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
More filters
MEG-based imaging of focal neuronal current sources
TL;DR: In this article, a Gibbs prior is constructed to reflect the sparse focal nature of neuronal current sources associated with evoked response data, and an estimate of the primary current source distribution for a specific data set is formed by maximizing over the posterior probability with respect to the binary and continuous variables.
Proceedings ArticleDOI
Simultaneous hyperparameter estimation and Bayesian image reconstruction for PET
TL;DR: A new iterative algorithm is presented that simultaneously estimates the PET image and the global hyperparameter /spl beta/ of a Gibbs prior and an approximation in which the marginalization with respect to the image sample space is reduced to the product of a set of one dimensional integrals; one per image pixel.
Proceedings ArticleDOI
A new combined surface and volume registration
Natasha Lepore,Anand A. Joshi,Richard M. Leahy,C. Brun,Yi-Yu Chou,Xavier Pennec,Agatha D. Lee,Marina Barysheva,Greig I. de Zubicaray,Margaret J. Wright,Katie L. McMahon,Arthur W. Toga,Paul M. Thompson +12 more
TL;DR: In this article, a surface-based cortical registration with a 3D fluid registration algorithm was proposed, enabling precise matching of cortical folds, but allowing large deformations in the enclosed brain volume, which guarantee diffeomorphisms.
Proceedings ArticleDOI
A new unsupervised hierarchical segmentation algorithm for textured images
Z. Wu,Richard M. Leahy +1 more
TL;DR: An unsupervised hierarchical segmentation method is described, and its application to tissue classification in magnetic resonance (MR) images of the human brain is demonstrated.
Book ChapterDOI
Using the Anisotropic Laplace Equation to Compute Cortical Thickness.
Anand A. Joshi,Chitresh Bhushan,Ronald Salloum,Jessica L. Wisnowski,David W. Shattuck,Richard M. Leahy +5 more
TL;DR: A novel method based on the anisotropic heat equation that explicitly accounts for the presence of partial tissue volumes to more accurately estimate cortical thickness is described and results with in-vivo data are more consistent with histological findings reported in the literature.