Principal Investigators: Susumu Mori, PhD; Brian Caffo, PhD; Jeremias Sulam, PhD
In the past two decades, we have witnessed remarkable advances in image acquisition, processing, and analysis technologies for brain MRI. As we enter a new decade, however, there remain several key areas in combining information across the macro-meso-micro scales, and discovering predictive models for which we require significant advances in existing tools and technologies. One of the significant opportunities going forward is to leverage the rapidly evolving data science technologies which are now emerging and opening new frontiers for researchers. This will require advances not only in collections of software to analyze each dimension but also a new generalized framework to integrate and explore the data. Another important development in recent years is the surge of heavily data-driven approaches such as deep learning. We believe this is a great time to invest time and resources to evaluate their capability by comparing them with conventional engineering approaches. More importantly, there is great potential in combining these two approaches to test improvements in precision, accuracy, and/or efficiency.
Deep Learning Approaches in Image Data Acquisition and Analysis
Integrate and analyze complex multi-modal and multi-scale imaging data
Integration of Deep Learning Approaches in Brain Mapping