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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Journal ArticleDOI
Determination of Internal Deformation Field in Asphalt Cores Using X-Ray Computer Tomography
TL;DR: In this paper, a method for computing the microscopic internal displacement fields associated with permanent deformations of 3D asphalt-aggregate cores with complex internal structure and satisfying the small gradient approximation of continuum mechanics is presented.
Journal ArticleDOI
Anemia predicts lower white matter volume and cognitive performance in sickle and non-sickle cell anemia syndrome
B.A. Soyoung Choi,B.A. Soyoung Choi,Sharon H. O'Neil,Sharon H. O'Neil,Anand A. Joshi,Jian Li,Adam Bush,Adam Bush,Adam Bush,Thomas D. Coates,Thomas D. Coates,Richard M. Leahy,John C. Wood,John C. Wood +13 more
TL;DR: The spatial distribution of volume loss suggests chronic hypoxic cerebrovascular injury, despite compensatory hyperemia, and the possible neurological consequences of chronic anemia may help inform current clinical practices.
Journal ArticleDOI
Exact and approximate Fourier rebinning of PET data from time-of-flight to non time-of-flight
TL;DR: This work presents a unified framework based on a generalized projection slice theorem for TOF data that can be used to compute each of the mappings for rebinning into non TOF formats without significant loss of information.
Journal ArticleDOI
Linear transforms for Fourier data on the sphere: Application to high angular resolution diffusion MRI of the brain
TL;DR: A novel family of linear transforms that can be applied to data collected from the surface of a 2-sphere in three-dimensional Fourier space that can outperform existing state-of-the-art orientation estimation methods with respect to measures such as angular resolution and robustness to noise and modeling errors.
Proceedings ArticleDOI
Matrix kernels for MEG and EEG source localization and imaging
TL;DR: The authors present the matrix kernels for the general boundary element model (BEM) and for MEG spherical models and show how these kernels are easily interchanged in a linear algebraic framework that includes sensor specifics such as orientation and gradiometer configuration.