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Image reconstruction by Algebraic Methods (AM) outperforms the transformmethods in situations where the data collection procedure is constrained bytime, space, and radiation dose. AM algorithms can also be applied for thecases where these constraints are not present but their high computational andstorage requirement prohibit their actual breakthrough in such cases. In thepresent work, we propose a novel Uniformly Sampled Polar/Cylindrical Grid(USPG/USCG) discretization scheme to reduce the computational and storageburden of algebraic methods. The symmetries of USPG/USCG are utilized to speedup the calculations of the projection coefficients. In addition, we also offeran efficient approach for USPG to Cartesian Grid (CG) transformation for thevisualization. The Multiplicative Algebraic Reconstruction Technique (MART) hasbeen used to determine the field function of the suggested grids. Experimentalprojections data of a frog and Cu-Lump have been exercised to validate theproposed approach. A variety of image quality measures have been evaluated tocheck the accuracy of the reconstruction. Results indicate that the currentstrategies speed up (when compared to CG-based algorithms) the reconstructionprocess by a factor of 2.5 and reduce the memory requirement by the factor p,the number of projections used in the reconstruction.

Uniformly Sampled Polar and Cylindrical Grid Approach for 2D, 3D Image Reconstruction using Algebraic Algorithm