and Brain Sciences
Dr. Hu Cheng
Physicist, Imaging Research Facility
hucheng [at] indiana.edu
office: PY 161A | (812)856-2518
lab: PY 161 | (812)856-3723
Artifact reduction for functional MRI; spatial-tempo characteristics of fMRI signal; parallel imaging; ultra-fast imaging techniques
- 2001 Ph.D., City University of New York
- 1995 Sci.M.,Nankai Institute of Mathematics
- 1992 B.Sc., University of Science and Technology of China
Area of Study
- MRI physics
- Biomedical engineering
- Parallel imaging
- Artifact reduction
- MRI phase image
- Signal processing and application of fMRI
- Functional and structural networks of human brains and their application in neuroscience
As MRI physicist, Dr. Cheng's research lies mainly on MRI related image quality control and protocol optimization for neuroimaging. Echo Planar Imaging (EPI) and Diffusion Tensor Imaging (DTI) are the two major imaging tools in neuroimaging. There are many artifacts and various noises in EPI and DTI images. Parallel imaging technique with multi-channel coil has allowed higher spatial and temporal resolution in brain imaging but also introduced instability of noise across multiple runs of fMRI and additional noise from head motion. Dr. Cheng is interested in tackling all these problems by combining MRI physics and signal processing. For instance, Dr. Cheng has developed a robust method to correct for respiratory noise using phase information. The Phase image is often discarded in MRI studies, but its value has been recognized in many applications. Taking advantage of phase images is an important component in Dr. Cheng's research. Recently Dr. Cheng has been focusing on the structural network constructed from DTI, including data optimization, characteristics of the weighted structural network, inter-subject variability, etc. Dr. Cheng is also interested in the functional network constructed from resting state fMRI time series, and the relation between structural network and functional network. The network analysis of human brain can measure brain alteration in a large scale and potentially provide bench markers for some neuropsychological diseases..
Cheng, H., Wang Y., Sheng, J., Sporns, O., Kronenberger, W.G., Mathews, V.P., Hummer, T., Saykin, A. (2012). Characteristics and variability of structural network derived from diffusion tensor imaging. Neuroimage, 61:1153-64.
Cheng, H. (2012). Variation of Noise in multi-run fMRI using GRAPPA. Journal of Magnetic Resonance Imaging, 35:462-70.
Cheng, H., Wang Y., Sheng, J., Sporns, O., Kronenberger, W.G., Mathews, V.P., Hummer, T., Saykin, A. (2012). Effect of Number of Seeds in Tractography on the Variance of Structural Networks. Journal of Neuroscience Methods, 203:264-72.
Kim, D., Patrick, D.S., Cheng, H., Pruce, B.J., Braumbaugh, M.S.m Vollmer, J.M., Hetrick, W.P., O'Donnell, B.F., Sporns, O., Puce, A., Newman, S.D., (2011). Structural Network Topology Revealed by White Matter Tractography in Cannabis Users: A Graph Theoretical Analysis, Brain Connectivity. 1:473-83.
Beeri, M.S., Lee, H., Cheng, H., Wollman, D., Silverman, J.M., Prohovnik, I. (2011). Memory activation in healthy nonagenarians. Neurobiology of Aging, 32:515-523.
Cheng, H. & Li, Y. (2010). Respiratory Noise Correction Using Phase Information. Magnetic Resonance Imaging, 28:574-582.
Cheng, H., Huang, F. (2006). MRI Image Intensity Correction with Extrapolation and Adaptive Smoothing. Magnetic Resonance in Medicine, 55:959-966.
Cheng, H., Zhao, Q., Duensing, G.R., Edelstein, W., Spencer, D., Browne, N., Saylor, C., Limkeman, M., (2006). SMARTPHANTOM – an fMRI Informatics Tool. Magnetic Resonance Imaging, 24:301-313.