Effect of cerebral sinus venous thrombosis and its location on cerebral blood flow: a [O]water PET study in acute stroke patients compared to healthy volunteers.

Background: Symptoms in acute cerebral sinus venous thrombosis (CSVT) are highly variable, ranging from headaches to fatal stroke, and the basis for this high inter-individual variability is poorly understood. The present study aimed to assess whether acute CSVT significantly alters regional cerebral blood flow (CBF), if findings differ from CBF patterns know from large-artery occlusion in stroke, and whether the pattern of CBF alterations depends on clot location. Therefore, we retrospectively analyzed 12 patients with acute CSVT 10.

Impact of training data composition on the generalizability of convolutional neural network aortic cross-section segmentation in four-dimensional magnetic resonance flow imaging.

Background: Four-dimensional cardiovascular magnetic resonance flow imaging (4D flow CMR) plays an important role in assessing cardiovascular diseases. However, the manual or semi-automatic segmentation of aortic vessel boundaries in 4D flow data introduces variability and limits the reproducibility of aortic hemodynamics visualization and quantitative flow-related parameter computation. This paper explores the potential of deep learning to improve 4D flow CMR segmentation by developing models for automatic segmentation and analyzes the impact of the training data on the generalization of the model across different sites, scanner vendors, sequences, and pathologies.

Learning carotid vessel wall segmentation in black-blood MRI using sparsely sampled cross-sections from 3D data.

Purpose: Atherosclerosis of the carotid artery is a major risk factor for stroke. Quantitative assessment of the carotid vessel wall can be based on cross-sections of three-dimensional (3D) black-blood magnetic resonance imaging (MRI). To increase reproducibility, a reliable automatic segmentation in these cross-sections is essential.

Deep learning survival model predicts outcome after intracerebral hemorrhage from initial CT scan.

Background: Predicting functional impairment after intracerebral hemorrhage (ICH) provides valuable information for planning of patient care and rehabilitation strategies. Current prognostic tools are limited in making long term predictions and require multiple expert-defined inputs and interpretation that make their clinical implementation challenging. This study aimed to predict long term functional impairment of ICH patients from admission non-contrast CT scans, leveraging deep learning models in a survival analysis framework.