Software for Automated Identification and Segmentation of Perivascular Spaces on MRI

Oregon Health & Science University Background
Increased areas of enlarged perivascular spaces (ePVS) are associated with many prevalent diseases, including Alzheimer’s disease, cerebral small vessel disease, cerebral amyloid angiopathy and multiple sclerosis. Analysis of ePVS is typically done by manual visual assessment of multiple MRI scans which is subjective and laborious.
Technology Overview
Oregon Health and Science University researchers have developed a fully-automated method of ePVS detection, with the following features:

Objective identification of ePVS for improved accuracy and reproducibility.
Faster ePVS analysis, as compared to manual assessment, allowing for rapid assessment of large datasets.
Object-based morphometric estimates of each ePVS, providing more data to end-users.
Compatible with standard clinical field-strength (3 Tesla) MRI scans, highlighting a large potential market of both clinical and research-focused MRI users.

Allows for objective and efficient ePVS detection in large datasets.
This algorithm could be incorporated into MRI software packages focusing on analysis of brain tissue.
This technology is available for licensing.

Related Blog

Smart, interactive desk

Get ready to take your space management game to the next level with the University of Glasgow’s innovative project! By combining the

Mechanical Hamstring™

University of Delaware Technology Overview This device was created to allow athletes who suffer a hamstring strain to return to the field

Join Our Newsletter

                                                   Receive Innovation Updates, New Listing Highlights And More