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Title: A full-spectral Bayesian reconstruction approach based on the material decomposition model applied in dual-energy computed tomography

Abstract

Purpose: Dual-energy computed tomography (DECT) makes it possible to get two fractions of basis materials without segmentation. One is the soft-tissue equivalent water fraction and the other is the hard-matter equivalent bone fraction. Practical DECT measurements are usually obtained with polychromatic x-ray beams. Existing reconstruction approaches based on linear forward models without counting the beam polychromaticity fail to estimate the correct decomposition fractions and result in beam-hardening artifacts (BHA). The existing BHA correction approaches either need to refer to calibration measurements or suffer from the noise amplification caused by the negative-log preprocessing and the ill-conditioned water and bone separation problem. To overcome these problems, statistical DECT reconstruction approaches based on nonlinear forward models counting the beam polychromaticity show great potential for giving accurate fraction images.Methods: This work proposes a full-spectral Bayesian reconstruction approach which allows the reconstruction of high quality fraction images from ordinary polychromatic measurements. This approach is based on a Gaussian noise model with unknown variance assigned directly to the projections without taking negative-log. Referring to Bayesian inferences, the decomposition fractions and observation variance are estimated by using the joint maximum a posteriori (MAP) estimation method. Subject to an adaptive prior model assigned to the variance, the jointmore » estimation problem is then simplified into a single estimation problem. It transforms the joint MAP estimation problem into a minimization problem with a nonquadratic cost function. To solve it, the use of a monotone conjugate gradient algorithm with suboptimal descent steps is proposed.Results: The performance of the proposed approach is analyzed with both simulated and experimental data. The results show that the proposed Bayesian approach is robust to noise and materials. It is also necessary to have the accurate spectrum information about the source-detector system. When dealing with experimental data, the spectrum can be predicted by a Monte Carlo simulator. For the materials between water and bone, less than 5% separation errors are observed on the estimated decomposition fractions.Conclusions: The proposed approach is a statistical reconstruction approach based on a nonlinear forward model counting the full beam polychromaticity and applied directly to the projections without taking negative-log. Compared to the approaches based on linear forward models and the BHA correction approaches, it has advantages in noise robustness and reconstruction accuracy.« less

Authors:
 [1]; ;  [2];  [3]
  1. CEA, LIST, 91191 Gif-sur-Yvette, France and CNRS, SUPELEC, UNIV PARIS SUD, L2S, 3 rue Joliot-Curie, 91192 Gif-sur-Yvette (France)
  2. CNRS, SUPELEC, UNIV PARIS SUD, L2S, 3 rue Joliot-Curie, 91192 Gif-sur-Yvette (France)
  3. CEA, LIST, 91191 Gif-sur-Yvette (France)
Publication Date:
OSTI Identifier:
22220269
Resource Type:
Journal Article
Journal Name:
Medical Physics
Additional Journal Information:
Journal Volume: 40; Journal Issue: 11; Other Information: (c) 2013 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-2405
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; ACCURACY; ALGORITHMS; BEAMS; CALIBRATION; COMPUTERIZED TOMOGRAPHY; CORRECTIONS; IMAGE PROCESSING; IMAGES; MINIMIZATION; MONTE CARLO METHOD; NOISE; SKELETON; WATER; X RADIATION

Citation Formats

Cai, C., Rodet, T., Mohammad-Djafari, A., and Legoupil, S. A full-spectral Bayesian reconstruction approach based on the material decomposition model applied in dual-energy computed tomography. United States: N. p., 2013. Web. doi:10.1118/1.4820478.
Cai, C., Rodet, T., Mohammad-Djafari, A., & Legoupil, S. A full-spectral Bayesian reconstruction approach based on the material decomposition model applied in dual-energy computed tomography. United States. https://doi.org/10.1118/1.4820478
Cai, C., Rodet, T., Mohammad-Djafari, A., and Legoupil, S. 2013. "A full-spectral Bayesian reconstruction approach based on the material decomposition model applied in dual-energy computed tomography". United States. https://doi.org/10.1118/1.4820478.
@article{osti_22220269,
title = {A full-spectral Bayesian reconstruction approach based on the material decomposition model applied in dual-energy computed tomography},
author = {Cai, C. and Rodet, T. and Mohammad-Djafari, A. and Legoupil, S.},
abstractNote = {Purpose: Dual-energy computed tomography (DECT) makes it possible to get two fractions of basis materials without segmentation. One is the soft-tissue equivalent water fraction and the other is the hard-matter equivalent bone fraction. Practical DECT measurements are usually obtained with polychromatic x-ray beams. Existing reconstruction approaches based on linear forward models without counting the beam polychromaticity fail to estimate the correct decomposition fractions and result in beam-hardening artifacts (BHA). The existing BHA correction approaches either need to refer to calibration measurements or suffer from the noise amplification caused by the negative-log preprocessing and the ill-conditioned water and bone separation problem. To overcome these problems, statistical DECT reconstruction approaches based on nonlinear forward models counting the beam polychromaticity show great potential for giving accurate fraction images.Methods: This work proposes a full-spectral Bayesian reconstruction approach which allows the reconstruction of high quality fraction images from ordinary polychromatic measurements. This approach is based on a Gaussian noise model with unknown variance assigned directly to the projections without taking negative-log. Referring to Bayesian inferences, the decomposition fractions and observation variance are estimated by using the joint maximum a posteriori (MAP) estimation method. Subject to an adaptive prior model assigned to the variance, the joint estimation problem is then simplified into a single estimation problem. It transforms the joint MAP estimation problem into a minimization problem with a nonquadratic cost function. To solve it, the use of a monotone conjugate gradient algorithm with suboptimal descent steps is proposed.Results: The performance of the proposed approach is analyzed with both simulated and experimental data. The results show that the proposed Bayesian approach is robust to noise and materials. It is also necessary to have the accurate spectrum information about the source-detector system. When dealing with experimental data, the spectrum can be predicted by a Monte Carlo simulator. For the materials between water and bone, less than 5% separation errors are observed on the estimated decomposition fractions.Conclusions: The proposed approach is a statistical reconstruction approach based on a nonlinear forward model counting the full beam polychromaticity and applied directly to the projections without taking negative-log. Compared to the approaches based on linear forward models and the BHA correction approaches, it has advantages in noise robustness and reconstruction accuracy.},
doi = {10.1118/1.4820478},
url = {https://www.osti.gov/biblio/22220269}, journal = {Medical Physics},
issn = {0094-2405},
number = 11,
volume = 40,
place = {United States},
year = {Fri Nov 15 00:00:00 EST 2013},
month = {Fri Nov 15 00:00:00 EST 2013}
}