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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.

Project Website

Collaborators: Satoru Hayasaka and Paul Laurienti, Wake Forest University School of Medicine.


Neurological Image Analysis in Primate Models of Alchohol and Drug Abuse

FA Map of Primate Brain

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.

Relevant Papers:


Computational Neuroanatomy and Morphology

Project Website

Relevant Papers:

Funding Sources:

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

Completed Projects

Segmentation and Polyp Detection in CT Colonography

Example Colon Lumen Surface

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.

Relevant Papers:


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Registration of MRI Spherical Navigators for Prospective Motion Correction

Example Spherical Navigator

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.