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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.

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.

Acute stroke is a major cause for death and disability among adults in the western hemisphere. Time-resolved perfusion-weighted (PWI) and diffusion-weighted (DWI) MR datasets are typically used for the estimation of tissue-at-risk, which is an important variable for acute stroke therapy decision-making. Although several parameters, which can be estimated based on PWI concentration curves, have been proposed for tissue-at-risk definition in the past, the time-to-peak (TTP) or time-to-max (Tmax) parameter is used most frequently in recent trials. Unfortunately, there is no clear consensus which method should be used for estimation of Tmax or TTP maps. Consequently, tissue-at-risk estimations and following treatment decision might vary considerably with the method used. In this work, 5 PWI datasets of acute stroke patients were used to calculate TTP or Tmax maps using 10 different estimation techniques. The resulting maps were segmented using a typical threshold of +4s and the corresponding PWI-lesions were calculated. The first results suggest that the TTP or Tmax method used has a major impact on the resulting tissue-at-risk volume. Numerically, the calculated volumes differed up to a factor of 3. In general, the deconvolution-based Tmax techniques estimate the ischemic penumbra rather smaller compared to direct TTP based techniques. In conclusion, the comparison of different methods for TTP or Tmax estimation revealed high variations regarding the resulting tissue-at-risk volume, which might lead to different therapy decisions. Therefore, a consensus how TTP or Tmax maps should be calculated seems necessary.

Exact cerebrovascular segmentations based on high resolution 3D anatomical datasets are required for many clinical applications. A general problem of most vessel segmentation methods is the insufficient delineation of small vessels, which are often represented by rather low intensities and high surface curvatures. This paper describes an improved direction-dependent level set approach for the cerebrovascular segmentation. The proposed method utilizes the direction information of the eigenvectors computed by vesselness filters for adjusting the weights of the internal energy depending on the location. The basic idea of this is to weight the internal energy lower in case the gradient of the level set is comparable to the direction of the eigenvector extracted by the vesselness filter. A quantitative evaluation of the proposed method based on three clinical Time-of-Flight MRA datasets with available manual segmentations using the Tanimoto coefficient showed that a mean improvement compared to the initial segmentation of 0.081 is achieved, while the corresponding level set segmentation without integration of direction information does not lead to satisfying results. In summary, the proposed method enables an improved delineation of small vessels, especially of those represented by low intensities and high surface curvatures.