@inproceedings{a0bc9e43109f4ecd879f940746403c83,
title = "A study of modeling x-ray transmittance for material decomposition without contrast agents",
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.",
keywords = "Dictionary learning, Low-order polynomial, Photon counting, X-ray transmittance",
author = "Okkyun Lee and Steffen Kappler and Christoph Polster and Katsuyuki Taguchi",
note = "Publisher Copyright: {\textcopyright} 2017 SPIE.; Medical Imaging 2017: Physics of Medical Imaging ; Conference date: 13-02-2017 Through 16-02-2017",
year = "2017",
doi = "10.1117/12.2254688",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Schmidt, {Taly Gilat} and Lo, {Joseph Y.} and Flohr, {Thomas G.}",
booktitle = "Medical Imaging 2017",
}