Covariance approximation for fast and accurate computation of channelized Hotelling observer statistics

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We describe a method for computing linear observer statistics for maximum a posteriori (MAP) reconstructions of PET images. The method is based on a theoretical approximation for the mean and covariance of MAP reconstructions. In particular, we derive here a closed form for the channelized Hotelling observer (CHO) statistic applied to 2D MAP images. We show reasonably good correspondence between these theoretical results and Monte Carlo studies. The accuracy and low computational cost of the approximation allow us to analyze the observer performance over a wide range of operating conditions and parameter settings for the MAP reconstruction algorithm.

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Bonetto, Paola; Qi, Jinyi & Leahy, Richard M. October 1, 1999.

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We describe a method for computing linear observer statistics for maximum a posteriori (MAP) reconstructions of PET images. The method is based on a theoretical approximation for the mean and covariance of MAP reconstructions. In particular, we derive here a closed form for the channelized Hotelling observer (CHO) statistic applied to 2D MAP images. We show reasonably good correspondence between these theoretical results and Monte Carlo studies. The accuracy and low computational cost of the approximation allow us to analyze the observer performance over a wide range of operating conditions and parameter settings for the MAP reconstruction algorithm.

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  • Other Information: Journal Publication Date: 08/2000

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  • Report No.: LBNL--45512
  • Grant Number: AC03-76SF00098
  • Office of Scientific & Technical Information Report Number: 843117
  • Archival Resource Key: ark:/67531/metadc787112

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  • October 1, 1999

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  • Dec. 3, 2015, 9:30 a.m.

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  • April 4, 2016, 12:34 p.m.

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Bonetto, Paola; Qi, Jinyi & Leahy, Richard M. Covariance approximation for fast and accurate computation of channelized Hotelling observer statistics, article, October 1, 1999; Berkeley, California. (https://digital.library.unt.edu/ark:/67531/metadc787112/: accessed April 25, 2024), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu; crediting UNT Libraries Government Documents Department.

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