The Benefit of a Kernel Estimate Based Forward Projection for Iterative Tomographic Reconstruction Techniques

X-ray forward and backward projections are major steps in all iterative tomographic reconstruction techniques. To ensure the quality of the reconstruction results, the projector should fit the imaging system geometry and the x-ray physic as well as possible. In this paper, we intend to show the benefit of our proposed kernel estimate based forward projection for iterative tomographic reconstruction techniques, such as Algebraic Reconstruction Techniques (ART). The proposed projection method is independent to the volume sampling and takes into account the projection geometry (cone-beam and parallel) by simply adjusting the variable bandwidth of the kernel estimate. We show that the sample-point estimate (adaptive-bandwidth kernel estimate) based forward projection provides accurate projections and better reconstructions than the fixed bandwidth estimate based forward projection which is similar to the well known Splatting techniques.