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