Title: Artificial Intelligence in Magnetic Resonance Imaging (MRI): Using Mathematical Modeling, Statistics and Human Observer Experiments to Improve Image Quality

Abstract: Artificial intelligence uses deep neural networks to learn from data and make predictions based on what it has learned.  Artificial intelligence (deep learning) relies on data to develop the structure of the mathematical model, uses non-linearity and benefits from advanced computing.  In this talk we will explore how deep learning is being used in MRI to decrease the time that a patient needs to be in the MRI scanner.  The talk will present commonly used methods for accelerating MRI, like collecting less data followed by neural network reconstructions to generate images.  We will then explore active areas of research for evaluating image quality which include experiments of how well a human detects a lesion in an MRI image, statistical detection theory and how to mathematically predict the outcomes of these experiments.  The work presented in this talk was mostly done by undergraduate research students.  This talk will begin with an overview of my career trajectory to share how I came to be a mathematician and as an example of the many paths that lead to mathematics.