Dynamic tomography with a priori information |
Applied Optics, Vol. 50, Issue 20, pp. 3685-3690 (2011)
http://dx.doi.org/10.1364/AO.50.003685
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Abstract
We present “dynamic tomography” algorithms that allow for the high-resolution, time-resolved imaging of dynamic (i.e., continuously time evolving) complex systems at existing x-ray micro-CT facilities. The behavior of complex systems is constrained by the underlying physics. By exploiting a priori knowledge of the geometry of the physical process being studied to allow the use of sophisticated iterative reconstruction techniques that incorporate constraints, we improve on current frame rates by at least an order of magnitude. This allows time-resolved imaging of previously intractable processes, such as two-phase fluid flow. We present reconstructions from experimental data collected at the Australian National University x-ray micro-CT facility.
© 2011 Optical Society of America
OCIS Codes
(100.6950) Image processing : Tomographic image processing
(340.7440) X-ray optics : X-ray imaging
(110.6955) Imaging systems : Tomographic imaging
ToC Category:
Image Processing
History
Original Manuscript: January 18, 2011
Revised Manuscript: April 19, 2011
Manuscript Accepted: May 9, 2011
Published: July 8, 2011
Virtual Issues
Vol. 6, Iss. 8 Virtual Journal for Biomedical Optics
Citation
Glenn R. Myers, Andrew M. Kingston, Trond K. Varslot, Michael L. Turner, and Adrian P. Sheppard, "Dynamic tomography with a priori information," Appl. Opt. 50, 3685-3690 (2011)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=ao-50-20-3685
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References
- F. Natterer, The Mathematics of Computerized Tomography (Society for Industrial and Applied Mathematics, 2001). [CrossRef]
- G. Chen, J. Tang, and S. Leng, “Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets,” Med. Phys. Lett. 35, 660–663 (2008). [CrossRef]
- S. Bonnet, A. Koenig, S. Roux, P. Hugonnard, R. Guillemard, and P. Grangeat, “Dynamic x-ray computed tomography,” Proc. IEEE 91, 1574–1587 (2003). [CrossRef]
- C. Caubit, G. Hamon, A. P. Sheppard, and P. E. Øren, “Evaluation of the reliability of prediction of petrophysical data through imagery and pore network modelling,” presented at the 22nd International Symposium of the Society of Core Analysts, Abu Dhabi, United Arab Emirates, 29 October–2 November 2008, paper SCA2008-33.
- A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging (SIAM, 2001). [CrossRef]
- G.T.Herman and A.Kuba, eds., Discrete Tomography: Foundations, Algorithms and Applications (Birkhäuser, 1999).
- K. J. Batenburg, “A network flow algorithm for binary image reconstruction from few projections,” Lect. Notes Comput. Sci. 4245, 86–97 (2006). [CrossRef]
- F. Natterer and F. Wübbeling, Mathematical Methods in Image Reconstruction (Society for Industrial and Applied Mathematics, 2001). [CrossRef]
- P. Gritzmann, D. Prangenberg, S. de Vries, and M. Wiegelmann, “Success and failure of certain reconstruction and uniqueness algorithms in discrete tomography,” Int. J. Imag. Syst. Technol. 9, 101–109 (1998). [CrossRef]
- S. Weber, T. Schüle, J. Hornegger, and C. Schnörr, “Binary tomography by iterating linear programs from noisy projections,” Lect. Notes Comput. Sci. 3322, 38–51 (2005). [CrossRef]
- L. Hajdu and R. Tijdeman, “An algorithm for discrete tomography,” Linear Algebra Appl. 339, 147–169 (2001). [CrossRef]
- A. Alpers, H. F. Poulsen, E. Knudsen, and G. T. Herman, “A discrete tomography algorithm for improving the quality of three-dimensional x-ray diffraction grain maps,” J. Appl. Cryst. 39, 582–588 (2006). [CrossRef]
- G. R. Myers, D. M. Paganin, T. E. Gureyev, and S. C. Mayo, “Phase-contrast tomography of single-material objects from few projections,” Opt. Express 16, 908–919 (2008). [CrossRef] [PubMed]
- G. R. Myers, C. D. L. Thomas, D. M. Paganin, T. E. Gureyev, and J. G. Clement, “A general few-projection method for tomographic reconstruction of samples consisting of several distinct materials,” Appl. Phys. Lett. 96, 021105 (2010). [CrossRef]
- I. Daubechies, M. Defrise, and C. De Mol, “An iterative thresholding algorithm for linear inverse problems with a sparsity constraint,” Commun. Pure Appl. Math. 57, 1413–1457(2004). [CrossRef]
- E. Y. Sidky and X. Pan, “Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,” Phys. Med. Biol. 53, 4777–4807 (2008). [CrossRef] [PubMed]
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