Tomographic reconstruction using prior with adaptively binary neighborhood

Bayesian approaches can be used to improve ill-posed image reconstruction by regularizing the inverse solution using spacial or temporal neighborhood information. This paper proposes a prior with adaptively binary neighborhood (ABN prior) for statistical tomographic reconstruction. With binary weight map adapted to local image features, the proposed prior can lead to improved reconstruction by including relevant pixels belonging to similar structures and excluding those not. A two-step algorithm is also put forward for tomographic reconstruction using the proposed prior. Experiments using both simulated and clinical computerized tomography (CT) data are performed to validate the reconstructions with the proposed ABN prior.