Synopsis

MRI offers an unrivaled opportunity to noninvasively examine the structure and function of the human brain. Yet, MRI exams are hindered by limitations on quality and diversity of acquired images due to scan time considerations. Classical approaches to acquisition and processing of imaging data often fail to address these limitations. In this talk, the potential role of machine learning in surpassing these fundamental barriers will be discussed. Novel deep learning techniques for image reconstruction, image synthesis and sampling design will be showcased. State-of-the-art results from these techniques indicate a bright future for machine learning in rapid, high-quality and high-sensitivity neuroimaging.