Monte Carlo sure-based regularization parameter selection for penalized-likelihood image reconstruction

Jian Zhou, Jinyi Qi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Penalized likelihood (PL) image reconstruction has been developed for emission tomography to improve the image quality of reconstructed images. One challenge in PL reconstruction is that the selection of a proper regularization parameter to achieve a balance between the likelihood function and penalty function can be difficult. Here we present a novel method to choose the regularization parameter by minimizing Stein's unbiased risk estimate (SURE), which is an unbiased estimator of the true mean square error (MSE) of the PL reconstruction. A Monte-Carlo method is developed to compute SURE. Simulation studies are conducted based on a real PET scanner. Results show that the Monte Carlo SURE provides a practical and reliable way to select the optimum regularization parameter to minimize the total predicted mean squared error.

Original languageEnglish (US)
Title of host publication2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1095-1098
Number of pages4
ISBN (Print)9781467319591
StatePublished - Jul 29 2014
Event2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China
Duration: Apr 29 2014May 2 2014

Other

Other2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
CountryChina
CityBeijing
Period4/29/145/2/14

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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