the existence of 2 levels of perfusion impairment: a territorial and a microvascular perfusion disturbance. Although the hemispheric asymmetry in territorial perfusion seems the result of arterioarterial redistribution, the PND was inhomogeneously distributed within a single vascular territory and thus might result from low perfusion pressure in small arteries and arterioles.

3D cerebrovascular segmentation combining fuzzy vessel enhancement and level-sets with anisotropic energy weights

The aim of this work is to present and evaluate a level-set segmentation approach with vesselness-dependent anisotropic energy weights, which focuses on the exact segmentation of malformed as well as small vessels from time-of-flight (TOF) magnetic resonance angiography (MRA) datasets. In a first step, a vesselness filter is used to calculate the vesselness dataset, which quantifies the likeliness of each voxel to belong to a bright tubular-shaped structure and estimate the corresponding vessel directions from a given TOF dataset. The vesselness and TOF datasets are then combined using fuzzy-logic and used for initialization of a variational level-set method. The proposed level-set model has been extended in a way that the weight of the internal energy is locally adapted based on the vessel direction information. Here, the main idea is to weight the internal energy lower if the gradient direction of the level-set is similar to the direction of the eigenvector extracted by the vesselness filter. Furthermore, an additional vesselness force has been integrated in the level-set formulation. The proposed method was evaluated based on ten TOF MRA datasets from patients with an arteriovenous malformation. Manual segmentations from two observers were available for each dataset and used for quantitative comparison. The evaluation revealed that the proposed method yields significantly better segmentation results than four other state-of-the-art segmentation methods tested. Furthermore, the segmentation results are within the range of the inter-observer variation. In conclusion, the proposed method allows an improved delineation of small vessels, especially of those represented by low intensities and high surface curvatures.

Time-Dependent Computed Tomographic Perfusion Thresholds for Patients With Acute Ischemic Stroke

Background and Purpose— Among patients with acute ischemic stroke, we determine computed tomographic perfusion (CTP) thresholds associated with follow-up infarction at different stroke onset-to-CTP and CTP-to-reperfusion times. Methods— Acute ischemic stroke patients with occlusion on computed tomographic angiography were acutely imaged with CTP. Noncontrast computed tomography and magnectic resonance diffusion–weighted imaging between 24 and 48 hours were used to delineate follow-up infarction. Reperfusion was assessed on conventional angiogram or 4-hour repeat computed tomographic angiography. Tmax, cerebral blood flow, and cerebral blood volume derived from delay-insensitive CTP postprocessing were analyzed using receiver–operator characteristic curves to derive optimal thresholds for combined patient data (pooled analysis) and individual patients (patient-level analysis) based on time from stroke onset-to-CTP and CTP-to-reperfusion. One-way ANOVA and locally weighted scatterplot smoothing regression was used to test whether the derived optimal CTP thresholds were different by time. Results— One hundred and thirty-two patients were included. Tmax thresholds of >16.2 and >15.8 s and absolute cerebral blood flow thresholds of <8.9 and <7.4 mL·min−1·100 g−1 were associated with infarct if reperfused <90 min from CTP with onset <180 min. The discriminative ability of cerebral blood volume was modest. No statistically significant relationship was noted between stroke onset-to-CTP time and the optimal CTP thresholds for all parameters based on discrete or continuous time analysis (P>0.05). A statistically significant relationship existed between CTP-to-reperfusion time and the optimal thresholds for cerebral blood flow (P<0.001; r=0.59 and 0.77 for gray and white matter, respectively) and Tmax (P<0.001; r=−0.68 and −0.60 for gray and white matter, respectively) parameters. Conclusions— Optimal CTP thresholds associated with follow-up infarction depend on time from imaging to reperfusion.

ANTONIA perfusion and stroke. A software tool for the multi-purpose analysis of MR perfusion-weighted datasets and quantitative ischemic stroke assessment.

OBJECTIVES The objective of this work is to present the software tool ANTONIA, which has been developed to facilitate a quantitative analysis of perfusion-weighted MRI (PWI) datasets in general as well as the subsequent multi-parametric analysis of additional datasets for the specific purpose of acute ischemic stroke patient dataset evaluation. METHODS Three different methods for the analysis of DSC or DCE PWI datasets are currently implemented in ANTONIA, which can be case-specifically selected based on the study protocol. These methods comprise a curve fitting method as well as a deconvolution-based and deconvolution-free method integrating a previously defined arterial input function. The perfusion analysis is extended for the purpose of acute ischemic stroke analysis by additional methods that enable an automatic atlas-based selection of the arterial input function, an analysis of the perfusion-diffusion and DWI-FLAIR mismatch as well as segmentation-based volumetric analyses. RESULTS For reliability evaluation, the described software tool was used by two observers for quantitative analysis of 15 datasets from acute ischemic stroke patients to extract the acute lesion core volume, FLAIR ratio, perfusion-diffusion mismatch volume with manually as well as automatically selected arterial input functions, and follow-up lesion volume. The results of this evaluation revealed that the described software tool leads to highly reproducible results for all parameters if the automatic arterial input function selection method is used. CONCLUSION Due to the broad selection of processing methods that are available in the software tool, ANTONIA is especially helpful to support image-based perfusion and acute ischemic stroke research projects.

