Synopsis

Keywords: Image acquisition: Machine learning, Image acquisition: Reconstruction, Image acquisition: Image processing

MRI offers an unrivaled opportunity to examine the structure and function of biological tissues. Yet, MRI exams are hindered by limitations on quality and diversity of acquired images due to scan time considerations. Classical approaches to processing of imaging data often fail to address these limitations. In this talk, advanced machine learning techniques that help surpass these fundamental barriers will be discussed. Recent technical developments will be showcased ranging from architectural leaps with the introduction of vision transformers to collaborative leaps with adoption of federated learning frameworks. State-of-the-art results from these techniques indicate a bright future for deep MRI.