Anna Matsulevits

Generating Synthetic MR Perfusion Maps from DWI and FLAIR in Acute Ischemic Stroke: Development and External Validation of a Deep Learning Model

This paper develops and validates a deep learning model to generate synthetic T-max perfusion maps from non-contrast MRI data in acute stroke patients.

DeepDisco: A Deep Learning Tool for Rapid Brain Connectivity Estimation

DeepDisco predicts brain connectivity maps from binary regions or lesions using deep learning, providing a scalable and accessible tool for large datasets.

A global effort to benchmark predictive models and reveal mechanistic diversity in long-term stroke outcomes

This study benchmarks predictive models for long-term stroke outcomes using a global consortium, revealing mechanistic diversity and key predictors across motor, cognitive, and emotional domains.

Deep Learning disconnectomes to accelerate and improve long-term predictions for post-stroke symptoms

This paper uses deep learning to generate disconnectomes from stroke lesion masks, achieving faster computation and better prediction of post-stroke cognitive and behavioral outcomes.