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Proceedings Paper

Multi-domain constraint based one-step selective-reconstruction method for spectral micro-CT
Author(s): Qian Wang; Yining Zhu; Hengyong Yu
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Paper Abstract

X-ray micro-computed tomography (micro-CT) achieves a spatial resolution of micron or submicron and is well-applied in many fields such as biomedicine, materials and electronic packaging. However, it suffers from low contrast and weak material distinguishability because of its low power of the X-ray source comparing with industrial or medical X-ray source. Recently, we adapt a state-of-the-art photon-counting detector (PCD) in a micro-CT system, leading a spectral Micro-CT. By dividing the X-ray photons into different energy bins, the PCD well maintains the energy-dependent property of matter attenuation and then contributes to selective-reconstruction. The selective-reconstruction problems of spectral micro-CT are ill-posed, i.e., the solution is very sensitive to noise. Meanwhile, for each PCD energy channel, the corresponding photon number is only a small fraction of the emitted photons, which further increases the noise level. To overcome the ill-posedness, in this work, we propose a multi-domain constraint based optimization model for one-step selective-reconstruction. First, we measure the data fidelity in photon domain using the Kullback-Leibler distance (Idivergence) and derive an equivalent expression in channel projection domain. Then, we introduce multi-domain constraints to establish the relationship among channel projections, material projections, and material images. After that, we employ the Mumford-Shah (MS) functional to describe the prior knowledge in the material image domain, such as gradient sparsity and edge information. Finally, we develop an iterative algorithm and verify it with numerical simulations.

Paper Details

Date Published: 18 September 2018
PDF: 13 pages
Proc. SPIE 10772, Unconventional and Indirect Imaging, Image Reconstruction, and Wavefront Sensing 2018, 107720M (18 September 2018); doi: 10.1117/12.2319061
Show Author Affiliations
Qian Wang, Univ. of Massachusetts Lowell (United States)
Yining Zhu, Capital Normal Univ. (China)
Beijing Higher Institution Engineering Research Ctr. of Testing and Imaging (China)
Hengyong Yu, Univ. of Massachusetts Lowell (United States)

Published in SPIE Proceedings Vol. 10772:
Unconventional and Indirect Imaging, Image Reconstruction, and Wavefront Sensing 2018
Jean J. Dolne; Philip J. Bones, Editor(s)

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