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Monte Carlo SURE-based regularization parameter selection for penalized-likelihood image reconstruction | IEEE Conference Publication | IEEE Xplore

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


Abstract:

Penalized likelihood (PL) image reconstruction has been developed for emission tomography to improve the image quality of reconstructed images. One challenge in PL recons...Show More

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.
Date of Conference: 29 April 2014 - 02 May 2014
Date Added to IEEE Xplore: 31 July 2014
Electronic ISBN:978-1-4673-1961-4

ISSN Information:

Conference Location: Beijing, China

References

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