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Iterative methods for tomographic image reconstruction have great potentialfor enabling high quality imaging from low-dose projection data. Thecomputational burden of iterative reconstruction algorithms, however, has beenan impediment in their adoption in practical CT reconstruction problems. Wepresent an approach for highly efficient and accurate computation of forwardmodel for image reconstruction in fan-beam geometry in X-ray CT. The efficiencyof computations makes this approach suitable for large-scale optimizationalgorithms with on-the-fly, memory-less, computations of the forward andback-projection. Our experiments demonstrate the improvements in accuracy aswell as efficiency of our model, specifically for first-order box splines(i.e., pixel-basis) compared to recently developed methods for this purpose,namely Look-up Table-based Ray Integration (LTRI) and Separable Footprints (SF)in 2-D.

A Convolutional Forward and Back-Projection Model for Fan-Beam Geometry