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A study of modeling x-ray transmittance for material decomposition without contrast agents
Author(s): Okkyun Lee; Steffen Kappler; Christoph Polster; Katsuyuki Taguchi
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Paper Abstract

This study concerns how to model x-ray transmittance, exp ( -- ∫ μa(r, E) dr), of the object using a small number of energy-dependent bases, which plays an important role for estimating basis line-integrals in photon counting detector (PCD)-based computed tomography (CT). Recently, we found that low-order polynomials can model the smooth x-ray transmittance, i.e. object without contrast agents, with sufficient accuracy, and developed a computationally efficient three-step estimator. The algorithm estimates the polynomial coefficients in the first step, estimates the basis line-integrals in the second step, and corrects for bias in the third step. We showed that the three-step estimator was ~1,500 times faster than conventional maximum likelihood (ML) estimator while it provided comparable bias and noise. The three-step estimator was derived based on the modeling of x-ray transmittance; thus, the accurate modeling of x-ray transmittance is an important issue. For this purpose, we introduce a modeling of the x-ray transmittance via dictionary learning approach. We show that the relative modeling error of dictionary learning-based approach is smaller than that of the low-order polynomials.

Paper Details

Date Published: 9 March 2017
PDF: 6 pages
Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101323G (9 March 2017); doi: 10.1117/12.2254688
Show Author Affiliations
Okkyun Lee, Johns Hopkins Univ. School of Medicine (United States)
Steffen Kappler, Siemens Healthcare GmbH (Germany)
Christoph Polster, Siemens Healthcare GmbH (Germany)
Ludwig-Maximilians-Univ. Hospital München (Germany)
Katsuyuki Taguchi, Johns Hopkins Univ. School of Medicine (United States)


Published in SPIE Proceedings Vol. 10132:
Medical Imaging 2017: Physics of Medical Imaging
Thomas G. Flohr; Joseph Y. Lo; Taly Gilat Schmidt, Editor(s)

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