Skip to main content

An Overview of Some Mathematical Methods for Medical Imaging

  • Conference paper
Molecular Imaging: Computer Reconstruction and Practice

Abstract

This chapter based on a series of lecture notes proposes an overview of mathematical concepts commonly used in medical physics and in medical imaging. One goal is to summarize basic and, in principle, well-known tools such as the continuous and discrete Fourier transforms, Shannon’s sampling theorem, or the singular value decomposition of a matrix or operator. Tomographic reconstruction is covered in another chapter. Due to differences in vocabulary, notations and context, it is often difficult to grasp the links between inverse problems as different as image reconstruction in emission tomography,18 photoacoustic tomography,25 image registration, 12 or simply data fitting in the analysis of time sequences. The second goal of this chapter is therefore to give a synthetic and coherent view of the structure of the inverse problems encountered in this field, by rigorously defining concepts such as ill-posedness, stability, and regularization. This will allow a better understanding of the rationale behind data processing methods described in the literature.

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 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bertero M and Boccacci P 1998 Introduction to Inverse Problems in Imaging, Institute of Physics

    Google Scholar 

  2. Barrett H H and Myers K J 2004 Foundation of Image Science, Wiley, New York

    Google Scholar 

  3. Bertero M, De Mol C and Pike E R 1985 Linear inverse problems with discrete data. I: General formulation and singular system analysis Inverse Probl. 1 301-330

    Article  MATH  MathSciNet  ADS  Google Scholar 

  4. Bertero M, De Mol C and Pike E R 1988 Linear inverse problems with discrete data. II: Stability and regularization Inverse Probl. 4 573-594

    Article  MATH  MathSciNet  ADS  Google Scholar 

  5. Brankov J G, Yang Y and Wernick M N 2004 Tomographic image reconstruction based on a content-adaptive mesh model IEEE Trans Med Imag 23 202-212

    Article  Google Scholar 

  6. Champeney D C 1990 A Handbook of Fourier Theorems, Cambridge University Press, Cambridge

    Google Scholar 

  7. Daubechies I 1992 Ten Lectures on Wavelets, SIAM, Philadelphia, PA

    MATH  Google Scholar 

  8. Daubechies I, Defrise M and De Mol C 2004 An iterative thresholding algorithm for linear inverse problems with a sparsity constraint Comm Pure Appl Math 57 1416-1457

    Article  MathSciNet  Google Scholar 

  9. de Boor C 1978 A Practical Guide to Splines, Springer, New York

    MATH  Google Scholar 

  10. Doyley M M, Meaney P M and Bamber J C 2000 Evaluation of an iterative reconstruction method for quantitative elastography Phys Med Biol 45 1521-1540

    Article  Google Scholar 

  11. Donoho D L 1995 Nonlinear solution of linear inverse problems by wavelet-vaguelette de composition Appl Comp Harmonic Anal 2 101-126

    Article  MATH  MathSciNet  Google Scholar 

  12. Hill D L G, Batchelor P G, Holden M and Hawkes D J 2001 Medical image registration Phys Med Biol 46 R1-R45

    Article  ADS  Google Scholar 

  13. Khurd P and Gindi G 2005 Fast LROC analysis of Bayesian reconstructed emission tomographic images using model observers Phys Med Biol 50 1519-1532

    Article  Google Scholar 

  14. Lange K and Carson R 1984 EM reconstruction algorithms for emission and transmission tomography J Comp Assist Tomo 8 306-316

    Google Scholar 

  15. Lazaro D, El Bitar Z, Breton V, Hill D and Buvat I 2005 Fully 3D Monte Carlo reconstruction in SPECT: a feasibility study Phys Med Biol 50 3739-3754

    Article  Google Scholar 

  16. Lee B I, Oh S H, Woo E Je, Lee S Y, Cho M H, Kwon O, Seo J K, Lee J-Y and Baek W S 2003 3D forward solver and its performance analysis for magnetic resonance electrical im-pedance tomography (MREIT) using recessed electrodes Phys Med Biol 48 1971-1986

