Self-Masking Noise Subtraction (SMNS) in Digital X-Ray Tomosynthesis for the Improvement of Tomographic Image Quality
Abstract
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). © 2010 Elsevier B.V.
Recommended Citation
J. E. Oh and H. S. Cho and S. I. Choi and Y. O. Park and M. S. Lee and H. M. Cho and Y. J. Yang and U. K. Je and T. H. Woo and H. Lee, "Self-Masking Noise Subtraction (SMNS) in Digital X-Ray Tomosynthesis for the Improvement of Tomographic Image Quality," Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Elsevier, Jan 2011.
The definitive version is available at https://doi.org/10.1016/j.nima.2011.01.151
Department(s)
Nuclear Engineering and Radiation Science
Keywords and Phrases
Digital X-Ray Tomosynthesis; Image Blur; Self-Masking Noise Subtraction (SMNS); Shift-And-Add (SAA)
International Standard Serial Number (ISSN)
0168-9002
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2011 Elsevier, All rights reserved.
Publication Date
01 Jan 2011
