Department of Nuclear Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand.
National Physical Laboratory (NPL), Teddington, Middlesex TW11 0LW, UK.
Sensors (Basel). 2021 Jan 15;21(2):591. doi: 10.3390/s21020591.
In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross-Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.
本文提出了一种基于蚁群优化（ACO）的方法，用于辅助选择基于全变差（TV）正则化的自适应加权投影控制最陡下降（AwPCSD）算法的超参数。在实现过程中，有一群人工蚂蚁在 AwPCSD 算法中进行搜索。每只蚂蚁都会选择一组迭代 CT 重建所需的超参数，并比较重建图像与参考图像的相关系数（CC）得分。一代蚂蚁会在其选择的路径上留下信息素，代表对超参数的选择。得分越高，信息素越强，下一世代吸引更多蚂蚁的概率就越高。在实现结束时，选择得分最高的超参数配置作为最优超参数集。在实验结果部分，将使用提出方法的超参数进行重建的结果与其他三种情况的结果进行了比较：共轭梯度最小二乘法（CGLS）、使用任意超参数集的 AwPCSD 算法和交叉验证方法。实验结果表明，提出方法的结果优于 CGLS 算法和使用任意超参数集的 AwPCSD 算法的结果。虽然 ACO 算法的结果在定量指标上略低于交叉验证方法，但 ACO 算法的速度比交叉验证快 10 倍以上。提出方法的最优超参数集对数据噪声的增加也具有鲁棒性，并且可以适用于具有相似背景的不同成像样本。提出方法中的 ACO 方法能够为数据集识别超参数的最优值，从而从有限数量的投影数据中生成高质量的重建图像。本文提出的方法成功解决了 TV 重建算法实施中的超参数选择问题，这是一个主要挑战。
