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.
Papers
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Journal ArticleDOI
High-Throughput Imaging of Brain Gene Expression
Vanessa M. Brown,Alex Ossadtchi,Arshad H. Khan,Simon R. Cherry,Richard M. Leahy,Desmond J. Smith +5 more
TL;DR: Using microarrays, voxel images of coronal hemisections at the level of the hippocampus of both the normal human brain and Alzheimer's disease brain were acquired and revealed a common network of coregulated genes, and allowed identification of putative control regions.
Proceedings Article
Correcting susceptibility-induced distortion in diffusion-weighted MRI using constrained nonrigid registration
TL;DR: The proposed method aligns diffusion images to the anatomical image with an error of 1-3 mm in most brain regions, and constrain the registration using spatial regularization and physics-based information about the characteristics of the distortion.
Journal ArticleDOI
Derivation and analysis of a filtered backprojection algorithm for cone beam projection data
X.-H. Yan,Richard M. Leahy +1 more
TL;DR: An approximate reconstruction formula is developed and shown to be essentially equivalent to the practical cone-beam algorithm of L.A. Feldkamp et al. (1984) and the resulting spatially varying point spread function is examined.
Journal ArticleDOI
Multiplex three-dimensional brain gene expression mapping in a mouse model of Parkinson's disease.
Vanessa M. Brown,Alex Ossadtchi,Arshad H. Khan,Simon Yee,Goran Lacan,William P. Melega,Simon R. Cherry,Richard M. Leahy,Desmond J. Smith +8 more
TL;DR: The investigation revealed a common network of coregulated genes shared between the normal and PD brain, and allowed identification of putative control regions responsible for these networks, and genes involved in cell/cell interactions were found to be prominently regulated in the PD brains.
Proceedings ArticleDOI
Statistical surface-based morphometry using a nonparametric approach
TL;DR: A novel method of statistical surface-based morphometry based on the use of nonparametric permutation tests to evaluate morphological differences of brain structures in order to evaluate anatomical structures acquired at different times and/or from different subjects is presented.