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Noise properties of CT images reconstructed by use of constrained total-variation, data-discrepancy minimization
1.R. Gordon, R. Bender, and G. T. Herman, “Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and x-ray photography,” J. Theor. Biol. 29, 471–481 (1970).
4.E. Y. Sidky, X. Pan, I. Reiser, R. M. Nishikawa, R. H. Moore, and D. B. Kopans, “Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image-reconstruction algorithms,” Med. Phys. 36, 4920–4932 (2009).
5.E. Y. Sidky, Y. Duchin, X. Pan, and C. Ullberg, “A constrained, total-variation minimization algorithm for low-intensity X-ray CT,” Med. Phys. 38, S117–S125 (2011).
8.S. Ahn, J. A. Fessler, D. Blatt, and A. O. Hero, “Convergent incremental optimization transfer algorithms: Application to tomography,” IEEE Trans. Med. Imaging 25, 283–295 (2006).
10.E. Y. Sidky, J. H. Jørgensen, and X. Pan, “Convex optimization problem prototyping for image reconstruction in computed tomography with the Chambolle-Pock algorithm,” Phys. Med. Biol. 57, 3065–3091 (2012).
and R. Proksa
, “Noise properties of maximum likelihood reconstruction with edge-preserving regularization in transmission tomography
,” in Proceedings of the 9th International Meeting on Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
), pp. 263–266, available at http://www.fully3d2009.org
12.D. P. Bertsekas, A. Nedic, and A. E. Ozdaglar, Convex Analysis and Optimization (Athena Scientific, Nashua, NH, 2003).
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The authors develop and investigate iterative image reconstruction algorithms based on data-discrepancy minimization with a total-variation (TV) constraint. The various algorithms are derived with different data-discrepancy measures reflecting the maximum likelihood (ML) principle. Simulations demonstrate the iterative algorithms and the resulting image
statistical properties for low-dose CT data acquired with sparse projection view angle sampling. Of particular interest is to quantify improvement of image
statistical properties by use of the ML data fidelity term.
An incremental algorithm framework is developed for this purpose. The instances of the incremental algorithms are derived for solving optimization problems including a data fidelity objective function combined with a constraint on the image TV. For the data fidelity term the authors, compare application of the maximum likelihood principle, in the form of weighted least-squares (WLSQ) and Poisson-likelihood (PL), with the use of unweighted least-squares (LSQ).
The incremental algorithms are applied to projection data generated by a simulation modeling the breast computed tomography (bCT) imaging application. The only source of data inconsistency in the bCT projections is due to noise, and a Poisson distribution is assumed for the transmitted x-ray
photon intensity. In the simulations involving the incremental algorithms an ensemble of images,
reconstructed from 1000 noise realizations of the x-ray transmission data, is used to estimate the image
statistical properties. The WLSQ and PL incremental algorithms are seen to reduce image variance as compared to that of LSQ without sacrificing image bias. The difference is also seen at few iterations—short of numerical convergence of the corresponding optimization problems.
The proposed incremental algorithms prove effective and efficient for iterative image reconstruction in low-dose CT applications particularly with sparse-view projection data.
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