Maximum a posteriori X-ray computed tomography using graph cuts

We develop maximum a posteriori (MAP) method for X-ray computed tomography (CT). We present a mixture prior to represent the knowledge that the human body is composed of a finite number of material kinds whose CT values are roughly known in advance. The homographic image and material classes are simultaneously estimated in an alternating manner, where a graph cut algorithm is used to minimize the MAP objective function. Experiments show that the proposed algorithm performs better than the existing methods in severe situations where samples are limited or metals are inserted into the body.