Advertisement

Use of Photon Scattering Interactions in Diagnosis and Treatment of Disease

  • Robert Moss
  • Andrea Gutierrez
  • Amany Amin
  • Chiaki Crews
  • Robert Speller
  • Francesco Iacoviello
  • Paul Shearing
  • Sarah Vinnicombe
  • Selina Kolokytha
Chapter

Abstract

This chapter looks at photon scattering applications in medicine. In the energy range of interest there are two types of scattering events, incoherent (Compton) and coherent (Rayleigh) scattering, and this chapter looks at how these events can be usefully used in the diagnosis and treatment of disease. In the first part we present an overview of Compton cameras for gamma imaging in the context of proton beam therapy, where they can be used for proton range verification. Proton beam therapy is currently in need of range verification for quality assurance and to improve treatment efficacy and safety. We will first briefly introduce potential methods for in vivo proton range verification, of which prompt gamma imaging is a promising example. We describe the process of gamma emission during proton irradiation, as well as the challenges of its detection and interpretation. The use of Compton camera for prompt gamma imaging has advantages over other gamma detectors since it does not require mechanical collimators and has a typical field of view of 180°. The Compton camera’s principle of operation and design criteria for prompt gamma imaging are described, as well as image reconstruction techniques such as back-projection and stochastic origin ensemble.

The second part of the chapter presents tissue diffraction, based upon coherent scattering as a diagnostic tool. X-ray diffraction (XRD) is a technique which can be used to calculate the atomic or molecular structure of a material by measuring X-ray scattering profiles. While XRD has been a longstanding tool in analytical and materials science, this section reviews the relatively new application of XRD to the differentiation of healthy and cancerous tissue and how the results compare to conventional histopathology. As well as outlining the typical signatures expected of different tissue types, the hardware and data processing requirements will be discussed, particularly in the context of the trade-offs that would need to be considered in the design and development of a clinically deployable system.

