P.J. La Riviere
University of Chicago
47 Papers
379 Citations
P.J. La Riviere is an academic researcher from University of Chicago. The author has contributed to research in topics: Iterative reconstruction & Smoothing. The author has an hindex of 12, co-authored 47 publications.
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Papers
Penalized-likelihood sinogram restoration for computed tomography
TL;DR: It is found that at low exposure levels typical of those being considered for screening CT, the Poisson-likelihood based approaches outperform the PWLS objective as well as a standard approach based on adaptive filtering followed by deconvolution.
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Reduction of noise-induced streak artifacts in X-ray computed tomography through spline-based penalized-likelihood sinogram smoothing
TL;DR: It is found that the statistically principled sinogram smoothing approach is naturally adaptive-it will smooth more variable measurements more heavily than it does less variable measurements, and significantly reduces streak artifacts and noise levels without comprising image resolution.
117
Monotonic penalized-likelihood image reconstruction for X-ray fluorescence computed tomography
P.J. La Riviere,Phillip Vargas +1 more
TL;DR: A monotonic penalized-likelihood algorithm for image reconstruction in X-ray fluorescence CT (XFCT) when the attenuation maps at the energies of the fluorescence X-rays are unknown, guaranteed to increase the penalized likelihood at each iteration.
54
Nonparametric regression sinogram smoothing using a roughness-penalized Poisson likelihood objective function
P.J. La Riviere,Xiaochuan Pan +1 more
TL;DR: The authors develop and investigate an approach to tomographic image reconstruction in which nonparametric regression using a roughness-penalized Poisson likelihood objective function is used to smooth each projection independently prior to reconstruction by unapodized filtered backprojection (FBP).
52
X-Ray Fluorescence Emission Tomography (XFET) With Novel Imaging Geometries— A Monte Carlo Study
TL;DR: This study has demonstrated that the proposedXFET approach could lead to a greatly improved imaging speed, which is critical for making XFET a practical imaging modality for a wide range of applications.
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