Refining penalty parameter selection in whole-body PET image reconstruction for lung cancer patients using the cross-validation log-likelihood method.

ABSTRACT: Objective.Penalty parameters in penalized likelihood positron emission tomography (PET) reconstruction are typically determined empirically. The cross-validation log-likelihood (CVLL) method has been introduced to optimize these parameters by maximizing a CVLL function, which assesses the likelihood of reconstructed images using one subset of a list-mode dataset based on another subset. This study aims to validate the efficacy of the CVLL method in whole-body imaging for cancer patients using a conventional clinical PET scanner.Approach.Fifteen lung cancer patients were injected with 243.7 ± 23.8 MBq of [¹⁸F]FDG and underwent a 22 min PET scan on a Biograph mCT PET/CT scanner, starting at 60 ± 5 min post-injection. The PET list-mode data were partitioned by subsamp

SUBMITTER: Wang Q 

PROVIDER: S-EPMC11500750 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature