CT technology has been widely used in various fields such as medical treatment, industry, and materials. Recently years, cone-beam CT (CBCT) used in the medical field is replacing the traditional spiral CT slowly due to its unique advantages. Improving the performance of CBCT and the image reconstruction algorithms could obtain higher quality of images, meanwhile, these also reduce the exposure time of X-ray irradiation. And image reconstruction techniques based on sparse angles have benefits for both. This article briefly introduces the advantages of CBCT and the shortcomings of spiral CT, then the traditional filtered projection algorithm Feldkamp is explained. The development of the CBCT reconstruction algorithms based on incomplete data is analyzed from three aspects: TV model, dictionary learning and compressed sensing sampling. The advantages and disadvantages of these algorithms are analyzed for the development of new algorithms.
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