Due to the COVID-19 pandemic, there is an increasing demand for portable CT machines worldwide in order to diagnose patients in a variety of settings. This has led to a need for CT image reconstruction algorithms that can produce high quality images in the case when multiple types of geometry parameters have been perturbed. In this paper we present an alternating minimization algorithm to address this issue, where one step minimizes a regularized linear least squares problem, and the other step minimizes a bounded non-linear least squares problem. Additionally, we survey existing methods to accelerate convergence of the algorithm and discuss implementation details. Finally, numerical experiments are conducted to illustrate the effectiveness of the algorithm.

翻译：由于COVID-19大流行,全世界对便携式CT机器的需求日益增加,以便在各种情况下诊断病人,这导致需要CT图像重建算法,在多类几何参数受到干扰的情况下,这种算法可以产生高质量的图像。在本文中,我们提出一种交替的最小化算法来解决这一问题,其中一步可以最大限度地减少正常化线性最小方位问题,另一步可以最大限度地减少非线性最小方位问题。此外,我们调查现有的方法,以加快算法的趋同,并讨论执行细节。最后,进行了数字实验,以说明算法的有效性。