Hyperparameter-controlled regularized reconstruction method based on object structure and acquisition conditions in...

In clinical nuclear medicine, reconstruction methods incorporating regularization terms have been widely investigated. However, searching for optimal hyperparameters for the entire examination is time-consuming and arduous because the optimal hyperparameters need to be determined experimentally and vary depending on factors, including the acquisition condition, reconstruction condition, and so on. In this study, we propose a row-action type automatic regularized expectation maximization method (RAREM). This method considers the acquisition conditions and object structure for determining the hyperparameters and does not require the user to set the hyperparameters experimentally. This study was conducted using numerical simulations and a real SPECT system METHODS: Total variation-expectation maximization (TV-EM) and modified-block sequential regularized EM (BSREM) were compared with RAREM, with the optimal hyperparameters of the two conventional reconstruction methods determined in advance from normalized root mean square error (NRMSE) results. This simulation examination utilized three types of phantoms with the number of counts and projections being examined in six ways each, resulting in a total of 108 conditions. The NRMSE and structural similarity index measure (SSIM) were used to evaluate of the simulation examination, and the Mann-Whitney U test was used for statistical analysis. In the real examination, two types of phantoms were used, and the number of projections was examined in three ways, for a total of six conditions. Contrast recovery coefficient (CRC) and specific binding ratio (SBR) were used to evaluate the real examination RESULTS: The NRMSE, CRC, and SBR of RAREM were equivalent to those of the conventional methods, and the SSIM of RAREM was equivalent to or better than that of the conventional methods, with significant differences in some cases. The results indicated that RAREM worked well with the evaluated object structure and considered the acquisition conditions CONCLUSION: In this study, an automatically controlled regularization reconstruction method was proposed. The proposed method does not require the user to set hyperparameters experimentally and can avoid the investigation of optimal hyperparameters; it is an alternative to conventional regularized methods in clinical.