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.
Phys Med Biol. 2024 Sep 3;69(18). doi: 10.1088/1361-6560/ad7222.
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.Fifteen lung cancer patients were injected with 243.7 ± 23.8 MBq of [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 strengthand Fair penalty function parameter, 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.Thevalue required to maximize the CVLL value was notably small (⩽10in this study). The influences of voxel size and scan duration on image optimization were investigated. A correlation analysis revealed a significant inverse relationship between optimaland scan count level, with a correlation coefficient of -0.68 (-value = 3.5 × 10). 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.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.
惩罚参数在惩罚似然正电子发射断层扫描 (PET) 重建中通常是经验确定的。交叉验证对数似然 (CVLL) 方法已被引入，通过最大化 CVLL 函数来优化这些参数，该函数使用列表模式数据集的一个子集基于另一个子集来评估重建图像的似然性。这项研究旨在使用传统临床 PET 扫描仪验证 CVLL 方法在癌症患者全身成像中的功效。
15 名肺癌患者注射了 243.7 ± 23.8 MBq 的 [F]FDG，并在 Biograph mCT PET/CT 扫描仪上进行了 22 分钟的 PET 扫描，从注射后 60 ± 5 分钟开始。通过无替换的子抽样对 PET 列表模式数据进行分区，使用内部有序子集期望最大化算法对 20 分钟的数据进行图像重建，对剩余的 2 分钟的数据进行交叉验证。对两种惩罚参数，惩罚强度和公平惩罚函数参数进行了优化。对全身图像进行重建，并计算了各种惩罚参数组合下的 CVLL 值。通过对每位患者进行网格搜索，选择对应于最大 CVLL 值的最佳图像。
需要最大化 CVLL 值的取值非常小（在本研究中⩽10）。研究还调查了体素大小和扫描持续时间对图像优化的影响。相关性分析显示，最佳值与扫描计数水平之间存在显著的负相关关系，相关系数为-0.68（-值=3.5×10）。根据诊断偏好，将 CVLL 方法选择的最佳图像与两位放射科医生选择的最佳图像进行比较。观察到在最佳图像的选择上存在差异。
这项研究表明，将 CVLL 方法纳入常规成像方案是可行的，这可能允许在现代 PET 成像中组合使用多种不同的放射性药物注射量和扫描持续时间。