    Article  Google Scholar 

  17. Lewitt R M 1992 Alternatives to voxels for image representation in iterative reconstruction algorithms Phys Med Biol 37 705-716

    Article  Google Scholar 

  18. Lewitt R M and Matej S 2003 Overview of methods for image reconstruction from projections in emission computed tomography Proc IEEE 91 1588-1611

    Article  Google Scholar 

  19. Mallat S 1999 A Wavelet Tour of Signal Processing, Academic, San Diego, CA

    MATH  Google Scholar 

  20. Metz C E and Goodenough D J 1972 Evaluation of receiver operating characteristic curves in terms of information theory Phys Med Biol 17 872-873

    Article  Google Scholar 

  21. Mumcuoglu E, Leahy R M and Cherry S R 1996 Bayesian reconstruction of PET images: methodology and performance analysis Phys Med Biol 41 1777-1807

    Article  Google Scholar 

  22. Natterer F 1986 The Mathematics of Computerized Tomography, Wiley, New York

    MATH  Google Scholar 

  23. Natterer F and Wubbeling F 2001 Mathematical Methods in Image Reconstruction, SIAM, Philadelphia, PA

    MATH  Google Scholar 

  24. Noo F, Clackdoyle R and Pack J D 2004 A two-step Hilbert transform method for 2D image reconstruction Phys Med Biol 49 3903-3923

    Article  Google Scholar 

  25. Patch S 2004 Thermoacoustic tomography: consistency conditions and the partial scan problem Phys Med Biol 49 2305-2315

    Article  Google Scholar 

  26. Press W H, Teukolsky S A, Vetterling W T and Flannery B P 1994 Numerical Recipes in C, Cambridge University Press, Cambridge

    Google Scholar 

  27. Strichartz R S 2003 A Guide to Distribution Theory and Fourier Transforms, World Scientific Publishing

    Google Scholar 

  28. Unser M 1999 Splines: a perfect fit for signal and image processing IEEE Signal Proc Mag 16 22-38

    Article  ADS  Google Scholar 

  29. Unser M 2000 Sampling - 50 years after Shannon Proc. IEEE 88 569-587

    Article  Google Scholar 

  30. Wang R K, Hebden J C and Tuchin V V 2004 Special issue on recent developments in biomedical optics Phys Med Biol 49 no 7 1085-1368

    Article  Google Scholar 

  31. Webb S 1990 From the Watching of the Shadows, Adam Hilger, Bristol

    Google Scholar 

  32. Welch A, Campbell C, Clackdoyle R, Natterer F, Hudson M, Bromiley A, Mikecz P, Chillcot F, Dodd M, Hopwood P, Craib S, Gullberg G T and Sharp P 1998 Attenuation correction in PET using consistency information, Nuclear Science, IEEE Transactions on Volume 45, Issue 6, Part 2, Dec. 1998 Page(s):3134-3141

    Article  Google Scholar 

  33. Wilson D W, Tsui B M W and Barrett H H 1994 Noise properties of the EM algorithm. II. Monte Carlo simulations Phys Med Biol 39 847-871

    Article  Google Scholar 

  34. Yendiki A and Fessler J A 2004 Comparison of rotation- and blob-based system models for 3D SPECT with depth-dependent detector response Phys Med Biol 49 2157-2168

    Article  Google Scholar 

  35. Zou Y and Pan X 2004 Exact image reconstruction on PI-lines from minimum data in helical conebeam CT Phys Med Biol 49 941-959

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer Science+Business Media B.V

About this paper

Cite this paper

Defrise, M., De Mol, C. (2008). An Overview of Some Mathematical Methods for Medical Imaging. In: Lemoigne, Y., Caner, A. (eds) Molecular Imaging: Computer Reconstruction and Practice. NATO Science for Peace and Security Series B: Physics and Biophysics. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8752-3_6

Download citation

Publish with us

Policies and ethics