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.
The full text of this article is not currently available.
oa
Iterative image-domain decomposition for dual-energy CT
Rent:
Rent this article for
Access full text Article
/content/aapm/journal/medphys/41/4/10.1118/1.4866386
1.
1. B. Ruzsics, H. Lee, P. L. Zwerner, M. Gebregziabher, P. Costello, and U. J. Schoepf, “Dual-energy CT of the heart for diagnosing coronary artery stenosis and myocardial ischemia-initial experience,” Eur. Radiol. 18(11), 24142424 (2008).
http://dx.doi.org/10.1007/s00330-008-1022-x
2.
2. L. J. Zhang, S. Y. Wu, J. B. Niu, Z. L. Zhang, H. Z. Wang, Y. E. Zhao, X. Chai, C. S. Zhou, and G. M. Lu, “Dual-energy CT angiography in the evaluation of intracranial aneurysms: Image quality, radiation dose, and comparison with 3D rotational digital subtraction angiography,” Am. J. Roentgenol. 194(1), 2330 (2010).
http://dx.doi.org/10.2214/AJR.08.2290
3.
3. K. Deng, C. Liu, R. Ma, C. Sun, X. M. Wang, Z. T. Ma, and X. L. Sun, “Clinical evaluation of dual-energy bone removal in CT angiography of the head and neck: Comparison with conventional bone-subtraction CT angiography,” Clin. Radiol. 64(5), 534541 (2009).
http://dx.doi.org/10.1016/j.crad.2009.01.007
4.
4. Y. Li, G. Shi, S. Wang, S. Wang, and R. Wu, “Iodine quantification with dual-energy CT: Phantom study and preliminary experience with VX2 residual tumour in rabbits after radiofrequency ablation,” Br. J. Radiol. 86(1029), 143151 (2013).
http://dx.doi.org/10.1259/bjr.20130143
5.
5. H. Chandarana, A. J. Megibow, B. A. Cohen, R. Srinivasan, D. Kim, C. Leidecker, and M. Macari, “Iodine quantification with dual-energy CT: Phantom study and preliminary experience with renal masses,” Am. J. Roentgenol. 196(6), W693W700 (2011).
http://dx.doi.org/10.2214/AJR.10.5541
6.
6. M. Eiber, K. Holzapfel, M. Frimberger, M. Straub, H. Schneider, E. J. Rummeny, M. Dobritz, and A. Huber, “Targeted dual-energy single-source CT for characterisation of urinary calculi: Experimental and clinical experience,” Eur. Radiol. 22(1), 251258 (2012).
http://dx.doi.org/10.1007/s00330-011-2231-2
7.
7. A. N. Primak, J. G. Fletcher, T. J. Vrtiska, O. P. Dzyubak, J. C. Lieske, M. E. Jackson, J. C. Williams Jr., and C. H. McCollough, “Noninvasive differentiation of uric acid versus non-uric acid kidney stones using dual-energy CT,” Acad. Radiol. 14(12), 14411447 (2007).
http://dx.doi.org/10.1016/j.acra.2007.09.016
8.
8. R. E. Alvarez and A. Macovski, “Energy-selective reconstructions in x-ray computerized tomography,” Phys. Med. Biol. 21(5), 733744 (1976).
http://dx.doi.org/10.1088/0031-9155/21/5/002
9.
9. R. Yuan, W. P. Shuman, J. P. Earls, C. J. Hague, H. A. Mumtaz, A. Scott-Moncrieff, J. D. Elli, J. R. Mayo, and J. A. Leipsic, “Reduced iodine load at CT pulmonary angiography with dual-energy monochromatic imaging: Comparison with standard CT pulmonary angiography—A prospective randomized trial,” Radiology 262(1), 290297 (2012).
http://dx.doi.org/10.1148/radiol.11110648
10.
10. K. Matsumoto, M. Jinzaki, Y. Tanami, A. Ueno, M. Yamada, and S. Kuribayashi, “Virtual monochromatic spectral imaging with fast kilovoltage switching: Improved image quality as compared with that obtained with conventional 120-kVp CT,” Radiology 259(1), 257262 (2011).
http://dx.doi.org/10.1148/radiol.11100978
11.
