A new approach to limited angle tomography using the compressed sensing framework

The limited angle problem is a well-known problem in computed tomography. It is caused by missing data over a certain angle interval, which make an inverse Radon transform impossible. In daily routine this problem can arise for example in tomosynthesis, C-arm CT or dental CT. In the last years there has been a big development in the field of compressed sensing algorithms in computed tomography, which deal very good with incomplete data. The most popular way is to integrate a minimal total variation norm in form of a cost function into the iteration process. To find an exact solution of such a constrained minimization problem, computationally very demanding higher order algorithms should be used. Due to the non perfect sparsity of the total variation representation, reconstructions often show the so called staircase effect. The method proposed here uses the solutions of the iteration process as an estimation for the missing angle data. Compared to a pure compressed sensing-based algorithm we reached much better results within the same number of iterations and could eliminate the staircase effect. The algorithm is evaluated using measured clinical datasets.