Skip to main content

Quantitative Electron Density CT Imaging for Radiotherapy Planning

  • Conference paper
  • First Online:
Medical Image Understanding and Analysis (MIUA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 723))

Included in the following conference series:

Abstract

Computed tomography (CT) is the imaging modality used to calculate the deposit of dose in radiotherapy planning, where the physical interactions are modelled based upon the electron density, which can be calculated from CT images. Traditionally this is a three step process: linearising the raw x-ray measurements and correcting for beam-hardening and scatter; inverting the system with analytic or iterative reconstruction algorithms into linear attenuation coefficient; then applying a non-linear calibration into electron density. In this work, we propose a new method for statistically inferring a quantitative image of electron density directly from the raw CT measurements, with no pre- or post-processing necessary, and able to cope with both beam-hardening from a single polyenergetic source and additive scatter. We evaluate this concept with cone-beam CT (CBCT) imaging for bladder cancer, where we demonstrate significantly higher electron density accuracy than other quantitative approaches. We also show through simulated photon and proton beam calculation, that our method may facilitate superior dose estimation, especially with regions containing bony structures.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Schneider, U., Pedroni, E., Lomax, A.: The calibration of CT Hounsfield units for radiotherapy treatment planning. Phys. Med. Biol. 41(1), 111–124 (1996)

    Article  Google Scholar 

  2. Curry, T.S., Dowdey, J.E., Murry, R.C.: Christensen’s Physics of Diagnostic Radiology (1990)

    Google Scholar 

  3. Joseph, P.M., Spital, R.D.: A method for correcting bone induced artifacts in computed tomography scanners (1978)

    Google Scholar 

  4. Fessler, J.A.: Fundamentals of CT reconstruction in 2D and 3D. Compr. Biomed. Phys. 2(2.11), 263–295 (2014). Elsevier

    Article  Google Scholar 

  5. Elbakri, I.A., Fessler, J.A.: Statistical image reconstruction for polyenergetic X-ray computed tomography. IEEE Trans. Med. Imaging 21(2), 89–99 (2002)

    Article  Google Scholar 

  6. Elbakri, I.A., Fessler, J.A.: Segmentation-free statistical image reconstruction for polyenergetic X-ray computed tomography with experimental validation. Phys. Med. Biol. 48(15), 2453–2477 (2003)

    Article  Google Scholar 

  7. De Man, B., Nuyts, J., Dupont, P., Marchal, G., Suetens, P.: An iterative maximum-likelihood polychromatic algorithm for CT. IEEE Trans. Med. Imaging 20(10), 999–1008 (2001)

    Article  Google Scholar 

  8. Bouman, C., Sauer, K.: A generalized Gaussian image model for edge-preserving MAP estimation. IEEE Trans. Image Process. 2(3), 296–310 (1993)

    Article  Google Scholar 

  9. Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D Nonlinear Phenom. 60(1–4), 259–268 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  10. Daubechies, I., Fornasier, M., Loris, I.: Accelerated projected gradient method for linear inverse problems with sparsity constraints. J. Fourier Anal. Appl. 14(5–6), 764–792 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  11. Siewerdsen, J.H., Jaffray, D.A.: Cone-beam computed tomography with a flat-panel imager: magnitude and effects of X-ray scatter. Med. Phys. 28(2), 220 (2001)

    Article  Google Scholar 

  12. Tuy, H.K.: An inversion formula for cone-beam reconstruction. SIAM J. Appl. Math. 43(3), 546–552 (1983)

    Article  MathSciNet  Google Scholar 

  13. Chang, Z., Zhang, R., Thibault, J.-B., Sauer, K., Bouman, C.: Statistical X-ray computed tomography imaging from photon-starved measurements. SPIE Comput. Imaging 9020, 90200G (2014)

    Article  Google Scholar 

  14. ICRP Publication 89. Basic anatomical and physiological data for use in radiological protection reference values. Ann. ICRP 32, 3–4 (2002)

    Google Scholar 

  15. Chouzenoux, E., Pesquet, J.C., Repetti, A.: Variable metric forward-backward algorithm for minimizing the sum of a differentiable function and a convex function. J. Optim. Theory Appl. 162(1), 107–132 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  16. Erdogan, H., Fessler, J.A.: Monotonic algorithms for transmission tomography. IEEE Trans. Med. Imaging 18(9), 801–814 (1999)

    Article  Google Scholar 

  17. ICRP Publication 110. Adult Reference Computational Phantoms. Ann. ICRP 39(2) (2009)

    Google Scholar 

  18. Jan, S., Benoit, D., Becheva, E., Carlier, T., Cassol, F., Descourt, P., Frisson, T., Grevillot, L., Guigues, L., Maigne, L., Morel, C., Perrot, Y., Rehfeld, N., Sarrut, D., Schaart, D.R., Stute, S., Pietrzyk, U., Visvikis, D., Zahra, N., Buvat, I.: GATE V6: a major enhancement of the GATE simulation platform enabling modelling of CT and radiotherapy. Phys. Med. Biol. 56(4), 881–901 (2011)

    Article  Google Scholar 

  19. Xu, Y., Bai, T., Yan, H., Ouyang, L., Pompos, A., Wang, J., Zhou, L., Jiang, S.B., Jia, X.: A practical cone-beam CT scatter correction method with optimized Monte Carlo simulations for image-guided radiation therapy. Phys. Med. Biol. 60(9), 3567–3587 (2015)

    Article  Google Scholar 

  20. Fessler, J.A.: Image Reconstruction Toobox. https://web.eecs.umich.edu/~fessler/code/. Accessed 14 Nov 2014

  21. Barrett, A., Morris, S., Dobbs, J., Roques, T.: Practical Radiotherapy Planning. 4th edn (2009)

    Google Scholar 

  22. Feldkamp, L.A., Davis, L.C., Kress, J.W.: Practical cone-beam algorithm. J. Opt. Soc. Am. A 1(6), 612 (1984)

    Article  Google Scholar 

  23. Wang, G.: X-ray micro-CT with a displaced detector array. Med. Phys. 29(7), 1634 (2002)

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Maxwell Advanced Technology Fund, EPSRC DTP studentship funds and ERC project: C-SENSE (ERC-ADG-2015-694888) for supporting this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jonathan H. Mason .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Mason, J.H., Perelli, A., Nailon, W.H., Davies, M.E. (2017). Quantitative Electron Density CT Imaging for Radiotherapy Planning. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-60964-5_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60963-8

  • Online ISBN: 978-3-319-60964-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics