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Proceedings Paper

Statistical x-ray computed tomography imaging from photon-starved measurements
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Paper Abstract

Dose reduction in clinical X-ray computed tomography (CT) causes low signal-to-noise ratio (SNR) in photonsparse situations. Statistical iterative reconstruction algorithms have the advantage of retaining image quality while reducing input dosage, but they meet their limits of practicality when significant portions of the sinogram near photon starvation. The corruption of electronic noise leads to measured photon counts taking on negative values, posing a problem for the log() operation in preprocessing of data. In this paper, we propose two categories of projection correction methods: an adaptive denoising filter and Bayesian inference. The denoising filter is easy to implement and preserves local statistics, but it introduces correlation between channels and may affect image resolution. Bayesian inference is a point-wise estimation based on measurements and prior information. Both approaches help improve diagnostic image quality at dramatically reduced dosage.

Paper Details

Date Published: 7 March 2014
PDF: 12 pages
Proc. SPIE 9020, Computational Imaging XII, 90200G (7 March 2014); doi: 10.1117/12.2048204
Show Author Affiliations
Zhiqian Chang, Univ. of Notre Dame (United States)
Ruoqiao Zhang, Purdue Univ. (United States)
Jean-Baptiste Thibault, GE Healthcare (United States)
Ken Sauer, Univ. of Notre Dame (United States)
Charles Bouman, Purdue Univ. (United States)

Published in SPIE Proceedings Vol. 9020:
Computational Imaging XII
Charles A. Bouman; Ken D. Sauer, Editor(s)

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