Conjoined Pathways: Unraveling the Coexistence of Developmental Venous Anomaly With Arteriovenous Malformation.

Developmental venous anomalies (DVAs) are intracranial vascular malformations typically characterized by their benign nature, often obviating the need for radiological follow-up. These anomalies arise from variations in the standard drainage pattern. While previously deemed congenital, there has been ongoing debate about a developmental component contributing to their etiology.

A lightweight asymmetric U-Net framework for acute ischemic stroke lesion segmentation in CT and CTP images.

Background And Objectives: This paper has introduced a patch-based, residual, asymmetric, encoder-decoder CNN that solves two major problems in acute ischemic stroke lesion segmentation from CT and CT perfusion data using deep neural networks. First, the class imbalance is encountered since the lesion core size covers less than 5% of the volume of the entire brain. Second, deeper neural networks face the drawback of vanishing gradients, and this degrades the learning ability of the network.

Computed tomographic and digital subtraction angiography evaluation of ophthalmic-ethmoidal artery dural arteriovenous fistula.

Ophthalmic-ethmoidal dural arteriovenous fistula (DAVFs) is a rare type of dural arteriovenous fistulas and usually presenting with spontaneous subarachnoid hemorrhage, subdural hemorrhage or ocular symptoms. We present a case of a 59-year old gentleman presenting with acute headache, vomiting and generalized weakness. CT study of the brain revealed a large left frontal hematoma and abnormal aneurysmal sac with dilated cortical vein, communicating with the superior sagittal sinus.

CSNet: A new DeepNet framework for ischemic stroke lesion segmentation.

Background And Objectives: Acute stroke lesion segmentation is of paramount importance as it can aid medical personnel to render a quicker diagnosis and administer consequent treatment. Automation of this task is technically exacting due to the variegated appearance of lesions and their dynamic development, medical discrepancies, unavailability of datasets, and the requirement of several MRI modalities for imaging. In this paper, we propose a composite deep learning model primarily based on the self-similar fractal networks and the U-Net model for performing acute stroke diagnosis tasks automatically to assist as well as expedite the decision-making process of medical practitioners.

Noncontrast Computed Tomography versus Computed Tomography Angiography Source Images for Predicting Final Infarct Size in Anterior Circulation Acute Ischemic Stroke: a Prospective Cohort Study.

Background: There has been a recent debate regarding the superiority of computed tomography angiography source images (CTASIs) over noncontrast computed tomography (NCCT) to predict the final infarct size in acute ischemic stroke (AIS). We hypothesized that the parenchymal abnormality on CTASI in faster scanners would overestimate ischemic core.

Methods: This prospective study assessed the correlation of Alberta Stroke Program Early CT Score (ASPECTS) on NCCT, CTASI, and computed tomography perfusion (CTP) with final infarct size in patients within 8 hours of AIS.