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There is an abundance of prior information available for imageguided radiotherapy, making it ideally suited for Bayesian techniques. I will demonstrate some results from applying the method of Teo, Sapiro & Wandell (1997) to conebeam computed tomography (CT). A previous CT scan of the same object forms the prior expectation. The posterior probabilities of class membership are smoothed by diffusion, before labeling each pixel according to the maximum a posteriori (MAP) estimate. The effect of the prior and of the smoothing is discussed and some potential extensions to this method are proposed.
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