NDLI: Techniques for regularization parameter and hyper-parameter selection in PET and SPECT Imaging

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Techniques for regularization parameter and hyper-parameter selection in PET and SPECT Imaging

Content Provider	Semantic Scholar
Author	Goldes, John
Copyright Year	2012
Abstract	Penalized maximum likelihood methods are commonly used in positron emission tomography (PET) and single photon emission computed tomography (SPECT). Due to the fact that a Poisson data-noise model is typically assumed, standard regularization parameter choice methods, such as the discrepancy principle or generalized cross validation, can not be directly applied. In recent work of the authors, regularization parameter choice methods for penalized negative-log Poisson likelihood problems are introduced. In this paper, we apply these methods to the applications of PET and SPECT, introducing a modification that improves the performance of the methods. We then demonstrate how these techniques can be used to choose the hyper-parameters in a Bayesian hierarchical regularization approach.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://web.math.umt.edu/bardsley/papers/RegParamPET.pdf
Language	English
Access Restriction	Open
Subject Keyword	Assumed Bayesian network CT scan Choose (action) Cross-validation (statistics) Discrepancy function Estimated Hyperactive behavior Least squares Least-Squares Analysis Magnetic Resonance Imaging Matrix regularization Numerical analysis Photons Polyethylene terephthalate Population Parameter Positron-Emission Tomography Positrons Single Photon Emission Computed Tomography Computed Tomography Tomography, Emission-Computed Tomography, Emission-Computed, Single-Photon X-Ray Computed Tomography algorithm triangulation
Content Type	Text
Resource Type	Article

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