Keywords

Compton scattering Imaging Coherent scattering X-ray diffraction Tissue analysis 

References

  1. 1.
    P.P. Dendy, B. Heaton, Physics for Diagnostic Radiology, Medical Science Series (IoP Publishing, UK, 1999), p. 122CrossRefGoogle Scholar
  2. 2.
    R.D. Speller, J.A. Horrocks, Photon scattering – a ‘new’ source of information in medicine and biology? Phys. Med. Biol. 36, 1 (1991)CrossRefGoogle Scholar
  3. 3.
    T. Koligliatis, J. Kalef-Ezra, R.D. Speller, M.J. Mooney, J. Litsas, Compton scatter densitometry in bone: influence of the anatomical site. Phys. Med. 14, 73 (1998)Google Scholar
  4. 4.
    M.A. Kumakhov, A.F. Gamaliy, V.N. Vasiliev, M.Y. Zaytsev, K.V. Zaytseva, A.A. Markelov, Y.V. Ozerov, Scattered X-rays in medical diagnostics. SPIE 5943, 210 (2005)Google Scholar
  5. 5.
    M. Antoniassi, A.L.C. Conceicao, M.E. Poletti, Characterization of breast tissues using Compton scattering. Nucl. Instrum. Methods Phys. Res., Sect. A 619, 375 (2010)CrossRefGoogle Scholar
  6. 6.
    M. Antoniassi, A.L.C. Conceicao, M.E. Ploetti, Study of effective atomic number of breast tissues determined using the elastic to inelastic scattering ratio. Nucl. Instrum. Methods Phys. Res., Sect. A 652, 739 (2011)CrossRefGoogle Scholar
  7. 7.
    M. Georgiadis, M. Guizar-Sicairos, A. Zwahlen, A. Trussel, O. Bunk, R. Muller, P. Schneider, 3D scanning SAXS: a novel method for the assessment of bone ultrastructure orientation. Bone 71, 42 (2015)CrossRefGoogle Scholar
  8. 8.
    N. Tamura, P.U. Gilbert, X-ray microdiffraction of biominerals. Methods Enzymol. 532, 501 (2013)CrossRefGoogle Scholar
  9. 9.
    C. Dawson, J.A. Horrocks, R. Kwong, R.D. Speller, H.N. Whitfield, Low-angle X-ray scattering signatures of urinary calculi. World J. Urol. 14, S43 (1996)CrossRefGoogle Scholar
  10. 10.
    S. Sidhu, G. Falzon, S.A. Hart, J.G. Fox, R.A. Lewis, K.K.W. Siu, Classification of breast tissue using a laboratory system for small-angle x-ray scattering (SAXS). Phys. Med. Biol. 56, 6779 (2011)CrossRefGoogle Scholar
  11. 11.
    B. Ghammraoui, L. Popescu, Non-invasive classification of breast microcalcifications using X-ray coherent scatter computed tomography. Phys. Med. Biol. 62, 1192 (2017)CrossRefGoogle Scholar
  12. 12.
    C. Sosa, A. Malezan, M.E. Poletti, R.D. Perez, Compact energy dispersive X-ray microdiffractometer for diagnosis of neoplastic tissues. Radiat. Phys. Chem. 137, 125 (2017)CrossRefGoogle Scholar
  13. 13.
    W. Elsharkawy, W. Elshemey, Quantitative characterization of fatty liver disease using X-ray scattering. Radiat. Phys Chem. 92, 14 (2013)CrossRefGoogle Scholar
  14. 14.
    H. Paganetti, Range uncertainties in proton therapy and the role of Monte Carlo simulations. Phys. Med. Biol. 57, R99–R117 (2012)CrossRefGoogle Scholar
  15. 15.
    A.-C. Knopf, A. Lomax, In vivo proton range verification: a review. Phys. Med. Biol. 58, R131–R160 (2013)CrossRefGoogle Scholar
  16. 16.
    H.-M. Lu, A potential method for in vivo range verification in proton therapy treatment. Phys. Med. Biol. 53, 1413–1424 (2008)CrossRefGoogle Scholar
  17. 17.
    H.-M. Lu etal., Investigation of an implantable dosimeter for single-point water equivalent path length verification in proton therapy. Med. Phys. 37, 5858–5866 (2010)CrossRefGoogle Scholar
  18. 18.
    B. Gottschalk etal., Water equivalent path length measurement in proton radiotherapy using time resolved diode dosimetry. Med. Phys. 38, 2282–2288 (2011)CrossRefGoogle Scholar
  19. 19.
    E.H. Bentefour etal., Effect of tissue heterogeneity on an in vivo range verification technique for proton therapy. Phys. Med. Biol. 57, 5473–5484 (2012)CrossRefGoogle Scholar
  20. 20.
    L. Sulak etal., Experimental studies of the acoustic signature of proton beams traversing fluid media. Nucl. Instrum. Methods 161, 203–217 (1979)CrossRefGoogle Scholar
  21. 21.
    Y. Hayakawa etal., Acoustic pulse generated in a patient during treatment by pulsed proton radiation beam. Radiat. Oncol. Investig. 3, 42–25 (1995)CrossRefGoogle Scholar
  22. 22.
    W. Assmann etal., Ionoacoustic characterization of the proton Bragg peak with submillimeter accuracy. Med. Phys. 42(2), 567–574 (2015)CrossRefGoogle Scholar
  23. 23.
    M. Ahmad etal., Theoretical detection threshold of the proton-acoustic range verification technique. Med. Phys. 42(10), 5735–5744 (2015)CrossRefGoogle Scholar
  24. 24.
    S. Kellnberger etal., Ionoacoustic tomography of the proton Bragg peak in combination with ultrasound and optoacoustic imaging. Sci. Rep. 6, 29305 (2016).  https://doi.org/10.1038/srep29305 CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    K. Parodi etal., Patient study of in vivo verification of beam delivery and range, using positron emission tomography and computed tomography imaging after proton therapy. Int. J. Radiat. Oncol. Biol. Phys. 68, 920–934 (2007)CrossRefGoogle Scholar
  26. 26.
    M.T. Studenski, Y. Xiao, Proton therapy dosimetry using positron emission tomography. World J. Radiol. 2, 135–142 (2010)CrossRefGoogle Scholar
  27. 27.
    S. Agostinelli etal., Geant4 – a simulation toolkit. Nucl. Instrum. Methods Phys. Res., Sect. A 506, 250–303 (2003)CrossRefGoogle Scholar
  28. 28.
    J.M. Verburg etal., Simulation of prompt gamma-ray emission during proton radiotherapy. Phys. Med. Biol. 57, 5459–5472 (2012)CrossRefGoogle Scholar
  29. 29.
