Time is a fundamental factor during stroke treatments. A fast, automatic approach that segments the ischemic regions helps treatment decisions. In clinical use today, a set of color-coded parametric maps generated from computed tomography perfusion (CTP) images are investigated manually to decide a treatment plan. We propose an automatic method based on a neural network using a set of parametric maps to segment the two ischemic regions (core and penumbra) in patients affected by acute ischemic stroke. Our model is based on a convolution-deconvolution bottleneck structure with multi-input and slow fusion. To address the data imbalance issue, a loss function based on the focal Tversky index is used. The proposed architecture demonstrates effective performance and results comparable to the ground truth annotated by neuroradiologists. A Dice coefficient of 0.81 for penumbra and 0.52 for core over the large vessel occlusion test set is achieved. The full implementation is available at: *************

Objective: Computed tomography (CT) scan is a fast and widely used modality for early assessment in patients with symptoms of a cerebral ischemic stroke. CT perfusion (CTP) is often added to the protocol and is used by radiologists for assessing the severity of the stroke. Standard parametric maps are calculated from the CTP datasets. Based on parametric value combinations, ischemic regions are separated into presumed infarct core (irreversibly damaged tissue) and penumbra (tissue-at-risk). Different thresholding approaches have been suggested to segment the parametric maps into these areas. The purpose of this study is to compare fully-automated methods based on machine learning and thresholding approaches to segment the hypoperfused regions in patients with ischemic stroke. Methods: We test two different architectures with three mainstream machine learning algorithms. We use parametric maps as input features, and manual annotations made by two expert neuroradiologists as ground truth. Results: The best results are produced with random forest (RF) and Single-Step approach; we achieve an average Dice coefficient of 0.68 and 0.26, respectively for penumbra and core, for the three groups analysed. We also achieve an average in volume difference of 25.1ml for penumbra and 7.8ml for core. Conclusions: Our best RFbased method outperforms the classical thresholding approaches, to segment both the ischemic regions in a group of patients regardless of the severity of vessel occlusion. Significance: A correct visualization of the ischemic regions will guide treatment decisions better.

Background. The main complications after endovascular therapy of intracranial aneurysms are aneurysm rupture and thromboembolic events. Yet, the widespread use of magnetic resonance imaging (MRI) in follow-up of these patients also demonstrates other, rarely known complications such as aseptic meningitis and foreign body reaction. Case Presentation. A small aneurysm in the right posterior communicating artery was treated with endovascular therapy in a 65 year old woman. Two weeks after successful interventional treatment, the patient developed a headache. On MRI performed five months after intervention, vasogenic edema was seen in the vascular territory of the right internal carotid artery. The edema and the symptoms diminished without specific treatment within a year. Interpretation. The clinical and radiological presentation of this case are suggestive of a foreign body reaction, a treatable condition that radiologists and clinicians should be aware of.

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