Self-masking noise subtraction (SMNS) in digital X-ray tomosynthesis for the improvement of tomographic image quality

In this paper, we proposed a simple and effective reconstruction algorithm, the so-called self-masking noise subtraction (SMNS), in digital X-ray tomosynthesis to reduce the tomographic blur that is inherent in the conventional tomosynthesis based upon the shift-and-add (SAA) method. Using the SAA and the SMNS algorithms, we investigated the influence of tomographic parameters such as tomographic angle (θ) and angle step (Δθ) on the image quality, measuring the signal-difference-to-noise ratio (SDNR). Our simulation results show that the proposed algorithm seems to be efficient in reducing the tomographic blur and, thus, improving image sharpness. We expect the simulation results to be useful for the optimal design of a digital X-ray tomosynthesis system for our ongoing application of nondestructive testing (NDT).