Moment preserving tomographic image reconstruction model

Shape descriptors provide valuable prior information in many tomographic image reconstruction methods. Such descriptors include, among others, centroid, circularity, orientation, and elongation. Shape descriptor measures are often analytically expressed as a composition of certain geometric moments. Building upon this fact, this paper suggests preserving the values of a specific geometric moment in the reconstruction process, instead of preserving entire descriptors, as it has been suggested so far. Reconstructions from two natural projection directions (vertical and horizontal) are considered with special attention. The provided theoretical analysis demonstrates that preserving the value of a specific geometric moment, provided as prior information for the reconstruction process, simultaneously ensures the preservation of the true measures of all four abovementioned descriptors. Based on this result, a novel regularized energy minimization reconstruction model is proposed. The minimization task of the new model is solved using gradient-based optimization algorithm. Performance evaluation of the proposed method is supported by experimental results obtained through comparisons with other well-known reconstruction methods. •The paper introduces a new geometrical moment preserving based tomographic reconstruction model.•The preserved moment implies preservation of following descriptors: orientation, elongation, centroid and circularity.•Analytical gradient of the proposed energy functions is derived, which makes the minimization process more efficient.