Multimodal Imaging for Diagnostics and Therapeutics
Medicine is currently experiencing a paradigm shift from the reliance on individual imaging modalities to a more integrative approach encompassing multiple imaging and non-imaging datasets for holistic information capture. This talk will present signal processing and machine learning techniques that integrate functional information from positron emission tomography (PET) with structural information from magnetic resonance imaging (MRI) to solve a range of inverse problems, including 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 of these techniques for accurate quantitation of amyloid plaques and tau tangles, which are neuropathological hallmarks of AD. Specifically, in the context of tau PET imaging, methods developed in my lab have led to improved correlations of PET measures with well-recognized clinical metrics of cognitive performance. I will also demonstrate the application of these methods for brain network connectivity analysis, especially in distinguishing subjects with early and late mild cognitive impairment from AD patients.
Joyita Dutta is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Massachusetts Lowell (UML), Lowell, MA, and directs the Biomedical Imaging and Data Science Laboratory (BIDSLab) at UML. She also holds ranks of Instructor at Harvard Medical School and Assistant in Physics at Massachusetts General Hospital (MGH), Boston, MA. 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, Los Angeles, CA, in 2006 and 2011 respectively. Her research interests are signal processing and image analysis using PET/MRI and PET/CT for medical applications with an emphasis on image quantitation, multimodal information integration, and graph network analysis in the context of aging and Alzheimer's disease. In 2013, she received a Young Investigator Award from Computer and Instrumentation Council under the Society of Nuclear Medicine and Molecular Imaging (SNMMI). She was also a recipient of the SNMMI Mitzi & William Blahd MD Pilot Research Grant (2013-2014), an American Lung Association Senior Research Training Fellowship (2013-2015), and an NIH K01 Career Award (2015-2020). 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.
Host: Professor Donald Brown