Joint-MAP Tomographic Reconstruction with Patch Similarity Based Mixture Prior Model

Tomographic reconstruction from noisy projections do not yield adequate results. Mathematically, this tomographic reconstruction represents an ill-imposed problem due to information missing caused from the presence of noise. Maximum A Posteriori (MAP) or Bayesian reconstruction methods offer possibilities to improve the image quality as compared with analytical methods in particular by introducing a prior to guide the reconstruction and regularize the noise. With an aim to achieve robust utilization of continuity/connectivity information and overcome the heuristic weight update for other nonlocal prior methods, this paper proposed a novel Patch Similarity based Mixture (PSM) prior model for tomographic reconstruction. This prior is defined by a weighted Gaussian distance between neighborhood intensities. The weight quantifies the similarity between local neighborhoods and is computed using a maximization entropy constraint. This prior is then introduced within a joint image/weight MAP CT reconstruction algorithm. Several acceleration trials including Compute Unified Device Architecture (CUDA) parallelization is applied to alleviate the intensive patch distance computation involved in the joint algorithm. The method was tested with both synthetic phantoms and clinical CT data and compared in accuracy with five other reconstruction algorithms which are FBP and Bayesian-based. Reconstruction results show that the proposed reconstructions are able to produce high-quality images with ensured iteration convergence.