Yi Wang

Yi Wang

Meinig School of Biomedical Engineering
MVR G301, the MRI facility
The Faculty Distinguished Professor, Director of MRI Research Institute Radiology
Weill Cornell Medicine


The primary research interests in Prof. Yi Wang’s lab revolve around applying and developing physics modeling, machine learning, optimization, and other statistical inference techniques for medical imaging acquisition and analysis. This encompasses endeavors to increase imaging speed, reduce image artifacts, and generate novel image contrasts/biomarkers using computer vision and signal processing strategies. We seek to formulate medical imaging problems for disease diagnosis and therapy delivery as inverse problems, deducing underlying pathogeneses from acquired signals based on biophysics. We work closely with clinicians to study neurological diseases such as multiple sclerosis, Parkinson’s disease, Alzheimer’s disease, stroke, as well as cancer in various organs, liver diseases, and heart diseases. These inverse problems are often poorly conditioned and involve noisy incomplete data, resulting in reconstructed images with errors or artifacts commonly encountered in computer vision. We have developed the Bayesian solutions for these inverse problems, drawing from prior knowledge established in biomedicine or acquired from multiple imaging modalities, including immunohistochemical staining and optical imaging.

Our research is exemplified in the following: 

  1. Quantitative susceptibility mapping (QSM), which employs a Bayesian approach to solve the field-to-susceptibility inverse problem. Tissue susceptibility reflects molecular electron cloud properties, and QSM enables precise quantitative studies of tissue susceptibility. QSM has been actively used to study neurodegeneration, inflammation, oxygen consumption, hemorrhage, liver iron and fibrosis, 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.
  2. Quantitative transport mapping (QTM), which solves the inverse problem from imaging to tissue perfusion quantification. We develop fast dynamic imaging (4D) techniques to capture tracer (including 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 quantifying perfusion parameters. QTM enables precise measurement of blood flow in tissue and aids in the accurate delivery of therapeutic drugs, cryotherapy and tissue ablation.  
  3. Lesion segmentation from acquired images, facilitating automated precise measurements and analyses of disease burden. We employ various image segmentation techniques including image feature approaches and deep neural network approaches.

For students interested in Ph.D. projects at Prof. Wang's lab, the following video provides additional information including a brief overview of recent theses:

Research Interests

Teaching Interests

Principles of medical imaging, Magnetic Resonance Imaging (MRI)

Selected Publications

  • Zhang Q, Luo X, Zhou L, Nguyen TD, Prince MR, Spincemaille P, Wang Y. Fluid mechanics approach to perfusion quantification: vasculature computational fluid dynamics simulation, quantitative transport mapping (QTM) analysis of dynamics contrast enhanced MRI, and application in nonalcoholic fatty liver disease classification. IEEE Trans Biomed Eng. 2022 Sep 15;PP. doi: 10.1109/TBME.2022.3207057. PMID: 36107908
  • Zhou L, Zhang Q, Spincemaille P, Nguyen TD, Morgan J, Dai W, Li Y, Gupta A, Prince MR, Wang Y. Quantitative transport mapping (QTM) of the kidney with an approximate microvascular network. Magn Reson Med. 2020 Nov 18. doi: 10.1002/mrm.28584. PMID: 33210310
  • Cho J, Zhang J, Spincemaille P, Zhang H, Hubertus S, Wen Y, Jafari R, Zhang S, Nguyen TD, Dimov AV, Gupta A, Wang Y. QQ-NET – using deep learning to solve Quantitative Susceptibility Mapping and Quantitative Blood Oxygen Level Dependent magnitude (QSM+qBOLD or QQ) based Oxygen Extraction Fraction (OEF) mapping. Magn Reson Med. 2021, doi: 10.1002/mrm.29057. PMID: 34719059
  • Jafari R, Spincemaille P, Zhang J, Nguyen TD, Luo X, Cho J, Margolis D, Prince MR, Wang Y. Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training. Magn Reson Med. 2020 Oct 26. doi: 10.1002/mrm.28546. PMID: 33107127
  • Dimov AV, Nguyen TD, Gillen KM, Marcille M, Spincemaille P, Pitt D, Gauthier SA, Wang Y. Susceptibility source separation from gradient echo data using magnitude decay modeling. J Neuroimaging. 2022 Jun 6. doi: 10.1111/jon.13014. PMID: 35668022

Selected Awards and Honors

  • Fellow of American Institute for Medical and Biological Engineering (AIMBE) 2006
  • Fellow (International Society of Magnetic Resonance in Medicine) 2012
  • Fellow (Institute of Electrical and Electronics Engineers) 2013
  • Advanced Richard B. Mazess Scholarship (University of Wisconsin) 1993
  • Graduate Fellowship (University of Wisconsin) 1988


  • B.S. (Nuclear Physics), Fudan University, 1986
  • M.S. (Theoretical Physics), University of Wisconsin, Milwaukee, 1988
  • Ph.D. (Medical Physics), University of Wisconsin, Madison, 1994
  • Postdoc, Mayo Clinic, 1994-1996