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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Localization of realistic cortical activity in MEG using current multipoles
TL;DR: The results indicate that the regularized multipole solution may be an attractive alternative to current-dipole-based source estimation methods in MEG.
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Three-dimensional maximum-likelihood reconstruction for an electronically collimated single-photon-emission imaging system
TL;DR: It is demonstrated that optimal iterative three-dimensional reconstruction approaches can be feasibly applied to emission imaging systems that have highly complex spatial sampling patterns and that generate extremely large numbers of data values.
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Generic head models for atlas‐based EEG source analysis
TL;DR: This approach provides a mechanism for comparing source localizations across subjects in an atlas‐based coordinate system, which can be used in the large fraction of EEG studies in which MR images are not available.
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Fast MLE for SPECT using an intermediate polar representation and a stopping criterion
TL;DR: The authors perform the actual source estimation on a rectangular grid but use a polar pixel representation of the source during forward/back projection operations to reduce the computational requirements; they use a chi-squared test to statistically determine a suitable cutoff point for the iterative process.
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Determining cardiac velocity fields and intraventricular pressure distribution from a sequence of ultrafast CT cardiac images
TL;DR: A numerical algorithm developed by discretizing the pressure Poisson equation (PPE) can reconstruct the pressure distribution using only the velocity data and is shown to be robust in the presence of noise.