R
Richard M. Leahy
Researcher at University of Southern California
Publications - 419
Citations - 27317
Richard M. Leahy is an academic researcher from University of Southern California. The author has contributed to research in topics: Iterative reconstruction & Imaging phantom. The author has an hindex of 70, co-authored 406 publications receiving 24876 citations. Previous affiliations of Richard M. Leahy include Los Alamos National Laboratory & Johns Hopkins University School of Medicine.
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Proceedings ArticleDOI
Multispectral tissue classification of MR images using sensor fusion approaches
TL;DR: A sensor fusion approach to tissue classification and segmentation in which each of the three images are treated as the output of different sensors and a new deterministic relaxation scheme that updates the belief intervals is presented.
Journal ArticleDOI
A comparison of seven different DTI-derived estimates of corticospinal tract structural characteristics in chronic stroke survivors.
Bokkyu Kim,Beth E. Fisher,Nicolas Schweighofer,Richard M. Leahy,Justin P. Haldar,Soyoung Choi,Dorsa Beroukhim Kay,James Gordon,Carolee J. Winstein +8 more
TL;DR: Compared to the six other methods, CST FA asymmetry from 3-D individual tractography is the most accurate estimate of CST structure in this cohort of stroke survivors and is recommended for future research seeking to understand brain-behavior mechanisms of motor recovery in chronic stroke survivors.
Journal ArticleDOI
Registration-Based Morphometry for Shape Analysis of the Bones of the Human Wrist
TL;DR: The results indicate that RBM has potential to provide a standardized approach to shape analysis of bones of the human wrist and is presented to show the application of RBM for tracking bone erosion status in rheumatoid arthritis.
Proceedings ArticleDOI
Multi-modality tomographic image reconstruction using mesh modeling
TL;DR: The preliminary results demonstrate that this mesh-based approach to multi-modality PET reconstruction can achieve good results at low computational cost.
Proceedings ArticleDOI
Canonical correlation analysis applied to functional connectivity in MEG
TL;DR: A multivariate method based on canonical correlation analysis for the study of functional connectivity in the brain with MEG data is presented, and it is demonstrated that it is able to detect functional interactions across space as well as the frequency bands that contribute to these interactions.