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EM+TV Based Reconstruction for Cone-Beam CT with Reduced Radiation

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Advances in Visual Computing (ISVC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6938))

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Abstract

Computerized tomography (CT) plays a critical role in modern medicine. However, the radiation associated with CT is significant. Methods that can enable CT imaging with less radiation exposure but without sacrificing image quality are therefore extremely important. This paper introduces a novel method for enabling image reconstruction at lower radiation exposure levels with convergence analysis. The method is based on the combination of expectation maximization (EM) and total variation (TV) regularization. While both EM and TV methods are known, their combination as described here is novel. We show that EM+TV can reconstruct a better image using much fewer views, thus reducing the overall dose of radiation. Numerical results show the efficiency of the EM+TV method in comparison to filtered backprojection and classic EM. In addition, the EM+TV algorithm is accelerated with GPU multicore technology, and the high performance speed-up makes the EM+TV algorithm feasible for future practical CT systems.

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References

  1. Shepp, L., Logan, B.: The Fourier reconstruction of a head section. IEEE Transaction on Nuclear Science 21, 21–34 (1974)

    Article  Google Scholar 

  2. Pan, X., Sidky, E., Vannier, M.: Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction? Inverse Problems 25, 123009 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  3. Kak, A., Slaney, M.: Principles of Computerized Tomographic Imaging. Society of Industrial and Applied Mathematics, Philadelphia (2001)

    Book  MATH  Google Scholar 

  4. Shepp, L., Vardi, Y.: Maximum likelihood reconstruction for emission tomography. IEEE Transaction on Medical Imaging 1, 113–122 (1982)

    Article  Google Scholar 

  5. Rudin, L., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys D 60, 259–268 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  6. Sidky, E., Pan, X.: Image reconstruction in circular cone-beam computed tomography by total variation minimization. Physics in Medicine and Biology 53, 4777–4807 (2008)

    Article  Google Scholar 

  7. Brune, C., Sawatzky, A., Wubbeling, F., Kosters, T., Burger, M.: An analytical view on EM-TV based methods for inverse problems with Poisson noise. Preprint, University of Münster (2009)

    Google Scholar 

  8. Jia, X., Lou, Y., Li, R., Song, W., Jiang, S.: GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation. Medical Physics 37, 1757–1760 (2010)

    Article  Google Scholar 

  9. Yan, M., Vese, L.A.: Expectation maximization and total variation based model for computed tomography reconstruction from undersampled data. In: Proceeding of SPIE Medical Imaging: Physics of Medical Imaging, vol. 7961, p. 79612X (2011)

    Google Scholar 

  10. Chen, J., Yan, M., Vese, L.A., Villasenor, J., Bui, A., Cong, J.: EM+TV for reconstruction of cone-beam CT with curved detectors using GPU. In: Proceedings of International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, pp. 363–366 (2011)

    Google Scholar 

  11. Compressive_Sensing_Resources, http://dsp.rice.edu/cs

  12. Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press, Cambridge (2004)

    Book  MATH  Google Scholar 

  13. Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B 39, 1–38 (1977)

    MathSciNet  MATH  Google Scholar 

  14. Siddon, R.: Fast calculation of the exact radiological path for a three-dimensional CT array. Medical Physics 12, 252–255 (1986)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mathematics, University of California, Los Angeles, USA

    Ming Yan & Luminita A. Vese

  2. Department of Electrical Engineering, University of California, Los Angeles, USA

    Jianwen Chen & John Villasenor

  3. Department of Radiological Sciences, University of California, Los Angeles, USA

    Alex Bui

  4. Department of Computer Science, University of California, Los Angeles, CA, 90095, United States

    Jason Cong

Authors
  1. Ming Yan
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  2. Jianwen Chen
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  3. Luminita A. Vese
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  4. John Villasenor
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  5. Alex Bui
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  6. Jason Cong
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Editor information

Editors and Affiliations

  1. University of Nevada, 89557, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, 94035, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, 94720, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, 89512, Reno, NV, USA

    Darko Koracin

  5. Department of Computer Science and Engineering, University of South Carolina, 29208, Columbia, SC, USA

    Song Wang

  6. HRL Laboratories, 3011 Malibu Canyon Road, 90265-4797, Malibu, CA, USA

    Kim Kyungnam

  7. Purdue University, 47907-2021, West Lafayette, IN, USA

    Bedrich Benes

  8. Sandia National Laboratory, 87185, Albuquerque, NM, USA

    Kenneth Moreland

  9. University of Louisiana, 70504, Lafayette, LA, USA

    Christoph Borst

  10. Adobe Systems Incorporated, San Francisco, CA, USA

    Stephen DiVerdi

  11. Polytechnic Institute of NYU, 11201, Brooklyn, NY, USA

    Chiang Yi-Jen

  12. Lawrence Livermore National Laboratory, 94551-0808, Livermore, CA, USA

    Jiang Ming

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© 2011 Springer-Verlag Berlin Heidelberg

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Yan, M., Chen, J., Vese, L.A., Villasenor, J., Bui, A., Cong, J. (2011). EM+TV Based Reconstruction for Cone-Beam CT with Reduced Radiation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6938. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24028-7_1

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  • DOI: https://doi.org/10.1007/978-3-642-24028-7_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24027-0

  • Online ISBN: 978-3-642-24028-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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