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

Autor:	Wang Q; Department of Biomedical Engineering, University of California, Davis, CA, United States of America.; Department of Radiology, University of California, Davis, CA, United States of America., Abdelhafez YG; Department of Radiology, University of California, Davis, CA, United States of America.; Department of Radiotherapy and Nuclear Medicine, South Egypt Cancer Institute, Assiut University, Assiut, Egypt., Nalbant H; Department of Radiology, University of California, Davis, CA, United States of America., Spencer BA; Department of Biomedical Engineering, University of California, Davis, CA, United States of America.; Department of Radiology, University of California, Davis, CA, United States of America., Bayerlein R; Department of Biomedical Engineering, University of California, Davis, CA, United States of America.; Department of Radiology, University of California, Davis, CA, United States of America., Qi J; Department of Biomedical Engineering, University of California, Davis, CA, United States of America., Cherry SR; Department of Biomedical Engineering, University of California, Davis, CA, United States of America.; Department of Radiology, University of California, Davis, CA, United States of America., Nardo L; Department of Radiology, University of California, Davis, CA, United States of America., Badawi RD; Department of Biomedical Engineering, University of California, Davis, CA, United States of America.; Department of Radiology, University of California, Davis, CA, United States of America.
Jazyk:	angličtina
Zdroj:	Physics in medicine and biology [Phys Med Biol] 2024 Sep 03; Vol. 69 (18). Date of Electronic Publication: 2024 Sep 03.
DOI:	10.1088/1361-6560/ad7222
Abstrakt:	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 [ 18 F]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 subsampling without replacement, with 20 minutes of data for image reconstruction using an in-house ordered subset expectation maximization algorithm and the remaining 2 minutes of data for cross-validation. Two penalty parameters, penalty strength β 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 β 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. (© 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)
Databáze:	MEDLINE
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