11. L. Yu, S. Leng, and C. H. McCollough, “Dual-energy CT-based monochromatic imaging,” Am. J. Roentgenol. 199(5 Suppl.), S9S15 (2012).
http://dx.doi.org/10.2214/AJR.12.9121
12.
12. L. J. Zhang, J. Peng, S. Y. Wu, Z. J. Wang, X. S. Wu, C. S. Zhou, X. M. Ji, and G. M. Lu, “Liver virtual non-enhanced CT with dual-source, dual-energy CT: A preliminary study,” Eur. Radiol. 20(9), 22572264 (2010).
http://dx.doi.org/10.1007/s00330-010-1778-7
13.
13. C. M. Sommer, C. B. Schwarzwaelder, W. Stiller, S. T. Schindera, U. Stampfl, N. Bellemann, M. Holzschuh, J. Schmidt, J. Weitz, L. Grenacher, H. U. Kauczor, and B. A. Radeleff, “Iodine removal in intravenous dual-energy CT-cholangiography: Is virtual non-enhanced imaging effective to replace true non-enhanced imaging?,” Eur. J. Radiol. 81(4), 692699 (2012).
http://dx.doi.org/10.1016/j.ejrad.2011.01.087
14.
14. B. Schmidt and C. McCollough, “Dual-energy computed tomography,” in Computed Tomography of the Cardiovascular System, editors by T. C. Gerber, Birgit Kantor, and E. E. Williamson (Informa Healthcare, London, 2007), pp. 451462.
15.
15. X. Liu, L. Yu, A. N. Primak, and C. H. McCollough, “Quantitative imaging of element composition and mass fraction using dual-energy CT: Three-material decomposition,” Med. Phys. 36(5), 16021609 (2009).
http://dx.doi.org/10.1118/1.3097632
16.
16. M. R. Millner, W. D. McDavid, R. G. Waggener, M. J. Dennis, W. H. Payne, and V. J. Sank, “Extraction of information from CT scans at different energies,” Med. Phys. 6, 7071 (1979).
http://dx.doi.org/10.1118/1.594555
17.
17. D. E. Avrin, A. Macovski, and L. M. Zatz, “Clinical application of Compton and photo-electric reconstruction in computed tomography: Preliminary results,” Invest. Radiol. 13(3), 217222 (1978).
http://dx.doi.org/10.1097/00004424-197805000-00007
18.
18. G. Dichiro, R. A. Brooks, R. M. Kessler, G. S. Johnston, A. E. Jones, J. R. Herdt, and W. T. Sheridan, “Tissue signatures with dual-energy computed-tomography,” Radiology 131(2), 521523 (1979).
19.
19. F. Kelcz, P. M. Joseph, and S. K. Hilal, “Noise considerations in dual energy CT scanning,” Med. Phys. 6, 418425 (1979).
http://dx.doi.org/10.1118/1.594520
20.
20. E. Y. Sidky, Y. Zou, and X. C. Pan, “Impact of polychromatic x-ray sources on helical, cone-beam computed tomography and dual-energy methods,” Phys. Med. Biol. 49(11), 22932303 (2004).
http://dx.doi.org/10.1088/0031-9155/49/11/012
21.
21. P. Sukovic and N. H. Clinthorne, “Penalized weighted least-squares image reconstruction for dual energy x-ray transmission tomography,” IEEE Trans. Med. Imaging 19(11), 10751081 (2000).
http://dx.doi.org/10.1109/42.896783
22.
22. C. Maass, M. Baer, and M. Kachelriess, “Image-based dual energy CT using optimized precorrection functions: A practical new approach of material decomposition in image domain,” Med. Phys. 36(8), 38183829 (2009).
http://dx.doi.org/10.1118/1.3157235
23.
23. X. Dong, T. Niu, and L. Zhu, “Single-scan energy-selective imaging on cone-beam CT: A preliminary study,” Proc. SPIE 8668, 86682Z186662Z9 (2013).
http://dx.doi.org/10.1117/12.2008027
24.
24. T. P. Szczykutowicz and G. H. Chen, “Dual energy CT using slow kVp switching acquisition and prior image constrained compressed sensing,” Phys. Med. Biol. 55(21), 64116429 (2010).
http://dx.doi.org/10.1088/0031-9155/55/21/005
25.
