Image reconstruction in cardiac interventions using a small flat-panel detector

C-arm CT is used in neurovascular interventions where a large flat panel detector is used to acquire cone-beam projection data. In this case, data truncation problems due to the limited detector size are mild. When the cone beam CT method is applied to cardiac interventions severe data truncation artifacts reduce the clinical utility of the reconstructions. However, accurate reconstruction is still possible given a priori knowledge of the reconstruction values within a small region inside the FOV. Several groups have studied the case of the interior problem where data is truncated from all views. In this paper, we applied these new mathematical discoveries to C-arm cardiac cone-beam CT to demonstrate that accurate image reconstruction may be achieved for cardiac interventions. The method is applied to iteratively reconstruct the image volume such that it satisfies several physical conditions. In this work, the algorithm is applied to data from in-vivo cardiac canine studies collected using a clinical C-arm system. It is demonstrated that the algorithm converges well to the reconstruction values of non-truncated data reconstructed using the FDK algorithm. Furthermore, proper convergence is achieved by using only an estimate of the average value within a subregion as a priori information (i.e. the exact value at each pixel in the a priori region need not be known). Two methods for obtaining a priori information are compared.