In this project, we propose to develop and validate a self-contained machine-learning based tool for both predicting stroke tissue damage and reconstructing clinically interpretable perfusion maps.  We believe this technology has the potential to deliver faster, more accurate assessments of tissue-at-risk than current methods, with the potential to open endovascular treatment to many patients beyond the 6 hour window.

Responsible persons: 

Richard McKinley