25. R. E. Alvarez, “Dimensionality and noise in energy selective x-ray imaging,” Med. Phys. 40(11), 111909 (13pp.) (2013).
http://dx.doi.org/10.1118/1.4824057
26.
26. E. Roessl, A. Ziegler, and R. Proksa, “On the influence of noise correlations in measurement data on basis image noise in dual-energy like x-ray imaging,” Med. Phys. 34(3), 959966 (2007).
http://dx.doi.org/10.1118/1.2514058
27.
27. A. Graser, T. R. Johnson, H. Chandarana, and M. Macari, “Dual energy CT: preliminary observations and potential clinical applications in the abdomen,” Eur Radiol. 19(1), 1323 (2009).
http://dx.doi.org/10.1007/s00330-008-1122-7
28.
28. W. A. Kalender, E. Klotz, and L. Kostaridou, “An Algorithm for noise suppression in dual energy ct material density images,” IEEE Trans. Med Imaging 7(3), 218224 (1988).
http://dx.doi.org/10.1109/42.7785
29.
29. L. S. Guimarães, J. G. Fletcher, W. S. Harmsen, L. F. Yu, H. Siddiki, Z. Melton, J. E. Huprich, D. Hough, R. Hartman, and C. H. McCollough, “Appropriate patient selection at abdominal dual-energy CT using 80 kV: Relationship between patient size, image noise, and image quality,” Radiology 257(3), 732742 (2010).
http://dx.doi.org/10.1148/radiol.10092016
30.
30. R. A. Rutherford, B. R. Pullan, and I. Isherwood, “Measurement of effective atomic number and electron-density using an EMI scanner,” Neuroradiology 11(1), 1521 (1976).
http://dx.doi.org/10.1007/BF00327253
31.
31. R. J. Warp and J. T. Dobbins, “Quantitative evaluation of noise reduction strategies in dual-energy imaging,” Med. Phys. 30(2), 190198 (2003).
http://dx.doi.org/10.1118/1.1538232
32.
32. P. C. Johns and M. J. Yaffe, “Theoretical optimization of dual-energy x-ray-imaging with application to mammography,” Med. Phys. 12(3), 289296 (1985).
http://dx.doi.org/10.1118/1.595766
33.
33. S. Kido, K. Kuriyama, C. Kuroda, and T. Horai, “Single-exposure dual-energy subtraction computed radiography of the chest for detection of low-contrast simulated nodules: Comparison of hard- and soft-copy images,” Radiology 209P, 320320 (1998).
34.
34. Q. Z. Cao, T. Brosnan, A. Macovski, and D. Nishimura, “Least-squares approach in measurement-dependent filtering for selective medical images,” IEEE Trans. Med Imaging 7(2), 154160 (1988).
http://dx.doi.org/10.1109/42.3942
35.
35. R. A. Rutherford, B. R. Pullan, and I. Isherwood, “X-ray energies for effective atomic number determination,” Neuroradiology 11(1), 2328 (1976).
http://dx.doi.org/10.1007/BF00327254
36.
36. D. A. Hinshaw and J. T. Dobbins, “Recent progress in noise reduction and scatter correction in dual-energy imaging,” Proc. SPIE 2432, 134142 (1995).
http://dx.doi.org/10.1117/12.208330
37.
37. D. P. Clark, K. Ghaghada, E. J. Moding, D. G. Kirsch, and C. T. Badea, “In vivo characterization of tumor vasculature using iodine and gold nanoparticles and dual energy micro-CT,” Phys. Med. Biol. 58(6), 16831704 (2013).
http://dx.doi.org/10.1088/0031-9155/58/6/1683
38.
38. G. Wang and M. W. Vannier, “Helical CT image noise analytical results,” Med. Phys. 20(6), 16351640 (1993).
http://dx.doi.org/10.1118/1.596950
39.
39. P. V. Granton, S. I. Pollmann, N. L. Ford, M. Drangova, and D. W. Holdsworth, “Implementation of dual- and triple-energy cone-beam micro-CT for postreconstruction material decomposition,” Med. Phys. 35(11), 50305042 (2008).
http://dx.doi.org/10.1118/1.2987668
40.
