Active Research Projects
Connecting Brain Networks Across Subjects and Across Modalities
This research applies image analysis techniques to the processing of functional MRI and diffusion tensor MRI for the purpose of constructing structural and functional brain connectivity networks.
Collaborators: Satoru Hayasaka and Paul Laurienti, Wake Forest University School of Medicine.
Neurological Image Analysis in Primate Models of Alchohol and Drug Abuse
This research applies image analysis techniques commonly used in humans to primates, such as Macaques, in order to determine structual changes in brain morphometry due to the effects of alchohol and various pharmacological agents.
Collaborators: Bob Kraft, Jim Daunais, David Freeman, Linda Porrino, Carol Shively, Wake Forest University School of Medicine.
- E. V. Sullivan, H. J. Sable, W. N Strother, D. P. Friedman, A. Davenport, H. Tillman-Smith, R. A. Kraft, C. L. Wyatt, K. T. Szeliga, N. C. Buchheimer, J. B. Daunais, E. Adalsteinsson, A. Pfefferbaum, K. A Grant, Neuroimaging of Rodent and Primate Models of Alcoholism: Initial Reports from the Integrative Neuroscience Initiative on Alcoholism, Alcoholism: Clinical and Experimental Research Vol. 29 No. 2 pgs 287-294 Feb 2005.
Computational Neuroanatomy and Morphology
- C. L. Wyatt and P. J. Laurienti, Nonrigid Registration of Images with Different Topologies using Embedded Maps, Proceedings of IEEE Engineering in Medicine and Biology Conference, August 2006
- C.L. Wyatt, X. Li, X. Gong, A Framework for Registration of Images with Varying Topology using Embedded Maps: Reimannian Embedding Spaces, BSL Report BSL2009-0001, Bioimaging Systems Laboratory, Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University, 2009.
- X. Li and C. Wyatt, Modeling Topological Changes In Deformable Registration, InProceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro (ISBI'10). IEEE Press, Piscataway, NJ, USA, 360-363.
- X. Li and C.L. Wyatt, Brain Segmentation Performance using T1-weighted Images versus T1 Maps Proc. SPIE 7623, 76233R (2010), DOI:10.1117/12.844278.
This work is funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 RR021813 entitled Center for Computational Biology (CCB). Information on the National Centers for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/bioinformatics.
Segmentation and Polyp Detection in CT Colonography
CT Colonography (also known as Virtual Colonoscopy) is a minamally invasive screening technique for coloreactal polyps. Mixed success has been achieved in clinical trials, in large part due to variations in reader experience, the large number of images, and the complex geometry of the colon. Similar to mammography and lung nodule detection, computer polyp detection (CPD) and computer-aided polyp detection (CAPD) promise to improve the sensitivity and specificity of CTC. Our long term goal is to develop robust CAPD methods. Our current focus is on feature discovery, colon registration, and classifier design.
Collaborators: Pete Santago, Wake Forest University School of Medicine. See also CT Colonography Research Page.
- J. W. Suh and C. L. Wyatt, Registration of prone and supine colons in the presence of topological changes, 2008 SPIE International Symposium on Medical Imaging, Proc. of SPIE Vol. 6916 69160C-1-69160C10.
- J. W. Suh and C. L. Wyatt, Deformable Registration of Supine and Prone Ccolons Using Centerline Analysis, 2007 IEEE International Symposium on Biomedical Imaging
- J. W. Suh and C. L. Wyatt, Deformable Registration of Prone and Supine Colons for CT Colonography, Proceedings of IEEE Engineering in Medicine and Biology Conference, August 2006
- C. L. Wyatt, Y. Ge, David Vining, Segmentation in Virtual Colonoscopy Using a Geometric Deformable Model, Computerized Medical Imaging and Graphics, 30 (1): 17-30 Jan 2006.
- C. L. Wyatt, Y. Ge, D. J. Vining,Automatic Segmentation of the Colon for Virtual Colonoscopy, Computerized Medical Imaging and Graphics, Vol. 24 No. 1 pgs. 1-9, Jan-Feb 2000.
Related Reproducible Research Pages:
- Open Implementation of Feature Extraction Methods for Computer Aided Polyp Detection with Principle Component Analysis
- Deformable Registration of Supine and Prone Colons for CT Colonography
Registration of MRI Spherical Navigators for Prospective Motion Correction
Spherical navigators are an attractive approach to motion compensation in Magnetic Resonance Imaging. Because they can be acquired quickly, spherical navigators have the potential to measure and correct for rigid motion during image acquisition (prospectively as opposed to retrospectively). A limiting factor to prospective use of navigators is the time required to estimate the motion parameters. We have developed an efficient algorithm for recovery of rotational motion using spherical navigators. Our results show that the spherical harmonic based estimation algorithm is significantly faster than existing methods and so is suited for prospective motion correction.
Collaborators: Bob Kraft, Wake Forest University School of Medicine.