Skip to main content
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.
/content/aapm/journal/medphys/41/1/10.1118/1.4851635
1.
1. D. J. Brenner and E. J. Hall, “Computed tomography: An increasing source of radiation exposure,” New Engl. J. Med. 357, 22772284 (2007).
http://dx.doi.org/10.1056/NEJMra072149
2.
2. J. Hsieh, “Adaptive streak artifact reduction in computed tomography resulting from excessive x-ray photon noise,” Med. Phys. 25, 21392147 (1998).
http://dx.doi.org/10.1118/1.598410
3.
3. M. Kachelriess, O. Watzke, and W. A. Kalender, “Generalized multi-dimensional adaptive filtering for conventional and spiral single-slice, multi-slice, and cone-beam CT,” Med. Phys. 28, 475490 (2001).
http://dx.doi.org/10.1118/1.1358303
4.
4. T. Li, X. Li, J. Wang, J. Wen, H. Lu, J. Hsieh, and Z. Liang, “Nonlinear sinogram smoothing for low-dose X-ray CT,” IEEE Trans. Nucl. Sci. 51, 25052513 (2004).
http://dx.doi.org/10.1109/TNS.2004.834824
5.
5. J. Wang, T. Li, H. Lu, and Z. 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, 12721283 (2006).
http://dx.doi.org/10.1109/TMI.2006.882141
6.
6. P. J. La Rivière, “Penalized-likelihood sinogram smoothing for low-dose CT,” Med. Phys. 32, 16761683 (2005).
http://dx.doi.org/10.1118/1.1915015
7.
7. P. J. La Rivière, J. Bian, and P. A. Vargas, “Penalized-likelihood sinogram restoration for computed tomography,” IEEE Trans. Med. Imaging 25, 10221036 (2006).
http://dx.doi.org/10.1109/TMI.2006.875429
8.
8. A. Manduca, L. Yu, J. D. Trzasko, N. Khaylova, J. M. Kofler, C. M. McCollough, and J. G. Fletcher, “Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT,” Med. Phys. 36, 49114919 (2009).
http://dx.doi.org/10.1118/1.3232004
9.
9. C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” Int. Conf. Comput. Vis. 6, 839846 (1998).
http://dx.doi.org/10.1109/ICCV.1998.710815
10.
10. L. I. Rudin, S. Osher, and E. Fatemi, “Nonlinear total variation based noise removal algorithms,” Physica D 60, 259268 (1992).
http://dx.doi.org/10.1016/0167-2789(92)90242-F
11.
11. A. Buades, B. Coll, and J. M. Morel, “A review of image denoising algorithms, with a new one,” Multiscale Model. Simul. 4, 490530 (2005).
http://dx.doi.org/10.1137/040616024
12.
12. M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54, 43114322 (2006).
http://dx.doi.org/10.1109/TSP.2006.881199
13.
13. K. Lange and J. A. Fessler, “Globally convergent algorithms for maximum a posteriori transmission tomography,” IEEE Trans. Image Process. 4, 14301438 (1995).
http://dx.doi.org/10.1109/83.465107
14.
14. J. Nuyts, B. De Man, P. Dupont, M. Defrise, P. Suetens, and L. Mortelmans, “Iterative reconstruction for helical CT: A simulation study,” Phys. Med. Biol. 43, 729737 (1998).
http://dx.doi.org/10.1088/0031-9155/43/4/003
15.
15. 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, 45264544 (2007).
http://dx.doi.org/10.1118/1.2789499
16.
16. F. J. Beekman and C. Kamphuis, “Ordered subset reconstruction for x-ray CT,” Phys. Med. Biol. 46, 18351844 (2001).
http://dx.doi.org/10.1088/0031-9155/46/7/307
17.
17. J. A. Fessler, E. P. Ficaro, N. H. Clinthorne, and K. Lange, “Grouped-coordinate ascent algorithms for penalized-likelihood transmission image reconstruction,” IEEE Trans. Med. Imaging 16, 166175 (1997).
http://dx.doi.org/10.1109/42.563662
18.
18. M. Kachelriess, M. Knaup, and O. Bockenbach, “Hyperfast parallel-beam and cone-beam backprojection using the cell general purpose hardware,” Med. Phys. 34, 14741486 (2007).
http://dx.doi.org/10.1118/1.2710328
19.
19. J. S. Kole and F. J. Beekman, “Evaluation of accelerated iterative x-ray CT image reconstruction using floating point graphics hardware,” Phys. Med. Biol. 51, 875889 (2006).
http://dx.doi.org/10.1088/0031-9155/51/4/008
20.
20. F. Xu and K. Mueller, “Accelerating popular tomographic reconstruction algorithms on commodity PC graphics hardware,” IEEE Trans. Nucl. Sci. 52, 654663 (2005).
http://dx.doi.org/10.1109/TNS.2005.851398
21.
21. F. Xu and K. Mueller, “Real-time 3D computed tomographic reconstruction using commodity graphics hardware,” Phys. Med. Biol. 52, 34053419 (2007).
http://dx.doi.org/10.1088/0031-9155/52/12/006
22.
22. D. J. Blezek, Z. Li, B. J. Bartholmai, A. Manduca, and B. J. Erickson, “Clinically feasible CT denoising using a GPU-based non-local means algorithm,” Society for Imaging Informatics in Medicine, 2011 Annual Meeting, Washington, DC, 2011.
23.
23. J. Darbon, A. Cunha, T. F. Chan, S. Osher, and G. J. Jensen, “Fast nonlocal filtering applied to electron cryomicroscopy,” in Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Paris, France, 2008 (IEEE, New York, NY, 2008), pp. 13311334.
24.
24. Y. L. Liu, J. Wang, X. Chen, Y. W. Guo, and Q. S. Peng, “A robust and fast non-local means algorithm for image denoising,” J. Comput. Sci. Technol. 23(2), 270279 (2008).