40. L. Zhu, J. Wang, and L. Xing, “Noise suppression in scatter correction for cone-beam CT,” Med. Phys. 36(3), 741752 (2009).
http://dx.doi.org/10.1118/1.3063001
41.
41. T. Strutz, Data Fitting and Uncertainty: A Practical Introduction to Weighted Least Squares and Beyond (Vieweg and Teubner, Vieweg+Teubner Verlag, 2010).
42.
42. J. Wang, T. Li, H. B. Lu, and Z. R. Liang, “Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray computed tomography,” IEEE Trans. Med Imaging 25(10), 12721283 (2006).
http://dx.doi.org/10.1109/TMI.2006.882141
43.
43. J. Canny, “A Computational Approach to Edge-Detection,” IEEE Trans. Pattern Anal. PAMI-8(6), 679698 (1986).
http://dx.doi.org/10.1109/TPAMI.1986.4767851
44.
44. P. Perona and J. Malik, “Scale-space and edge-detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. 12(7), 629639 (1990).
http://dx.doi.org/10.1109/34.56205
45.
45. J. M. Prewitt, Object Enhancement and Extraction (Academic Press, New York, 1970), Vol. 75.
46.
46. J. B. Thibault, K. D. Sauer, C. A. Bouman, and J. Hsieh, “A three-dimensional statistical approach to improved image quality for multislice helical CT,” Med. Phys. 34(11), 45264544 (2007).
http://dx.doi.org/10.1118/1.2789499
47.
47. T. Y. Niu and L. Zhu, “Scatter correction for full-fan volumetric CT using a stationary beam blocker in a single full scan,” Med. Phys. 38(11), 60276038 (2011).
http://dx.doi.org/10.1118/1.3651619
48.
48. T. Y. Niu, M. S. Sun, J. Star-Lack, H. W. Gao, Q. Y. Fan, and L. Zhu, “Shading correction for on-board cone-beam CT in radiation therapy using planning MDCT images,” Med. Phys. 37(10), 53955406 (2010).
http://dx.doi.org/10.1118/1.3483260
49.
49. J. Hatton, B. McCurdy, and P. B. Greer, “Cone beam computerized tomography: The effect of calibration of the Hounsfield unit number to electron density on dose calculation accuracy for adaptive radiation therapy,” Phys. Med. Biol. 54(15), N329N346 (2009).
http://dx.doi.org/10.1088/0031-9155/54/15/N01
50.
50. T. R. C. Johnson, B. Krauss, M. Sedlmair, M. Grasruck, H. Bruder, D. Morhard, C. Fink, S. Weckbach, M. Lenhard, B. Schmidt, T. Flohr, M. F. Reiser, and C. R. Becker, “Material differentiation by dual energy CT: Initial experience,” Eur. Radiol. 17(6), 15101517 (2007).
http://dx.doi.org/10.1007/s00330-006-0517-6
51.
51. T. G. Flohr, C. H. McCollough, H. Bruder, M. Petersilka, K. Gruber, C. Suss, M. Grasruck, K. Stierstorfer, B. Krauss, R. Raupach, A. N. Primak, A. Kuttner, S. Achenbach, C. Becker, A. Kopp, and B. M. Ohnesorge, “First performance evaluation of a dual-source CT (DSCT) system,” Eur. Radiol. 16(2), 256268 (2006).
http://dx.doi.org/10.1007/s00330-005-2919-2
52.
52. L. Zhu and J. StarLack, “A practical reconstruction algorithm for CT noise variance maps using FBP reconstruction,” Proc. SPIE 6510, U1074U1081 (2007).
http://dx.doi.org/10.1117/12.713692
53.
53. T. Y. Niu and L. Zhu, “Accelerated barrier optimization compressed sensing (ABOCS) reconstruction for cone-beam CT: Phantom studies,” Med. Phys. 39(7), 45884598 (2012).
http://dx.doi.org/10.1118/1.4729837
54.
54. E. Y. Sidky and X. C. Pan, “Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,” Phys. Med. Biol. 53(17), 47774807 (2008).
http://dx.doi.org/10.1088/0031-9155/53/17/021
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/41/4/10.1118/1.4866386
Loading
/content/aapm/journal/medphys/41/4/10.1118/1.4866386
Loading

