While Computerized Tomography (CT) images can help detect disease such as Covid-19, regular CT machines are large and expensive. Cheaper and more portable machines suffer from errors in geometry acquisition that downgrades CT image quality. The errors in geometry can be represented with parameters in the mathematical model for image reconstruction. To obtain a good image, we formulate a nonlinear least squares problem that simultaneously reconstructs the image and corrects for errors in the geometry parameters. We develop an accelerated alternating minimization scheme to reconstruct the image and geometry parameters.

翻译：虽然计算机化的地形图象可以帮助检测Covid-19等疾病,但普通CT机器是大而昂贵的。 廉价的和更多的便携式机器在降低CT图像质量的几何获取中遭遇错误。 几何错误可以用数学模型中的参数来表示图像重建。 为了获得良好的图像, 我们设计了一个非线性最小方块问题, 同时重建图像, 纠正几何参数中的错误。 我们开发了一个加速交替最小化方案, 以重建图像和几何参数 。