http://dx.doi.org/10.1007/s11390-008-9129-8
25.
25. X. Pan and L. Yu, “Image reconstruction with shift-variant filtration and its implication for noise and resolution properties in fan-beam computed tomography,” Med. Phys. 30, 590600 (2003).
http://dx.doi.org/10.1118/1.1556608
26.
26. A. Wunderlich and F. Noo, “Image covariance and lesion detectability in direct fan-beam x-ray computed tomography,” Phys. Med. Biol. 53, 24712493 (2008).
http://dx.doi.org/10.1088/0031-9155/53/10/002
27.
27. K. Stierstorfer, A. Rauscher, J. Boese, H. Bruder, S. Schaller, and T. Flohr, “Weighted FBP—a simple approximate 3D FBP algorithm for multislice spiral CT with good dose usage for arbitrary pitch,” Phys. Med. Biol. 49, 22092218 (2004).
http://dx.doi.org/10.1088/0031-9155/49/11/007
28.
28. J. H. Hubbell and S. M. Seltzer, “Tables of X-ray mass attenuation coefficients and mass energy absorption coefficients from 1 keV to 20 MeV for elements Z 51 to 92 and 48 additional substances of dosimetric interest,” NISTIR Report No. 5632 (U.S. Department of Commerce, Washington, DC, 1995).
29.
29. H. H. Barrett and K. Myers, Foundations of Image Science, 1st ed. (Wiley-Interscience, Hoboken, NJ, 2003).
30.
30. H. H. Barrett and W. Swindell, Radiological Imaging: The Theory of Image Formation, Detection, and Processing (Academic, New York, 1981).
31.
31. W. A. Kalender, H. Wolf, and C. Suess, “Dose reduction in CT by anatomically adapted tube current modulation. II. Phantom measurements,” Med. Phys. 26, 22482253 (1999).
http://dx.doi.org/10.1118/1.598738
32.
32. J. Hsieh, Computed Tomography: Principles, Design, Artifacts, and Recent Advances, 2nd Revised ed. (SPIE, Bellingham, WA, 2009).
33.
33. B. R. Whiting, P. Massoumzadeh, O. A. Earl, J. A. O’Sullivan, D. L. Snyder, and J. F. Williamson, “Properties of preprocessed sinogram data in x-ray computed tomography,” Med. Phys. 33, 32903303 (2006).
http://dx.doi.org/10.1118/1.2230762
34.
34. M. Gies, W. A. Kalender, H. Wolf, and C. Suess, “Dose reduction in CT by anatomically adapted tube current modulation. I. Simulation studies,” Med. Phys. 26, 22352247 (1999).
http://dx.doi.org/10.1118/1.598779
35.
35. A. C. Kak and M. Slaney, Principles of Computerized Tomographic Imaging (Society of Industrial and Applied Mathematics, Philadelphia, PA, 2001).
36.
36. R. C. Mittelhammer, Mathematical Statistics for Economics and Business, Corrected ed. (Springer, New York, 1996).
37.
37. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes with Source Code CD-ROM 3rd Edition: The Art of Scientific Computing, 3rd ed. (Cambridge University Press, Cambridge, England, 2007).
38.
38. J. M. Boone, “Determination of the presampled MTF in computed tomography,” Med. Phys. 28, 356360 (2001).
http://dx.doi.org/10.1118/1.1350438
39.
39. J. H. Siewerdsen, I. A. Cunningham, and D. A. Jaffray, “A framework for noise-power spectrum analysis of multidimensional images,” Med. Phys. 29, 26552671 (2002).
http://dx.doi.org/10.1118/1.1513158
40.
40. L. Yu, S. Leng, L. Chen, J. M. Kofler, R. E. Carter, and C. H. McCollough, “Prediction of human observer performance in a 2-alternative forced choice low-contrast detection task using channelized Hotelling observer: Impact of radiation dose and reconstruction algorithms,” Med. Phys. 40, 041908 (9pp.) (2013).
http://dx.doi.org/10.1118/1.4794498
41.
41. L. Yu, M. Shiung, D. Jondal, and C. H. McCollough, “Development and validation of a practical lower-dose-simulation tool for optimizing computed tomography scan protocols,” J. Comput. Assist. Tomogr. 36, 477487 (2012).
http://dx.doi.org/10.1097/RCT.0b013e318258e891
42.
42. G. Yadava, S. Kulkarni, Z. R. Colon, J. Thibault, and J. Hsieh, “TU-A-201B-03: Dose reduction and image quality benefits using model based iterative reconstruction (MBIR) technique for computed tomography,” Med. Phys. 37, 3372 (2010).
http://dx.doi.org/10.1118/1.3469177
43.
43. A. Winklehner, C. Karlo, G. Puippe, B. Schmidt, T. Flohr, R. Goetti, T. Pfammatter, T. Frauenfelder, and H. Alkadhi, “Raw data-based iterative reconstruction in body CTA: Evaluation of radiation dose saving potential,” Eur. Radiol. 21, 25212526 (2011).
http://dx.doi.org/10.1007/s00330-011-2227-y
44.
44. Y. Zhang and R. Ning, “Investigation of image noise in cone-beam CT imaging due to photon counting statistics with the Feldkamp algorithm by computer simulations,” J. X-Ray Sci. Technol. 16, 143158 (2008).
45.
45. P. Massoumzadeh, S. Don, C. F. Hildebolt, K. T. Bae, and B. R. Whiting, “Validation of CT dose-reduction simulation,” Med. Phys. 36, 174174 (2009).
http://dx.doi.org/10.1118/1.3031114
http://aip.metastore.ingenta.com/content/aapm/journal/medphys/41/1/10.1118/1.4851635
Loading
/content/aapm/journal/medphys/41/1/10.1118/1.4851635
Loading

