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.

中文翻译：

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基于nt菌落的超参数优化在X射线计算机断层扫描中的总变异重建中

本文提出了一种计算机辅助训练方法，用于有限数据X射线计算机断层扫描（XCT）重建的超参数选择。所提出的方法采用蚁群优化（ACO）方法来辅助自适应加权投影控制最速下降（AwPCSD）算法的超参数选择，该算法是基于总变量（TV）的正则化算法。在实施过程中，有大量的人工蚂蚁聚集在AwPCSD算法中。每只蚂蚁都选择了其迭代CT重建所需的一组超参数，并给出了与参考图像相比重建图像的相关系数（CC）分数。一代人的蚁群在其选择的路径上留下了信息素，代表了超参数的选择。更高的分数意味着更强的信息素/概率来吸引下一代的更多蚂蚁。在实施的最后，选择得分最高的超参数配置作为最佳的超参数集。在实验结果部分，将使用所提出方法的超参数重建与其他三种情况的结果进行了比较：共轭梯度最小二乘（CGLS），使用任意超参数集的AwPCSD算法和交叉验证方法。结果表明，所提出的方法的结果优于使用任意超参数集的CGLS算法和AwPCSD算法的结果。尽管按量化指标衡量，ACO算法的结果略逊于交叉验证方法的结果，ACO算法比交叉验证快10倍以上。所提出方法的最佳超参数集对于数据中噪声的增加也很稳定，并且可以适用于具有相似上下文的不同成像样本。所提出的方法中的ACO方法能够为数据集识别超参数的最佳值，因此，可以从有限数量的投影数据中生成高质量的重建图像。这项工作中提出的方法成功解决了超参数选择的问题，这是实现基于电视的重构算法的主要挑战。所提出方法的最佳超参数集对于数据中噪声的增加也很稳定，并且可以适用于具有相似上下文的不同成像样本。提出的方法中的ACO方法能够为数据集识别超参数的最佳值，因此，可以从有限数量的投影数据中生成高质量的重建图像。这项工作中提出的方法成功解决了超参数选择的问题，这是实现基于电视的重构算法的主要挑战。所提出方法的最佳超参数集对于数据中噪声的增加也很稳定，并且可以适用于具有相似上下文的不同成像样本。提出的方法中的ACO方法能够为数据集识别超参数的最佳值，因此，可以从有限数量的投影数据中生成高质量的重建图像。这项工作中提出的方法成功解决了超参数选择的问题，这是实现基于电视的重构算法的主要挑战。