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Due to its wide field of view, cone-beam computed tomography (CBCT) isplagued by large amounts of scatter, where attenuated photons hit the detector,and corrupt the linear models used for reconstruction. Given that one cangenerate a good estimate of scatter however, then image accuracy can beretained. In the context of adaptive radiotherapy, one usually has alow-scatter planning CT image of the same patient at an earlier time.Correcting for scatter in the subsequent CBCT scan can either be selfconsistent with the new measurements or exploit the prior image, and there areseveral recent methods that report high accuracy with the latter. In thisstudy, we will look at the accuracy of various scatter estimation methods, howthey can be effectively incorporated into a statistical reconstructionalgorithm, along with introducing a method for matching off-line Monte-Carlo(MC) prior estimates to the new measurements. Conclusions we draw from testingon a neck cancer patient are: statistical reconstruction that incorporates thescatter estimate significantly outperforms analytic and iterative methods withpre-correction; and although the most accurate scatter estimates can be madefrom the MC on planning image, they only offer a slight advantage over themeasurement based scatter kernel superposition (SKS) in reconstruction error.

Can Planning Images Reduce Scatter in Follow-Up Cone-Beam CT?