Data & Media loading...

Loading

Article metrics loading...

/content/aapm/journal/medphys/41/1/10.1118/1.4851635
2013-12-31
2016-09-30

Abstract

To develop and evaluate an image-domain noise reduction method based on a modified nonlocal means (NLM) algorithm that is adaptive to local noise level of CT images and to implement this method in a time frame consistent with clinical workflow.

A computationally efficient technique for local noise estimation directly from CT images was developed. A forward projection, based on a 2D fan-beam approximation, was used to generate the projection data, with a noise model incorporating the effects of the bowtie filter and automatic exposure control. The noise propagation from projection data to images was analytically derived. The analytical noise map was validated using repeated scans of a phantom. A 3D NLM denoising algorithm was modified to adapt its denoising strength locally based on this noise map. The performance of this adaptive NLM filter was evaluated in phantom studies in terms of in-plane and cross-plane high-contrast spatial resolution, noise power spectrum (NPS), subjective low-contrast spatial resolution using the American College of Radiology (ACR) accreditation phantom, and objective low-contrast spatial resolution using a channelized Hotelling model observer (CHO). Graphical processing units (GPU) implementation of this noise map calculation and the adaptive NLM filtering were developed to meet demands of clinical workflow. Adaptive NLM was piloted on lower dose scans in clinical practice.

The local noise level estimation matches the noise distribution determined from multiple repetitive scans of a phantom, demonstrated by small variations in the ratio map between the analytical noise map and the one calculated from repeated scans. The phantom studies demonstrated that the adaptive NLM filter can reduce noise substantially without degrading the high-contrast spatial resolution, as illustrated by modulation transfer function and slice sensitivity profile results. The NPS results show that adaptive NLM denoising preserves the shape and peak frequency of the noise power spectrum better than commercial smoothing kernels, and indicate that the spatial resolution at low contrast levels is not significantly degraded. Both the subjective evaluation using the ACR phantom and the objective evaluation on a low-contrast detection task using a CHO model observer demonstrate an improvement on low-contrast performance. The GPU implementation can process and transfer 300 slice images within 5 min. On patient data, the adaptive NLM algorithm provides more effective denoising of CT data throughout a volume than standard NLM, and may allow significant lowering of radiation dose. After a two week pilot study of lower dose CT urography and CT enterography exams, both GI and GU radiology groups elected to proceed with permanent implementation of adaptive NLM in their GI and GU CT practices.

This work describes and validates a computationally efficient technique for noise map estimation directly from CT images, and an adaptive NLM filtering based on this noise map, on phantom and patient data. Both the noise map calculation and the adaptive NLM filtering can be performed in times that allow integration with clinical workflow. The adaptive NLM algorithm provides effective denoising of CT data throughout a volume, and may allow significant lowering of radiation dose.

Loading

Full text loading...

/deliver/fulltext/aapm/journal/medphys/41/1/1.4851635.html;jsessionid=Nx00Hg9k5OZBvG4cAU6UHMcu.x-aip-live-03?itemId=/content/aapm/journal/medphys/41/1/10.1118/1.4851635&mimeType=html&fmt=ahah&containerItemId=content/aapm/journal/medphys
true
true

Access Key

  • FFree Content
  • OAOpen Access Content
  • SSubscribed Content
  • TFree Trial Content
752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
/content/realmedia?fmt=ahah&adPositionList=
&advertTargetUrl=//oascentral.aip.org/RealMedia/ads/&sitePageValue=online.medphys.org/41/1/10.1118/1.4851635&pageURL=http://scitation.aip.org/content/aapm/journal/medphys/41/1/10.1118/1.4851635'
Right1,Right2,Right3,