Minagawa Tomoya, Hori Kensuke, Hashimoto Takeyuki
Department of Radiology, Toho University Ohashi Medical Center, 2-22-36 Ohashi, Meguro-ku, Tokyo, 153-8515, Japan.
Department of Medical Radiological Technology, Graduate School of Health Sciences, Kyorin University, 5-4-1 Shimorenjaku, Mitaka-shi, Tokyo, 181-8612, Japan.
EJNMMI Phys. 2025 Jul 29;12(1):73. doi: 10.1186/s40658-025-00788-7.
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.
在临床核医学中，结合正则化项的重建方法已得到广泛研究。然而，为整个检查寻找最优超参数既耗时又费力，因为最优超参数需要通过实验确定且会因采集条件、重建条件等因素而有所不同。在本研究中，我们提出了一种行作用式自动正则化期望最大化方法（RAREM）。该方法在确定超参数时考虑了采集条件和对象结构，无需用户通过实验设置超参数。本研究使用数值模拟和真实的单光子发射计算机断层扫描（SPECT）系统进行。方法：将总变差期望最大化（TV-EM）和改进的块序贯正则化期望最大化（BSREM）与RAREM进行比较，两种传统重建方法的最优超参数预先根据归一化均方根误差（NRMSE）结果确定。该模拟检查使用了三种类型的体模，每种体模的计数和投影数量以六种方式进行检查，总共产生108种情况。使用NRMSE和结构相似性指数测量（SSIM）对模拟检查进行评估，并使用曼-惠特尼U检验进行统计分析。在实际检查中，使用了两种类型的体模，投影数量以三种方式进行检查，总共六种情况。使用对比恢复系数（CRC）和特异性结合率（SBR）对实际检查进行评估。结果：RAREM的NRMSE、CRC和SBR与传统方法相当，且RAREM的SSIM与传统方法相当或更好，在某些情况下存在显著差异。结果表明，RAREM在评估的对象结构上表现良好，并考虑了采集条件。结论：在本研究中，提出了一种自动控制的正则化重建方法。所提出的方法无需用户通过实验设置超参数，可避免对最优超参数的研究；它是临床中传统正则化方法的一种替代方法。
