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
Chronic anemia: The effects on the connectivity of white matter
Clio Gonzalez-Zacarias,Soyoung Choi,Chau Vu,Botian Xu,Jian-hua Shen,Anand Y. Joshi,Richard M. Leahy,John N. Wood +7 more
TL;DR: Lower FA values in anemic patients are found; indicating the loss of coherence in the main diffusion direction that potentially indicates WM injury, and a positive correlation between FA and hemoglobin in these same regions is seen, suggesting that decreased WM microstructural integrity FA is highly driven by chronic hypoxia.
Journal ArticleDOI
Identification of overlapping and interacting networks reveals intrinsic spatiotemporal organization of the human brain
TL;DR: In this article , a functional network atlas is proposed to explore group and individual differences in neurocognitive function, as well as demonstrate in the context of ADHD and IQ prediction.
Posted ContentDOI
BrainSuite BIDS App: Containerized Workflows for MRI Analysis
Yeun Kim,Anand Y. Joshi,Soyoung Choi,Shantanu H. Joshi,Chitresh Bhushan,Divya Varadarajan,Justin P. Haldar,Richard M. Leahy,David W. Shattuck +8 more
TL;DR: The BrainSuite BIDS App as mentioned in this paper provides a standard for implementing containerized processing environments that include all necessary dependencies to process Brain Imaging Data Structure (BIDS) datasets using image processing workflows.
Posted Content
Deep Quantile Regression for Uncertainty Estimation in Unsupervised and Supervised Lesion Detection.
TL;DR: In this article, the authors use quantile regression to estimate aleatoric uncertainty and use it for estimating uncertainty in both supervised and unsupervised lesion detection problems using VAE.
Proceedings ArticleDOI
Fully 3D uniform resolution transmission microPET image reconstruction
TL;DR: MAP (maximum a posteriori) reconstructions of transmission images produce more accurate ACFs (attenuation correction factors) than smoothed division of blank and transmission scans and analytical methods and the relationship between resolution and the global smoothing parameter is investigated through computer simulations.