  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.

Image and Video Processing;Numerical Analysis;Numerical Analysis