Bennett Landman, Ph.D.
Our research concentrates on analyzing large-scale cross-sectional and longitudinal neuroimaging data. Specifically, we are interested in population characterization with magnetic resonance imaging (MRI), multi-parametric studies (DTI, sMRI, qMRI), and shape modeling.
We are working technologies to enable large-scale and high throughput medical image analysis. Current projects include investigation of statistical label fusion techniques and multi-modal MRI approaches. In support of the VUIIS Center for Computational Imaging, we are developing imaging informatics and automated analysis technologies.
Research Information
Lucas BC, Bogovic JA, Carass A, Bazin PL, Prince JL, Pham DL, Landman BA. The Java Image Science Toolkit (JIST) for Rapid Prototyping and Publishing of Neuroimaging Software. Neuroinformatics. 2010 Jan 14. PMID:20077162 NIHMS PMC:177090
B. A. Landman, H. Wan, J. Bogovic, P.-L. Bazin, and J. L. Prince. Resolution of Crossing Fibers with Constrained Compressed Sensing using Traditional Diffusion Tensor MRI', In Proceedings of the SPIE Medical Imaging Conference. San Diego, CA, February 2010 NIHMS/PMC:158459
B. A. Landman, H. Wan, J. Bogovic, and J. L. Prince. Simultaneous Truth and Performance Level Estimation with Incomplete, Over-complete, and Ancillary Data, In Proceedings of the SPIE Medical Imaging Conference. San Diego, CA, February 2010
B. A. Landman, P-L Bazin, S. A. Smith, and J. L. Prince, Robust Estimation of Spatially Variable Noise Fields, Magnetic Resonance in Medicine, Aug;62(2):500-9. 2009 PMID:19526510 PMC2806192
B. A. Landman, J. A. Farrell, C. K. Jones, S. A. Smith, J. L. Prince, P. C. van Zijl, and S. Mori. Effects of Diffusion Weighting Schemes on the Reproducibility of DTI-derived Fractional Anisotropy, Mean Diffusivity, and Principal Eigenvector Measurements at 1.5T, NeuroImage. 36(4): 1123-1138. July 2007. PMID:17532649