10,333 research outputs found
A Spectral CT Method to Directly Estimate Basis Material Maps From Experimental Photon-Counting Data
The proposed spectral CT method solves the constrained one-step spectral CT reconstruction (cOSSCIR) optimization problem to estimate basis material maps while modeling the nonlinear X-ray detection process and enforcing convex constraints on the basis map images. In order to apply the optimization-based reconstruction approach to experimental data, the presented method empirically estimates the effective energy-window spectra using a calibration procedure. The amplitudes of the estimated spectra were further optimized as part of the reconstruction process to reduce ring artifacts. A validation approach was developed to select constraint parameters. The proposed spectral CT method was evaluated through simulations and experiments with a photon-counting detector. Basis material map images were successfully reconstructed using the presented empirical spectral modeling and cOSSCIR optimization approach. In simulations, the cOSSCIR approach accurately reconstructed the basis map images
Tomography of atomic number and density of materials using dual-energy imaging and the Alvarez and Macovski attenuation model
Dual-energy computed tomography and the Alvarez and Macovski [Phys. Med. Biol. 21, 733 (1976)] transmitted intensity (AMTI) model were used in this study to estimate the maps of density (Ļ) and atomic number (Z) of mineralogical samples. In this method, the attenuation coefficients are represented [Alvarez and Macovski, Phys. Med. Biol. 21, 733 (1976)] in the form of the two most important interactions of X-rays with atoms that is, photoelectric absorption (PE) and Compton scattering (CS). This enables material discrimination as PE and CS are, respectively, dependent on the atomic number (Z) and density (Ļ) of materials [Alvarez and Macovski, Phys. Med. Biol. 21, 733 (1976)]. Dual-energy imaging is able to identify sample materials even if the materials have similar attenuation coefficients at single-energy spectrum. We use the full model rather than applying one of several applied simplified forms [Alvarez and Macovski, Phys. Med. Biol. 21, 733 (1976); Siddiqui et al., SPE Annual Technical Conference and Exhibition (Society of Petroleum Engineers, 2004); Derzhi, U.S. patent application 13/527,660 (2012); Heismann et al., J. Appl. Phys. 94, 2073ā2079 (2003); Park and Kim, J. Korean Phys. Soc. 59, 2709 (2011); Abudurexiti et al., Radiol. Phys. Technol. 3, 127ā135 (2010); and Kaewkhao et al., J. Quant. Spectrosc. Radiat. Transfer 109, 1260ā1265 (2008)]. This paper describes the tomographic reconstruction of Ļ and Z maps of mineralogical samples using the AMTI model. The full model requires precise knowledge of the X-ray energy spectra and calibration of PE and CS constants and exponents of atomic number and energy that were estimated based on fits to simulations and calibration measurements. The estimated Ļ and Z images of the samples used in this paper yield average relative errors of 2.62% and 1.19% and maximum relative errors of 2.64% and 7.85%, respectively. Furthermore, we demonstrate that the method accounts for the beam hardening effect in density (Ļ) and atomic number (Z) reconstructions to a significant extent.S.J.L., G.R.M., and A.M.K. acknowledge funding through the
DigiCore consortium and the support of a linkage grant
(LP150101040) from the Australian Research Council and
FEI Company
Optimizing dual energy cone beam CT protocols for preclinical imaging and radiation research
Objective: The aim of this work was to investigate whether quantitative dual-energy CT (DECT) imaging is feasible for small animal irradiators with an integrated cone-beam CT (CBCT) system.
Methods: The optimal imaging protocols were determined by analyzing different energy combinations and dose levels. The influence of beam hardening effects and the performance of a beam hardening correction (BHC) were investigated. In addition, two systems from different manufacturers were compared in terms of errors in the extracted effective atomic numbers (Z(eff)) and relative electron densities (rho(e)) for phantom inserts with known elemental compositions and relative electron densities.
Results: The optimal energy combination was determined to be 50 and 90kVp. For this combination, Z(eff) and r rho(e) can be extracted with a mean error of 0.11 and 0.010, respectively, at a dose level of 60cGy.
Conclusion: Quantitative DECT imaging is feasible for small animal irradiators with an integrated CBCT system. To obtain the best results, optimizing the imaging protocols is required. Well-separated X-ray spectra and a sufficient dose level should be used to minimize the error and noise for Z(eff) and rho(e). When no BHC is applied in the image reconstruction, the size of the calibration phantom should match the size of the imaged object to limit the influence of beam hardening effects. No significant differences in Z(eff) and rho(e) errors are observed between the two systems from different manufacturers.
Advances in knowledge: This is the first study that investigates quantitative DECT imaging for small animal irradiators with an integrated CBCT system
Statistical Sinogram Restoration in Dual-Energy CT for PET Attenuation Correction
Dual-energy (DE) X-ray computed tomography (CT)
has been found useful in various applications. In medical imaging,
one promising application is using low-dose DECT for attenuation
correction in positron emission tomography (PET). Existing approaches
to sinogram material decomposition ignore noise characteristics
and are based on logarithmic transforms, producing noisy
component sinogram estimates for low-dose DECT. In this paper,
we propose two novel sinogram restoration methods based on statistical
models: penalized weighted least square (PWLS) and penalized
likelihood (PL), yielding less noisy component sinogram estimates
for low-dose DECT than classical methods. The proposed
methods consequently provide more precise attenuation correction
of the PET emission images than do previous methods for sinogram
material decomposition with DECT. We report simulations
that compare the proposed techniques and existing approaches.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85900/1/Fessler11.pd
Maximum-Likelihood Dual-Energy TomographicImage Reconstruction
Dual-energy (DE) X-ray computed tomography (CT) has shown promise for material characterization and for providing
quantitatively accurate CT values in a variety of applications. However, DE-CT has not been used routinely in medicine to
date, primarily due to dose considerations. Most methods for DE-CT have used the filtered backprojection method for image
reconstruction, leading to suboptimal noise/dose properties. This paper describes a statistical (maximum-likelihood)
method for dual-energy X-ray CT that accommodates a wide variety of potential system configurations and measurement
noise models. Regularized methods (such as penalized-likelihood or Bayesian estimation) are straightforward extensions.
One version of the algorithm monotonically decreases the negative log-likelihood cost function each iteration. An
ordered-subsets variation of the algorithm provides a fast and practical version.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85934/1/Fessler172.pd
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