We introduce a stochastic framework into the open--source Core Imaging Library (CIL) which enables easy development of stochastic algorithms. Five such algorithms from the literature are developed, Stochastic Gradient Descent, Stochastic Average Gradient (-Am\'elior\'e), (Loopless) Stochastic Variance Reduced Gradient. We showcase the functionality of the framework with a comparative study against a deterministic algorithm on a simulated 2D PET dataset, with the use of the open-source Synergistic Image Reconstruction Framework. We observe that stochastic optimisation methods can converge in fewer passes of the data than a standard deterministic algorithm.

翻译：我们在开源核心成像库（CIL）中引入随机优化框架，该框架支持随机算法的便捷开发。我们实现了文献中的五种随机算法：随机梯度下降法、随机平均梯度法（及其改进版本）、（无循环）随机方差缩减梯度法。通过使用开源协同图像重建框架，我们在模拟二维PET数据集上开展对比研究，将随机算法与确定性算法进行性能比较，以此展示该框架的功能特性。实验结果表明，随机优化方法相比标准确定性算法能够以更少的数据遍历次数达到收敛。