1887
banner image
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.
oa
A constrained, total-variation minimization algorithm for low-intensity x-ray CT
Rent:
Rent this article for
Access full text Article
/content/aapm/journal/medphys/38/S1/10.1118/1.3560887
1.
1. C. H. McCollough, A. N. Primak, N. Braun, J. Kofler, L. Yu, and J. Christner, “Strategies for reducing radiation dose in CT,” Radiol. Clin. North Am. 47, 2740 (2009).
http://dx.doi.org/10.1016/j.rcl.2008.10.006
2.
2. H. Erdogan and J. A. Fessler, “Ordered subsets algorithms for transmission tomography,” Phys. Med. Biol. 44, 28352852 (1999).
http://dx.doi.org/10.1088/0031-9155/44/11/311
3.
3. J. Qi and R. M. Leahy, “Iterative reconstruction techniques in emission computed tomography,” Phys. Med. Biol. 51, R541R578 (2006).
http://dx.doi.org/10.1088/0031-9155/51/15/R01
4.
4. X. Pan, E. Y. Sidky, and M. Vannier, “Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?,” Inverse Probl. 25, 123009112300936 (2009).
http://dx.doi.org/10.1088/0266-5611/25/12/123009
5.
5. M. Li, H. Yang, and H. Kudo, “An accurate iterative reconstruction algorithm for sparse objects: Application to 3D blood vessel reconstruction from a limited number of projections,” Phys. Med. Biol. 47, 25992609 (2002).
http://dx.doi.org/10.1088/0031-9155/47/15/303
6.
6. E. Y. Sidky, C.-M. Kao, and X. Pan, “Accurate image reconstruction from few-views and limited-angle data in divergent-beam CT,” J. X-Ray Sci. Technol. 14, 119139 (2006).
7.
7. G. H. 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. 35, 660663 (2008).
http://dx.doi.org/10.1118/1.2836423
8.
8. J. Song, Q. H. Liu, G. A. Johnson, and C. T. Badea, “Sparseness prior based iterative image reconstruction for retrospectively gated cardiac micro-CT,” Med. Phys. 34, 44764483 (2007).
http://dx.doi.org/10.1118/1.2795830
9.
9. E. Y. Sidky and X. Pan, “Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,” Phys. Med. Biol. 53, 47774807 (2008).
http://dx.doi.org/10.1088/0031-9155/53/17/021
10.
10. E. Y. Sidky, X. Pan, I. S. 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, 49204932 (2009).
http://dx.doi.org/10.1118/1.3232211
11.
11. E. Y. Sidky, M. A. Anastasio, and X. Pan, “Image reconstruction exploiting object sparsity in boundary-enhanced x-ray phase-contrast tomography,” Opt. Express 18, 1040410422 (2010).
http://dx.doi.org/10.1364/OE.18.010404
12.
12. X. Jia, R. Li, W. Y. Song, S. B. Jiang, and Y. Lou, “GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation,” Med. Phys. 37, 17571760 (2010).
http://dx.doi.org/10.1118/1.3371691
13.
13. K. Choi, S. Boyd, J. Wang, L. Xing, L. Zhu, and T.-S. Suh, “Compressed sensing based cone-beam computed tomography reconstruction with a first-order method,” Med. Phys. 37, 51135125 (2010).
http://dx.doi.org/10.1118/1.3481510
14.
14. F. Bergner, R. Grimmer, L. Ritschl, M. Kachelriess, T. Berkus, M. Oelhafen, P. Kunz, and T. Pan, “An investigation of 4D cone-beam CT algorithms for slowly rotating scanners,” Med. Phys. 37, 50445054 (2010).
http://dx.doi.org/10.1118/1.3480986
15.
15. E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory 52, 489509 (2006).
http://dx.doi.org/10.1109/TIT.2005.862083
16.
16. E. J. Candès, J. K. Romberg, and T. Tao, “Stable signal recovery from incomplete and inaccurate measurements,” Commun. Pure Appl. Math. 59, 12071223 (2006).
http://dx.doi.org/10.1002/cpa.20124
17.
17. E. J. Candès and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag. 25, 2130 (2008).
http://dx.doi.org/10.1109/MSP.2007.914731
18.
18. J. Bian, J. H. Siewerdsen, X. Han, E. Y. Sidky, J. L. Prince, C. A. Pelizzari, and X. Pan, “Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT,” Phys. Med. Biol. 55, 65756599 (2010).
http://dx.doi.org/10.1088/0031-9155/55/22/001
19.
19. T. Flohr, K. Stierstorfer, R. Raupach, S. Ulzheimer, and H. Bruder, “Performance evaluation of a 64-slice CT system with z-flying focal spot,” RoFo., Fortschr. Geb. Rontgenstr. Nuklearmed. 176, 18031810, ISSN 1438-9029 (2004).
http://dx.doi.org/10.1055/s-2004-813717
20.
20. M. Kachelriess, M. Knaup, C. Penssel, and W. A. Kalender, “Flying focal spot (FFS) in cone-beam CT,” IEEE Trans. Nucl. Sci. 53, 12381247 (2006).
http://dx.doi.org/10.1109/TNS.2006.874076
21.
21. W. Zbijewski and F. J. Beekman, “Characterization and suppression of edge and aliasing artefacts in iterative X-ray CT reconstruction,” Phys. Med. Biol. 49, 145157 (2004).
http://dx.doi.org/10.1088/0031-9155/49/1/010
22.
22. C. R. Vogel, Computational Methods for Inverse Problems (Society for Industrial and Applied Mathematics, Philadelphia, PA, 2002).
http://dx.doi.org/10.1137/1.9780898717570
23.
23. P. L. Combettes and J.-C. Pesquet, “Image restoration subject to a total variation constraint,” IEEE Trans. Image Process. 13, 12131222 (2004).
http://dx.doi.org/10.1109/TIP.2004.832922
24.
24. C. L. Byrne, Applied Iterative Methods (AK Peters, Wellesley, MA, 2008).
25.
25. T. Wu, R. H. Moore, E. A. Rafferty, and D. B. Kopans, “A comparison of reconstruction algorithms for breast tomosynthesis,” Med. Phys. 31, 26362647 (2004).
http://dx.doi.org/10.1118/1.1786692
26.
26. Y. Zhang, H.-P. Chan, B. Sahiner, J. Wei, M. M. Goodsitt, L. M. Hadjiiski, J. Ge, and C. Zhou, “A comparative study of limited-angle cone-bean reconstruction methods for breast tomosynthesis,” Med. Phys. 33, 37813795 (2006).
http://dx.doi.org/10.1118/1.2237543
27.
27. M. A. T. Figueiredo, R. D. Nowak, and S. J. Wright, “Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems,” IEEE J. Sel. Top. Signal Process. 1, 586597 (2007).
http://dx.doi.org/10.1109/JSTSP.2007.910281
28.
28. F. Xu and K. Mueller, “Accelerating popular tomographic reconstruction algorithms on commodity PC graphics hardware,” IEEE Trans. Nucl. Sci. 53, 12381247 (2006).
http://dx.doi.org/10.1109/TNS.2006.880974
29.
29. Y. Zou, E. Y. Sidky, and X. Pan, “Partial volume and aliasing artefacts in helical cone-beam CT,” Phys. Med. Biol. 49, 23652375 (2004).
http://dx.doi.org/10.1088/0031-9155/49/11/017
30.
30. T. F. Chan, S. Osher, and J. Shen, “The digital TV filter and nonlinear denoising,” IEEE Trans. Image Process. 10, 231241 (2001).
http://dx.doi.org/10.1109/83.902288
31.
31. D. Xia, J. Bian, X. Han, E. Y. Sidky, and X. Pan, “An investigation of compressive-sensing image reconstruction from flying-focal-spot CT data,” IEEE Medical Imaging Conference Record, Orlando, FL, pp. 34583462, 2009.
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/38/S1/10.1118/1.3560887
Loading
/content/aapm/journal/medphys/38/S1/10.1118/1.3560887
Loading

