Prior-image-based sparse-view cone-beam CT

Abstract

Computed tomography (CT) is widely used in medicine for diagnostics or for image-guided therapies, and is also popular in industrial applications for nondestructive testing. CT conventionally requires a large number of projections to produce volumetric images of a scanned object, because the conventional image reconstruction algorithm is based on filtered-backprojection. This requirement often results in relatively high radiation dose to the patients in medical CT, and expensive scanning time and efforts in industrial CT applications. Sparse-view CT is a viable option to address both issues including high radiation dose and expensive scanning efforts. However, image reconstruction from sparsely sampled data in CT is in general very challenging, and much efforts have been made to develop algorithms for such an image reconstruction problem. Image total-variation minimization algorithm inspired by compressive sensing theory has recently been developed, which exploits the sparseness of the image derivative magnitude and can reconstruct images from sparse-view data to similar quality of the images conventionally reconstructed from many views. In certain CT applications, prior CT image of the object is readily available from which current CT image may have only slight differences. Considering the sparseness of such a difference image between the successive scans, image reconstruction of the difference image may be achieved from very sparsely sampled data. In this work, i showed that one can further reduce the number of projections, resulting in a super-sparse scan, for a good quality image reconstruction with the aid of a prior image. Both numerical and experimental results are provided. The proposed method is believed to play an important role in repeated CT scans to lower imaging radiation dose and also scanning time and efforts.