09/13/2021
By Joanne Gagnon-Ketchen
Zoom link to join: Contact Joanne Gagnon-Ketchen for the link.
Joyita Dutta, Associate Professor, University of Massachusetts Lowell will give a talk on "Artificial Intelligence in Medical Imaging."
Abstract: Recent advances in machine learning and artificial intelligence (AI), particularly the emergence of innovative deep neural network architectures, have been transformative for medical imaging. Specifically, for positron emission tomography (PET) imaging, where accurate quantitative interpretation has been both a pressing need and a lingering challenge, AI has assumed a growing role in improving image quantitation and facilitating image interpretation. The former category includes image reconstruction and image enhancement techniques, while the latter includes lesion detection and image segmentation. In this talk, I will provide an overview of AI techniques in the context of medical imaging and highlight some of my lab’s research on image denoising, deblurring, motion correction, and super-resolution. In the context of Alzheimer’s disease (AD), where there is a critical void in biomarker development, I will demonstrate the application of some graph-domain AI techniques for the predictive modeling of tau tangles in the brain, which are a neuropathological hallmark of AD.
Bio: Joyita Dutta is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Massachusetts Lowell (UML) and directs the Biomedical Imaging and Data Science Laboratory (BIDSLab) at UML. She also holds faculty appointments at Harvard Medical School and Massachusetts General Hospital (MGH). Dr. Dutta received her B.Tech. (Honors) in Electronics and Electrical Communication Engineering from the Indian Institute of Technology Kharagpur, India, in 2004 and her M.S. and Ph.D. degrees in Electrical Engineering from the University of Southern California in 2006 and 2011 respectively. Her core research expertise is in machine learning and signal processing for imaging, graph, and time-series datasets. The overarching goal of her lab’s research is to solve biomedical inverse problems via the integration of multimodality information. She has 14 years of experience working with different imaging modalities, including positron emission tomography (PET), magnetic resonance imaging (MRI), and fluorescence molecular tomography. Her contributions to medical imaging have been recognized by the 2016 Tracy Lynn Faber Memorial Award from the SNMMI and the 2016 Bruce Hasegawa Young Investigator Medical Imaging Science Award from the IEEE. She is also the recipient of an SNMMI CaIC (now PIDSC) Young Investigator Award, the 2013 SNMMI Mitzi & William Blahd MD Pilot Research Grant, the 2013 American Lung Association Senior Research Training Fellowship, and an NIH K01 Career Development Award.