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 Article
Dual-time-point Patlak estimation from list mode PET data
TL;DR: In this article, a dual-time-point PET estimation method was proposed to compute voxel-wise estimates of Patlak parameters using two frames of data for each bed position, which in turn can be used to obtain whole-body dynamic PET using dual time point PET data.
Journal ArticleDOI
The FAST graph: A novel framework for the anatomically-guided visualization and analysis of cortico-cortical evoked potentials.
Kenneth N. Taylor,Anand A. Joshi,Jian Li,Jorge Gonzalez-Martinez,Xiaofeng Wang,Richard M. Leahy,Dileep Nair,John C. Mosher +7 more
TL;DR: An innovative framework for sorting, registering, labeling, ordering, and quantifying the functional CCEPs data, using the anatomical labelling of the brain, is described to provide an informative visualization and summary statistics which are called the "FAST graph" (Functional-Anatomical STacked area graphs).
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Relationships of alpha, beta, and theta EEG spectra properties with aggressive and nonaggressive antisocial behavior in children and adolescents
Sharon Niv,Syed Ashrafulla,Anand A. Joshi,Adrian Raine,Richard M. Leahy,Laura A. Baker,Catherine Tuvblad +6 more
TL;DR: In this article, the authors investigated potential correlations between underlying electroencephalogram (EEG) spectral power and aggressive or nonaggressive antisocial behavior and found that EEG spectral properties are correlated with aggression and non-aggression.
Book ChapterDOI
BrainSync: An Orthogonal Transformation for Synchronization of fMRI Data Across Subjects.
TL;DR: The geometry of the rfMRI signal space is exploited to conjecture the existence of an orthogonal transformation that synchronizes fMRI time series across sessions and subjects and the utility of this transformation is illustrated through applications to quantification of fMRI variability across subjects and sessions.
Book ChapterDOI
Quantile Regression for Uncertainty Estimation in VAEs with Applications to Brain Lesion Detection.
TL;DR: In this article, an alternative VAE model, Quantile-Regression VAE (QR-VAE), is proposed to avoid the variance shrinkage problem by estimating conditional quantiles for the given input image.