Data & Media loading...

Loading

Article metrics loading...

/content/aapm/journal/medphys/41/4/10.1118/1.4866386
2014-03-04
2014-11-28

Abstract

Dual energy CT (DECT) imaging plays an important role in advanced imaging applications due to its capability of material decomposition. Direct decomposition via matrix inversion suffers from significant degradation of image signal-to-noise ratios, which reduces clinical values of DECT. Existing denoising algorithms achieve suboptimal performance since they suppress image noise either before or after the decomposition and do not fully explore the noise statistical properties of the decomposition process. In this work, the authors propose an iterative image-domain decomposition method for noise suppression in DECT, using the full variance-covariance matrix of the decomposed images.

The proposed algorithm is formulated in the form of least-square estimation with smoothness regularization. Based on the design principles of a best linear unbiased estimator, the authors include the inverse of the estimated variance-covariance matrix of the decomposed images as the penalty weight in the least-square term. The regularization term enforces the image smoothness by calculating the square sum of neighboring pixel value differences. To retain the boundary sharpness of the decomposed images, the authors detect the edges in the CT images before decomposition. These edge pixels have small weights in the calculation of the regularization term. Distinct from the existing denoising algorithms applied on the images before or after decomposition, the method has an iterative process for noise suppression, with decomposition performed in each iteration. The authors implement the proposed algorithm using a standard conjugate gradient algorithm. The method performance is evaluated using an evaluation phantom (Catphan©600) and an anthropomorphic head phantom. The results are compared with those generated using direct matrix inversion with no noise suppression, a denoising method applied on the decomposed images, and an existing algorithm with similar formulation as the proposed method but with an edge-preserving regularization term.

On the Catphan phantom, the method maintains the same spatial resolution on the decomposed images as that of the CT images before decomposition (8 pairs/cm) while significantly reducing their noise standard deviation. Compared to that obtained by the direct matrix inversion, the noise standard deviation in the images decomposed by the proposed algorithm is reduced by over 98%. Without considering the noise correlation properties in the formulation, the denoising scheme degrades the spatial resolution to 6 pairs/cm for the same level of noise suppression. Compared to the edge-preserving algorithm, the method achieves better low-contrast detectability. A quantitative study is performed on the contrast-rod slice of Catphan phantom. The proposed method achieves lower electron density measurement error as compared to that by the direct matrix inversion, and significantly reduces the error variation by over 97%. On the head phantom, the method reduces the noise standard deviation of decomposed images by over 97% without blurring the sinus structures.

The authors propose an iterative image-domain decomposition method for DECT. The method combines noise suppression and material decomposition into an iterative process and achieves both goals simultaneously. By exploring the full variance-covariance properties of the decomposed images and utilizing the edge predetection, the proposed algorithm shows superior performance on noise suppression with high image spatial resolution and low-contrast detectability.

Loading

Full text loading...

/deliver/fulltext/aapm/journal/medphys/41/4/1.4866386.html;jsessionid=fx3pb8qhel8j.x-aip-live-06?itemId=/content/aapm/journal/medphys/41/4/10.1118/1.4866386&mimeType=html&fmt=ahah&containerItemId=content/aapm/journal/medphys
true
true
This is a required field
Please enter a valid email address
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Iterative image-domain decomposition for dual-energy CT
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/41/4/10.1118/1.4866386
10.1118/1.4866386
SEARCH_EXPAND_ITEM