STATISTICALLY RECONSTRUCTING AN X-RAY COMPUTED TOMOGRAPHY IMAGE WITH BEAM HARDENING CORRECTOR

摘要

A method for statistically reconstructing an X-ray computed tomography image produced by a single X-ray CT scan having a polenergetic source spectrum and an image reconstructor which utilize a convergent statistical algorithm which explicitly accounts for the polyenergetic source spectrum are provided. First and second related statistical iterative methods for CT reconstruction based on a Poisson statistical model are described. Both methods are accelerated by the use of ordered subsets, which replace sums over the angular index of a sinogram with a series of sums over angular subsets of the sinogram. The first method is generalized to model the more realistic case of polyenergetic computed tomography (CT). The second method eliminates beam hardening artifacts seen when filtered back projection (FBP) is used without post-processing correction. The method are superior to FBP reconstruction in terms of noise reduction. The methods are superior to FBP reconstruction in terms of noise reduction. The method and image reconstructor of the invention are effective in producing corrected images that do not suffer from beam hardening effects.