IEEE Trans Med Imaging. 2018 Apr;37(4):1000-1010. doi: 10.1109/TMI.2017.2786865.
This paper reports on the feasibility of using a quasi-Newton optimization algorithm, limited-memory Broyden-Fletcher-Goldfarb-Shanno with boundary constraints (L-BFGS-B), for penalized image reconstruction problems in emission tomography (ET). For further acceleration, an additional preconditioning technique based on a diagonal approximation of the Hessian was introduced. The convergence rate of L-BFGS-B and the proposed preconditioned algorithm (L-BFGS-B-PC) was evaluated with simulated data with various factors, such as the noise level, penalty type, penalty strength and background level. Data of three F-FDG patient acquisitions were also reconstructed. Results showed that the proposed L-BFGS-B-PC outperforms L-BFGS-B in convergence rate for all simulated conditions and the patient data. Based on these results, L-BFGS-B-PC shows promise for clinical application.
本文报告了在发射断层成像（ET）中使用拟牛顿优化算法、带边界约束的有限内存 Broyden-Fletcher-Goldfarb-Shanno（L-BFGS-B）对惩罚图像重建问题的可行性。为了进一步加速，引入了一种基于海森近似的附加预处理技术。使用具有不同噪声水平、惩罚类型、惩罚强度和背景水平等因素的模拟数据评估了 L-BFGS-B 和所提出的预处理算法（L-BFGS-B-PC）的收敛速度。还对三名 F-FDG 患者采集的数据进行了重建。结果表明，在所模拟的所有条件和患者数据中，所提出的 L-BFGS-B-PC 在收敛速度上均优于 L-BFGS-B。基于这些结果，L-BFGS-B-PC 有望应用于临床。
