Effect of IV Thrombolysis With Alteplase in Patients With Vessel Occlusion in the WAKE-UP Trial.

Background And Objectives: Data from randomized trials on the treatment effect of pure thrombolysis in patients with vessel occlusion are lacking. We examined data from a corresponding subsample of patients from the multicenter, randomized, placebo-controlled WAKE-UP trial to determine whether MRI-guided IV thrombolysis with alteplase in unknown-onset ischemic stroke benefits patients presenting with vessel occlusion.

Methods: Patients with an acute ischemic lesion visible on MRI diffusion-weighted imaging but no marked parenchymal hyperintensity on fluid-attenuated inversion recovery images were randomized to treatment with IV alteplase or placebo.

Non-invasive Assessment of Cerebral Hemodynamics Using Resting-State Functional Magnetic Resonance Imaging in Multiple Sclerosis and Age-Related White Matter Lesions.

Perfusion changes in white matter (WM) lesions and normal-appearing brain regions play an important pathophysiological role in multiple sclerosis (MS). However, most perfusion imaging methods require exogenous contrast agents, the repeated use of which is discouraged. Using resting-state functional MRI (rs-fMRI), we aimed to investigate differences in perfusion between white matter lesions and normal-appearing brain regions in MS and healthy participants.

An Artificial Intelligence Algorithm Integrated into the Clinical Workflow Can Ensure High Quality Acute Intracranial Hemorrhage CT Diagnostic.

Purpose: Intracranial hemorrhage (ICH) is a life-threatening condition requiring rapid diagnostic and therapeutic action. This study evaluates whether Artificial intelligence (AI) can provide high-quality ICH diagnostics and turnaround times suitable for routine radiological practice.

Methods: A convolutional neural network (CNN) was trained and validated to detect ICHs on DICOM images of cranial CT (CCT) scans, utilizing about 674,000 individually labeled slices.

Generative adversarial network-based reconstruction of healthy anatomy for anomaly detection in brain CT scans.

Purpose: To help radiologists examine the growing number of computed tomography (CT) scans, automatic anomaly detection is an ongoing focus of medical imaging research. Radiologists must analyze a CT scan by searching for any deviation from normal healthy anatomy. We propose an approach to detecting abnormalities in axial 2D CT slice images of the brain.