This project aimed to develop practical tools to help researchers more effectively detect changes in brain perfusion associated with accelerated aging and, ultimately, cognitive decline. Changes in brain perfusion can be an early indicator of neurological conditions, but analyzing such data is often technically complex and not easily accessible to all
researchers. To address this, the project focused on developing user-friendly, reliable methods to simplify the process.
The main outcome of the project was the development of both feature-based and deep-learning–based tools for estimating “BrainAge” from brain perfusion MRI data. In addition, a graphical user interface (GUI) was created to make these tools accessible to non-expert users. By integrating these methods with the existing ExploreASL framework for
perfusion-MRI data processing, the project enables clinical researchers to apply advanced analysis techniques directly to unprocessed MRI data, reducing the need for specialized technical expertise.
The impact of this project lies in lowering the barrier for researchers to use advanced neuroimaging analysis methods. It has the potential to change how clinical researchers work with perfusion data by making BrainAge estimation more accessible and scalable. While some objectives were refined during the project, and clinical validation results are still being finalized, the core goals have been successfully achieved.
The primary target audience includes clinical researchers in neuroimaging, particularly those working with ASL data. These groups have been engaged through workshops and direct outreach. Moving forward, the next steps include finalizing clinical validation, expanding adoption, and continuing development of the tools.
