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Proceedings Paper

Statistical signal estimation methods in X-ray differential phase contrast imaging
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Paper Abstract

In x-ray differential phase contrast imaging (DPCI), a simple sinusoidal model is used to describe the measured signal intensities. When there is no noise added to the measurements, one is able to estimate the three unknown parameters, absorption contrast, phase-contrast, and small angle scatter, directly from the signal model. However, for x-ray detections, noise is always present and the noise level increases with a decrease in x-ray flux. When noise is involved, an experimental ensemble average is needed to make the above parameter estimation method accurate. Unfortunately, an ensemble average requires a large number of measurements acquired under identical experimental conditions. Such repetition procedures would significantly prolong the data acquisition time and require additional radiation dose to the image object. Therefore, it is desirable to develop a reliable parameter estimation method from a group of single x-ray signal intensity measurements. This paper addresses this concern by proposing a statistical signal estimation method. This method maximizes the joint probability of measurements in a complete phase stepping period and is able to estimate the three unknowns in DPCI simultaneously. Our numerical simulations demonstrate that the statistical signal estimation method outperformed the current blind estimation method by minimizing the noise variance of each parameter. As a result, this method has the potential to further improve the dose efficiency of DPCI.

Paper Details

Date Published: 19 March 2014
PDF: 7 pages
Proc. SPIE 9033, Medical Imaging 2014: Physics of Medical Imaging, 90330G (19 March 2014); doi: 10.1117/12.2043938
Show Author Affiliations
Yongshuai Ge, Univ. of Wisconsin-Madison (United States)
Ke Li, Univ. of Wisconsin-Madison (United States)
Guang-Hong Chen, Univ. of Wisconsin-Madison (United States)


Published in SPIE Proceedings Vol. 9033:
Medical Imaging 2014: Physics of Medical Imaging
Bruce R. Whiting; Christoph Hoeschen, Editor(s)

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