  Iterative methods for tomographic image reconstruction have great potential
for enabling high quality imaging from low-dose projection data. The
computational burden of iterative reconstruction algorithms, however, has been
an impediment in their adoption in practical CT reconstruction problems. We
present an approach for highly efficient and accurate computation of forward
model for image reconstruction in fan-beam geometry in X-ray CT. The efficiency
of computations makes this approach suitable for large-scale optimization
algorithms with on-the-fly, memory-less, computations of the forward and
back-projection. Our experiments demonstrate the improvements in accuracy as
well 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.
