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

Journal Article · Fri Oct 01 00:00:00 EDT 1999

DOI:https://doi.org/10.1109/NSSMIC.1999.842813· OSTI ID:843117

Bonetto, Paola; Qi, Jinyi; Leahy, Richard M

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.

• Research Organization: Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE Director. Office of Science; National Cancer Institute Grants R01 CA59794 and R01 CA56655, US Department of Health and Human Services Grant P01 HL25840
• DOE Contract Number: AC03-76SF00098
• OSTI ID: 843117
• Report Number(s): LBNL-45512; R&D Project: 860538; TRN: US0504145
• Resource Relation: Other Information: Journal Publication Date: 08/2000
• Country of Publication: United States
• Language: English

Related Subjects

62 RADIOLOGY AND NUCLEAR MEDICINE
ACCURACY
PERFORMANCE
STATISTICS
Covariance Channelized Hotelling observer fast computation