1). Fluid mechanics approach to tissue perfusion quantification in MRI,
2). Quantitative MRI-based cerebral oxygen metabolism in Alzheimer’s disease
3). Fluid mechanics based quantification of tau PET for Alzheimer's disease
4). Quantitative cerebral oxygen metabolic imaging of acute ischemic stroke
5). MR imaging based QSM for estimating outcome and guiding patient selection and surgery in DBS of PD.
Major research interests in Professor Wang’s lab involve applying and developing data science, machine learning, optimization, physics, and statistical inference techniques for medical imaging acquisition and analysis.
Quantitative susceptibility mapping (QSM) to solve the field-to-susceptibility inverse problem using the Bayesian inference. Tissue susceptibility reflects molecular electron cloud properties and QSM enables its precise quantitative study. QSM has become a very active field of studying tissue magnetism for applications in neurodegeneration, inflammation, oxygen consumption, hemorrhage, liver, osteoporosis, atherosclerosis, and drug delivery. QSM is increasingly used in clinical practice, particularly in precision targeting for deep brain stimulation, precision monitoring of chronic active hemorrhages and inflammation, precision medication for iron chelation therapy, and precision diagnosis and gadolinium-free imaging for multiple sclerosis.
Quantitative transport mapping (QTM) to solve the inverse problem from imaging to tissue perfusion quantification. We develop fast dynamic imaging (4D) to capture tracer (drugs, contrast agents and spin labels) transport in tissue using super resolution, sparsity, and deep learning techniques. Perfusion parameters affect imaging through convolution in space time according to transport equation of mass and momentum flux laws. We develop QTM to deconvolve 4D time resolved imaging for perfusion quantification. QTM enables precise measurement of blood flow in tissue and helps with precise delivery of therapeutic drugs, cryotherapy and tissue ablation.
Lesion segmentation from acquired images to enable automated precise measurements and analyses of disease burden. We employ various image segmentation techniques including image feature-based approaches and deep neural network-based approaches.
Biography
Dr. Yi Wang, Ph.D., obtained his BS in Nuclear Physics from Fudan University in 1986. He then went on to complete his PhD in Medical Physics from the University o Wisconsin, Madison, in 1994. He completed his postdoctoral training at the Mayo Clinic in Rochester, Minnesota in 1996. He now holds the Faculty Distinguished Professorship in the Department of Radiology, and is a tenured Professor of Physics in Radiology, Director of the Magnetic Resonance Imaging (MRI) Research Institute at Weill Cornell Medicine (WCM), and Professor of Biomedical Engineering at Cornell University.
Distinctions:
Graduate Fellowship, University of Wisconsin-1988
Advanced Richard B. Mazess Scholarship, University of Wisconsin-1993
Fellow, American Institute for Medical and Biological Engineering (AIMBE)-2006
Fellow, International Society of Magnetic Resonance in Medicine (ISMRM)-2012
Fellow, Institute of Electrical and Electronics Engineers (IEEE)-2014
Distinguished Investigator Award, Academy of Radiology Research-2015
NIH Advisory Panel on Review of Imaging Science-2017
