Author
Compressed Sensing of Multiple Intra-Voxel Orientations with Traditional DTI
Sep. 1, 2008—A. Landman, J. Bogovic, and J. L. Prince. “Compressed Sensing of Multiple Intra-Voxel Orientations with Traditional DTI”, In Proceedings of the Workshop on Computational Diffusion MRI at the 11th International Conference on Medical Image Computing and Computer Assisted Intervention, New York, NY, September 2008. Full Text:http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.701.4262&rep=rep1&type=pdf#page=183 Abstract Diffusion tensor imaging (DTI) is widely used to...
Robust Maximum Likelihood Estimation in Q-space MRI
May. 1, 2008—A. Landman, J. A.D. Farrell, S. A. Smith, P. A. Calabresi, P. C.M. van Zijl, and J. L. Prince. “Robust Maximum Likelihood Estimation in Q-space MRI”, In Proceedings of the 2008 IEEE International Symposium on Biomedical Imaging, Paris, France, May 2008 PMC2872926 Full Text:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2872926/ Abstract Q-space imaging is an emerging diffusion weighted MR imaging technique...
Automatically identifying white matter tracts using cortical labels
May. 1, 2008—A. Bogovic, A. Carass, J. Wan, B. A. Landman, and J. L. Prince. “Automatically identifying white matter tracts using cortical labels,” In Proceedings of the 2008 IEEE International Symposium on Biomedical Imaging, Paris, France, May 2008 PMC2812932 Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2812932/ Abstract Diffusion tensor imaging (DTI) has become a standard clinical procedure in assessing the health...
Diffusion Tensor Estimation by Maximizing Rician Likelihood
Oct. 1, 2007—B. A. Landman, P-L. Bazin, and J. L. Prince. “Diffusion Tensor Estimation by Maximizing Rician Likelihood”, In Proceedings of the 2007 International Conference on Computer Vision Workshop on Mathematical Methods in Biomedical Image Analysis, Rio de Janeiro, Brazil, October 2007. (Oral Presentation) Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3488430/ Abstract Diffusion tensor imaging (DTI) is widely used to characterize...