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Refining penalty parameter selection in whole-body PET image reconstruction for lung cancer patients using the cross-validation log-likelihood method.
Wang, Qian; Abdelhafez, Yasser G; Nalbant, Hande; Spencer, Benjamin A; Bayerlein, Reimund; Qi, Jinyi; Cherry, Simon R; Nardo, Lorenzo; Badawi, Ramsey D.
Afiliación
  • Wang Q; Biomedical Engineering, University of California Davis, 451 Health Science Drive, Davis, California, 95616, UNITED STATES.
  • Abdelhafez YG; Radiology, University of California Davis, 3195 Folsom Blvd, Sacramento, California, 95816, UNITED STATES.
  • Nalbant H; Radiology, University of California Davis, 3195 Folsom Blvd, Sacramento, California, 95816, UNITED STATES.
  • Spencer BA; University of California Davis Medical Center, 3195 Folsom Blvd, Sacramento, California, 95817-2282, UNITED STATES.
  • Bayerlein R; Department of Radiology, University of California Davis Health System, 3195 Folsom Blvd, Sacramento, California, 95816, UNITED STATES.
  • Qi J; Biomedical Engineering Department, University of California - Davis, One Shields Avenue, Davis, CA 95616, USA, Davis, California, 95616, UNITED STATES.
  • Cherry SR; Department of Biomedical Engineering, University of California - Davis, One Shields Avenue, Davis, California, 95616, UNITED STATES.
  • Nardo L; Department of Radiology, UC Davis Health System, 4860 Y Street, Suite 3100, Sacramento, CA 95817, USA, Sacramento, California, 95817, UNITED STATES.
  • Badawi RD; Department of Radiology, UC Davis Health System, 4860 Y Street, Suite 3100, Sacramento, CA 95817, USA, Sacramento, California, 95817, UNITED STATES.
Phys Med Biol ; 2024 Aug 21.
Article en En | MEDLINE | ID: mdl-39168154
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-minute PET scan on a Biograph mCT PET/CT scanner, starting at 60±5 minutes post-injection. The PET list-mode data were partitioned by subsampling without replacement, with 20 minutes used for image reconstruction using an in-house ordered subset expectation maximization algorithm and the remaining 2 minutes for cross-validation. Two penalty parameters, penalty strength ß and Fair penalty function parameter δ, were subjected to optimization. Whole-body images were reconstructed, and CVLL values were computed across various penalty parameter combinations. The optimal image corresponding to the maximum CVLL value was selected by a grid search for each patient. MAIN

RESULTS:

The δ value required to maximize the CVLL value was notably small (≤ 10-6 in this study). The influences of voxel size and scan duration on image optimization were investigated. A correlation analysis revealed a significant inverse relationship between optimal ß and scan count level, with a correlation coefficient of -0.68 (p-value = 3.5×10-5). The optimal images selected by the CVLL method were compared with those chosen by two radiologists based on their diagnostic preferences. Differences were observed in the selection of optimal images.

SIGNIFICANCE:

This study demonstrates the feasibility of incorporating the CVLL method into routine imaging protocols, potentially allowing for a wide range of combinations of injected radioactivity amounts and scan durations in modern PET imaging.
Palabras clave

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Phys Med Biol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Phys Med Biol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos