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


Major research interests in Prof. Yi Wang’s lab are in applying and developing data science, machine learning, optimization, physics, and statistical inference techniques for medical imaging acquisition and analysis. This includes increasing imaging speed, reducing image artifacts, and generating 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 from acquired signals to underlying pathogeneses based on biophysics. We work closely with clinicians to study neurological diseases such as multiple sclerosis, Parkinson’s disease, Alzheimer’s disease, stroke, cancer in various organs, liver diseases, and heart diseases. The 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 statistical inference approach to removing image artifacts in MRI using prior knowledge established in biomedicine or acquired from multiple imaging modalities including immunohistochemical staining and optical imaging.

Our work is exemplified in the following:

  1. Quantitative susceptibility mapping (QSM) to solve the field-to-susceptibility inverse problem using the Bayesian approach. 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.
  2. 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.  
  3. 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.

For students interested in PhD 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