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Proceedings Paper

A spectral CT denoising algorithm based on weighted block matching 3D filtering
Author(s): Morteza Salehjahromi; Yanbo Zhang; Hengyong Yu
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Paper Abstract

In spectral CT, an energy-resolving detector is capable of counting the number of received photons in different energy channels with appropriate post-processing steps. Because the received photon number in each energy channel is low in practice, the generated projections suffer from low signal-to-noise ratio. This poses a challenge to perform image reconstruction of spectral CT. Because the reconstructed multi-channel images are for the same object but in different energies, there is a high correlation among these images and one can make full use of this redundant information. In this work, we propose a weighted block-matching and three-dimensional (3-D) filtering (BM3D) based method for spectral CT denoising. It is based on denoising of small 3-D data arrays formed by grouping similar 2-D blocks from the whole 3-D data image. This method consists of the following two steps. First, a 2-D image is obtained using the filtered back-projection (FBP) in each energy channel. Second, the proposed weighted BM3D filtering is performed. It not only uses the spatial correlation within each channel image but also exploits the spectral correlation among the channel images. The proposed method is evaluated on both numerical simulation and realistic preclinical datasets, and its merits are demonstrated by the promising results.

Paper Details

Date Published: 26 September 2017
PDF: 12 pages
Proc. SPIE 10391, Developments in X-Ray Tomography XI, 103910G (26 September 2017); doi: 10.1117/12.2273213
Show Author Affiliations
Morteza Salehjahromi, Univ. of Massachusetts Lowell (United States)
Yanbo Zhang, Univ. of Massachusetts Lowell (United States)
Hengyong Yu, Univ. of Massachusetts Lowell (United States)

Published in SPIE Proceedings Vol. 10391:
Developments in X-Ray Tomography XI
Bert Müller; Ge Wang, Editor(s)

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