Automatic subarachnoid space segmentation and hemorrhage detection in clinical head CT scans.

Purpose: The subarachnoid space (SAS) lies between the arachnoid membrane and the pia mater of the human brain, normally filled with cerebrospinal fluid (CSF). Subarachnoid hemorrhage (SAH) is a serious complication of neurological disease that can have high mortality and high risk of disability. Computed tomography (CT) head scans are often used for diagnosing SAH which may be difficult when the hemorrhage is small or subtle.

Effect of baseline magnetic resonance imaging (MRI) apparent diffusion coefficient lesion volume on functional outcome in ischemic stroke.

Objective: We explored the relationship between predicted infarct core, predicted ischemic penumbras and predicted final infarct volumes obtained though apparent diffusion coefficient (ADC)-based method, as well as other clinical variables, and functional outcome.

Methods: Patients with acute cerebral ischemic stroke were retrospectively recruited. The National Institutes of Health Stroke Scale score was evaluated at baseline and the modified Rankin Scale (mRS) at day 90.

Prediction of infarct core and salvageable ischemic tissue volumes by analyzing apparent diffusion coefficient without intravenous contrast material.

Rationale And Objectives: To investigate whether baseline apparent diffusion coefficient (ADC) maps can be employed to predict both infarct core and salvageable ischemic tissue volumes in acute ischemic stroke.

Materials And Methods: An automated image analysis system based on baseline ADC maps was tested against 30 patients with acute ischemic stroke of anterior circulation to predict both infarct core and salvageable ischemic tissue volumes. The predicted infarct core and predicted salvageable ischemic tissue were quantitatively and qualitatively compared with follow-up imaging data in recanalization and no recanalization groups, respectively.

Knowledge-driven automated extraction of the human cerebral ventricular system from MR images.

This work presents an efficient and automated method to extract the human cerebral ventricular system from MRI driven by anatomic knowledge. The ventricular system is divided into six three-dimensional regions; six ROIs are defined based on the anatomy and literature studies regarding variability of the cerebral ventricular system. The distribution histogram of radiological properties is calculated in each ROI, and the intensity thresholds for extracting each region are automatically determined.