No data available.
Please log in to see this content.
You have no subscription access to this content.
No metrics data to plot.
The attempt to load metrics for this article has failed.
The attempt to plot a graph for these metrics has failed.
The full text of this article is not currently available.
Joint estimation of tissue types and linear attenuation coefficients for
photon counting CT
1.K. Taguchi and J. S. Iwanczyk, “Vision 20/20: Single photon counting x-ray detectors in medical imaging,” Med. Phys. 40, 100901 (19pp.) (2013).
4.X. Wang, D. Meier, K. Taguchi, D. J. Wagenaar, B. E. Patt, and E. C. Frey, “Material separation in x-ray CT with energy resolved photon-counting detectors,” Med. Phys. 38, 1534–1546 (2011).
5.J. A. Fessler, I. A. Elbakri, P. Sukovic, and N. H. Clinthorne, “Maximum-likelihood dual-energy tomographic image reconstruction,” Proc. SPIE 4684, 38–49 (2002).
6.P. Sukovic and N. H. Clinthorne, “Penalized weighted least-squares image reconstruction for dual energy x-ray transmission tomography,” IEEE Trans. Med. Imaging 19, 1075–1081 (2000).
7.I. A. Elbakri and J. A. Fessler, “Statistical image reconstruction for polyenergetic x-ray computed tomography,” IEEE Trans. Med. Imaging 21, 89–99 (2002).
10.Y. Zhang, M. Brady, and S. Smith, “Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm,” IEEE Trans. Med. Imaging 20, 45–57 (2001).
11.S. Z. Li, in Markov Random Field Modeling in Image Analysis (Springer, London, 2009), Vol. 26.
12.C. M. Bishop et al., in Pattern Recognition and Machine Learning (Springer, New York, 2006), Vol. 4.
13.R. Hasegawa, M. Okada, and S. Miyoshi, “Image Segmentation Using Region-Based Latent Variables and Belief Propagation,” J. Phys. Soc. Jpn. 80, 093802 (4pp.) (2011).
14.K. Taguchi, M. Zhang, E. C. Frey, X. Wang, J. S. Iwanczyk, E. Nygard, N. E. Hartsough, B. M. Tsui, and W. C. Barber, “Modeling the performance of a photon counting x-ray detector for CT: Energy response and pulse pileup effects,” Med. Phys. 38, 1089–1102 (2011).
15.J. Cammin, J. Xu, W. C. Barber, J. S. Iwanczyk, N. E. Hartsough, and K. Taguchi, “A cascaded model of spectral distortions due to spectral response effects and pulse pileup effects in a photon-counting x-ray detector for CT,” Med. Phys. 41, 041905 (15pp.) (2014).
16.G. R. D. R. White and I. J. Wilson, “Photon, electron, proton and neutron interaction data for body tissues,” ICRU Report 46 (11pp.) (1992).
17.J. Besag, “On the statistical analysis of dirty pictures,” J. R. Stat. Soc. Ser. B (Methodol.) 48(3), 259–302 (1986).
18.J. A. Fessler, “Penalized weighted least-squares image reconstruction for positron emission tomography,” IEEE Trans. Med. Imaging 13, 290–300 (1994).
19.C. Schirra, S. Xu, T. Koehler, B. Brendel, A. Thran, D. Pan, M. Anastasio, and R. Proksa, “Statistical reconstruction of material decomposed data in spectral CT,” IEEE Trans. Med. Imaging 32, 1249–1257 (2013).
21.W. Segars, G. Sturgeon, S. Mendonca, J. Grimes, and B. Tsui, “4D XCAT phantom for multimodality imaging research,” Med. Phys. 37, 4902–4915 (2010).
22.T. Köhler and R. Proksa, “Noise properties of maximum likelihood reconstruction with edge-preserving regularization in transmission tomography,” Proc. Fully 3D, 263–266 (2009).
24.Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222–1239 (2001).
Article metrics loading...
Newly developed spectral computed tomography
as photon counting detector
more accurate tissue-type identification through material decomposition technique.
Many iterative reconstruction methods, including those developed for spectral
however, employ a regularization term whose penalty transition is designed using
pixel value of CT
image itself. Similarly, the tissue-type identification
methods are then applied after reconstruction; thus, it is impossible to take
into account probability
distribution obtained from projection likelihood. The purpose
of this work is to develop comprehensive image reconstruction
and tissue-type identification algorithm which improves quality of both
image and tissue-type map.
The authors propose a new framework to jointly perform image reconstruction,
material decomposition, and tissue-type identification for photon counting
applying maximum a posteriori estimation with voxel-based latent
variables for the tissue types. The latent variables are treated using a
voxel-based coupled Markov random field to describe the continuity and
discontinuity of human organs and a set of Gaussian distributions to incorporate
the statistical relation between the tissue types and their attenuation
characteristics. The performance of the proposed method is
quantitatively compared to that of filtered backprojection and a quadratic
penalized likelihood method by 100 noise realization.
Results showed a superior trade-off between image
resolution to current reconstruction methods. The standard deviation (SD) and bias
image were improved from quadratic penalized likelihood method:
bias, −0.9 vs −0.1 Hounsfield unit (HU); SD, 46.8 vs 27.4 HU. The accuracy of
tissue-type identification was also improved from quadratic penalized likelihood
method: 80.1% vs 86.9%.
The proposed method makes it possible not only to identify tissue types more
accurately but also to reconstruct
images with decreased noise and enhanced
sharpness owing to the information about the tissue types.
Full text loading...
Most read this month