Name
Title | ||
|---|---|---|
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 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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ISBI.2014.6868065 | ISBI |
Keywords | DocType | ISSN |
likelihood function,image quality,Monte Carlo sure-based regularization parameter selection,image reconstruction,Monte-Carlo method,MSE,penalized-likelihood image reconstruction,Monte Carlo methods,positron emission tomography,Stein's unbiased risk estimation,PET scanner,optimum regularization parameter,mean square error method,medical image processing,mean square error methods,penalty function | Conference | 1945-7928 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
2 | ||