Fuzzy-based Vascular Structure Enhancement in Time-of-Flight MRA Images for Improved Segmentation

OBJECTIVES Cerebral vascular malformations might lead to strokes due to occurrence of ruptures. The rupture risk is highly related to the individual vascular anatomy. The 3D Time-of-Flight (TOF) MRA technique is a commonly used non-invasive imaging technique for exploration of the vascular anatomy. Several clinical applications require exact cerebrovascular segmentations from this image sequence. For this purpose, intensity-based segmentation approaches are widely used. Since small low-contrast vessels are often not detected, vesselness filter-based segmentation schemes have been proposed, which contrariwise have problems detecting malformed vessels. In this paper, a fuzzy logic-based method for fusion of intensity and vesselness information is presented, allowing an improved segmentation of malformed and small vessels at preservation of advantages of both approaches. METHODS After preprocessing of a TOF dataset, the corresponding vesselness image is computed. The role of the fuzzy logic is to voxel-wisely fuse the intensity information from the TOF dataset with the corresponding vesselness information based on an analytically designed rule base. The resulting fuzzy parameter image can then be used for improved cerebrovascular segmentation. RESULTS Six datasets, manually segmented by medical experts, were used for evaluation. Based on TOF, vesselness and fused fuzzy parameter images, the vessels of each patient were segmented using optimal thresholds computed by maximizing the agreement to manual segmentations using the Tanimoto coefficient. The results showed an overall improvement of 0.054 (fuzzy vs. TOF) and 0.079 (fuzzy vs. vesselness). Furthermore, the evaluation has shown that the method proposed yields better results than statistical Bayes classification. CONCLUSION The proposed method can automatically fuse the benefits of intensity and vesselness information and can improve the results of following cerebrovascular segmentations.

Persistent Hemodynamic Changes in Ruptured Brain Arteriovenous Malformations

Background and Purpose— Hemodynamic properties of brain arteriovenous malformations (AVMs) with risk factors for a future hemorrhage are essentially unknown. We hypothesized that AVMs with anatomic properties, which are associated with an increased rupture risk, exhibit different hemodynamic characteristics than those without these properties. Methods— Seventy-two consecutive patients with AVMs diagnosed by conventional angiography underwent MRI examination, including time-resolved 3-dimensional MR angiography. Signal-intensity curves derived from the time-resolved 3-dimensional MR angiography datasets were used to calculate relative blood flow transit times through the AVM nidus based on the time-to-peak parameter. For identification of characteristics associated with altered transit times, a multiple normal regression model was fitted with stepwise selection of the following regressors: intracranial hemorrhage, deep nidus location, infratentorial location, deep drainage, associated aneurysm, nidus size, draining venous stenosis, and number of draining veins. Results— A previous intracranial hemorrhage is the only characteristic that was associated with a significant alteration of the relative transit time, leading to an increase of 2.4 seconds (95% CI, 1.2–3.6 seconds;, P<0.001) without adjustment and 2.1 seconds (95% CI, 0.6–3.6 seconds; P=0.007) with adjustment for all other regressors considered. The association was independent of the bleeding age. Conclusion— Hemodynamic parameters do not seem useful for risk assessment of an AVM-related hemorrhage because only a previous AVM rupture leads to a significant and permanent alteration of the hemodynamic situation.

Automatic Brain Segmentation in Time-of-Flight MRA Images

OBJECTIVES Cerebral vascular malformations might, caused by ruptures, lead to strokes. The rupture risk depends to a great extent on the individual anatomy of the vasculature. The 3D Time-of-Flight (TOF) MRA technique is one of the most commonly used non-invasive imaging techniques to obtain knowledge about the individual vascular anatomy. Unfortunately TOF images exhibit drawbacks for segmentation and direct volume visualization of the vasculature. To overcome these drawbacks an initial segmentation of the brain tissue is required. METHODS After preprocessing of the data is applied the low-intensity tissues surrounding the brain are segmented using region growing. In a following step this segmentation is used to extract supporting points at the border of the brain for a graph-based contour extraction. Finally a consistency check is performed to identify local outliers which are corrected using non-linear registration. RESULTS A quantitative validation of the method proposed was performed on 18 clinical datasets based on manual segmentations. A mean Dice coefficient of 0.989 was achieved while in average 99.56% of all vessel voxels were included by the brain segmentation. A comparison to the results yielded by three commonly used tools for brain segmentation revealed that the method described achieves better results, using TOF images as input, which are within the inter-observer variability. CONCLUSION The method suggested allows a robust and automatic segmentation of brain tissue in TOF images. It is especially helpful to improve the automatic segmentation or direct volume rendering of the cerebral vascular system.

Comparison of 10 TTP and Tmax Estimation Techniques for MR Perfusion-Diffusion Mismatch Quantification in Acute Stroke

BACKGROUND AND PURPOSE: The mismatch between lesions identified in perfusion- and diffusion-weighted MR imaging is typically used to identify tissue at risk of infarction in acute stroke. The purpose of this study was to analyze the variability of mismatch volumes resulting from different time-to-peak or time-to-maximum estimation techniques used for hypoperfused tissue definition. MATERIALS AND METHODS: Data of 50 patients with middle cerebral artery stroke and intracranial vessel occlusion imaged within 6 hours of symptom onset were analyzed. Therefore, 10 different TTP/Tmax techniques and delay thresholds between +2 and +12 seconds were used for calculation of perfusion lesions. Diffusion lesions were semiautomatically segmented and used for mismatch quantification after registration. RESULTS: Mean volumetric differences up to 40 and 100 mL in individual patients were found between the mismatch volumes calculated by the 10 TTP/Tmax estimation techniques for typically used delay thresholds. The application of typical criteria for the identification of patients with a clinically relevant mismatch volume resulted in different mismatch classifications in ≤24% of all cases, depending on the TTP/Tmax estimation method used. CONCLUSIONS: High variations of tissue-at-risk volumes have to be expected when using different TTP/Tmax estimation techniques. An adaption of different techniques by using correction formulas may enable more comparable study results until a standard has been established by agreement.