Data & Media loading...

Loading

Article metrics loading...

/content/aapm/journal/medphys/38/S1/10.1118/1.3560887
2011-07-25
2014-08-27

Abstract

Purpose:

The authors developed an iterative image-reconstruction algorithm for application to low-intensity computed tomography projection data, which is based on constrained, total-variation (TV) minimization. The algorithm design focuses on recovering structure on length scales comparable to a detector bin width.

Methods:

Recovering the resolution on the scale of a detector bin requires that pixel size be much smaller than the bin width. The resulting image array contains many more pixels than data, and this undersampling is overcome with a combination of Fourier upsampling of each projection and the use of constrained, TV minimization, as suggested by compressive sensing. The presented pseudocode for solving constrained, TV minimization is designed to yield an accurate solution to this optimization problem within 100 iterations.

Results:

The proposed image-reconstruction algorithm is applied to a low-intensity scan of a rabbit with a thin wire to test the resolution. The proposed algorithm is compared to filtered backprojection (FBP).

Conclusions:

The algorithm may have some advantage over FBP in that the resulting noise level is lowered at equivalent contrast levels of the wire.

Loading

Full text loading...

/deliver/fulltext/aapm/journal/medphys/38/S1/1.3560887.html;jsessionid=34334au40tqis.x-aip-live-03?itemId=/content/aapm/journal/medphys/38/S1/10.1118/1.3560887&mimeType=html&fmt=ahah&containerItemId=content/aapm/journal/medphys
true
true
This is a required field
Please enter a valid email address
This feature is disabled while Scitation upgrades its access control system.
This feature is disabled while Scitation upgrades its access control system.
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: A constrained, total-variation minimization algorithm for low-intensity x-ray CT
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/38/S1/10.1118/1.3560887
10.1118/1.3560887
SEARCH_EXPAND_ITEM