    F. Stichelbaut, Y. Jongen, in Verification of Prompt Beam Position in the Patient by the Detection of Prompt Gamma-Rays Emission. 39th Meeting In the Particle Therapy Cooperative Group, San Francisco (2003)Google Scholar
  30. 30.
    C.H. Min etal., Prompt gamma measurements for locating the dose falloff region in the proton therapy. Appl. Phys. Lett. 89, 183517 (2006)CrossRefGoogle Scholar
  31. 31.
    D. Kim etal., Pinhole camera measurements of prompt gamma-rays for detection of beam range variation in proton therapy. J. Korean Phys. Soc. 55, 1673–1676 (2009)CrossRefGoogle Scholar
  32. 32.
    V. Bom etal., Real-time prompt gamma monitoring in spot-scanning proton therapy using imaging through a knife-edge-shaped slit. Phys. Med. Biol. 57, 297–308 (2012)CrossRefGoogle Scholar
  33. 33.
    J. Smeets etal., Prompt gamma imaging with a slit camera for real-time range control in proton therapy. Phys. Med. Biol. 57, 3371–3405 (2012)CrossRefGoogle Scholar
  34. 34.
    C.H. Min etal., Development of array-type prompt gamma measurements system for in vivo range verification in proton therapy. Med. Phys. 39, 2100–2107 (2012)CrossRefGoogle Scholar
  35. 35.
    J. Krimmer etal., Collimated prompt gamma TOF measurements with multi-slit multi-detector configurations. J. Instrum. 10, P01011 (2015)CrossRefGoogle Scholar
  36. 36.
    J. Smeets etal., Experimental comparison of knife-edge and multi-parallel collimators for prompt gamma imaging of proton pencil beams. Front. Oncol. 6, 156 (2016)CrossRefGoogle Scholar
  37. 37.
    M. Frandes etal., A tracking Compton-scattering imaging system for hadron therapy monitoring. IEEE Trans. Nucl. Sci. 57, 144–150 (2010)CrossRefGoogle Scholar
  38. 38.
    F. Roellinghoff etal., Design of a Compton camera for 3D prompt gamma imaging during ion beam therapy. Nucl. Instrum. Methods Phys. Res., Sect. A 648, S20–S23 (2011)CrossRefGoogle Scholar
  39. 39.
    C. Golnik etal., Tests of a Compton imaging prototype in a monoenergetic 4.44 MeV photon field – a benchmark setup for prompt gamma-ray imaging devices. J. Instrum. 11, P06009 (2016)CrossRefGoogle Scholar
  40. 40.
    S.W. Peterson etal., Optimizing a 3-stage Compton camera for measuring prompt gamma rays emitted during proton radiotherapy. Phys. Med. Biol. 55, 6841–6856 (2010)CrossRefGoogle Scholar
  41. 41.
    D. Robertson etal., Material efficiency studies for a Compton camera designed to measure characteristic prompt gamma rays emitted during proton beam radiotherapy. Phys. Med. Biol. 56, 3047–3059 (2011)CrossRefGoogle Scholar
  42. 42.
    J.C. Polf etal., Imaging of prompt gamma rays emitted during delivery of clinical proton beams with a Compton camera: feasibility studies for range verification. Phys. Med. Biol. 60, 7085–7099 (2015)CrossRefGoogle Scholar
  43. 43.
    P. Cambraia Lopes etal., Time-resolved imaging of prompt-gamma rays for proton range verification using a knife-edge slit camera based on digital photon counters. Med. Phys. Biol. 60, 6063–6085 (2015)CrossRefGoogle Scholar
  44. 44.
    C. Richter etal., First clinical application of a prompt gamma based in vivo proton range verification system. Radiother. Oncol. 118, 232–237 (2016)CrossRefGoogle Scholar
  45. 44.
    R.W. Todd, J.M. Nightingale, D.B. Everett, Nature 251, 132–134 (1974)CrossRefGoogle Scholar
  46. 45.
    M. Singh, D. Doria, An electronically collimated gamma camera for single photon emission computed tomography. Part II: Image reconstruction and preliminary measurements. Med. Phys. 10, 427–435 (1983)Google Scholar
  47. 46.
    O. Klein, Y. Nishina, Über die Streuung von Strahlung durch freie Elektronen nach der neuen relativistischen Quantendynamik von Dirac. Z. Phys. 52(11–12), 853–868 (1929)CrossRefGoogle Scholar
  48. 47.
    S.J. Wilderman etal., Fast algorithm for list mode back-projection of Compton scatter camera data. IEEE Trans. Nucl. Sci. 45, 957–962 (1998)CrossRefGoogle Scholar
  49. 48.
    S.E. King, A solid-state Compton camera for three-dimensional imaging. Nucl. Instrum. Methods Phys. Res., Sect. A 353, 320–323 (1994)CrossRefGoogle Scholar
  50. 49.
    B. Smith, Reconstruction methods and completeness conditions for two Compton data models. J. Opt. Soc. Am. A 22, 445–459 (2005)CrossRefGoogle Scholar
  51. 50.
    T. Hebert etal., Three-dimensional maximum likelihood reconstruction for an electronically collimated single-photon-emission imaging system. J. Opt. Soc. Am. A 7, 1305–1313 (1990)CrossRefGoogle Scholar
  52. 51.
    S.J. Wilderman etal., Improved modeling of system response in list mode EM reconstruction of Compton scatter camera images. IEEE Trans. Nucl. Sci. 48, 111–116 (2001)CrossRefGoogle Scholar
  53. 52.
    L. Han etal., Statistical performance evaluation and comparison of a Compton medical imaging system and a collimated Anger camera for higher energy photon imaging. Phys. Med. Biol. 53, 7029–7045 (2008)CrossRefGoogle Scholar
  54. 53.
    S.M. Kim etal., Fully three-dimensional OSEM-based image reconstruction for Compton imaging using optimized ordering schemes. Phys. Med. Biol. 55, 5007–5027 (2010)CrossRefGoogle Scholar
  55. 54.
    V.-G. Nguyen etal., GPU-accelerated 3D Bayesian image reconstruction from Compton scattered data. Phys. Med. Biol. 56, 2817–2836 (2011)CrossRefGoogle Scholar
  56. 55.
    J. Cui etal., in Fast and Accurate 3D Compton Cone Projections on GPU Using CUDA. Proc. IEEE Nucl. Sci. Symp. Med. Imag. Conf. (NSS/MIC) (2011), pp. 2572–2575Google Scholar
  57. 56.
    V.-G. Nguyen, S.-J. Lee, GPU-accelerated iterative reconstruction from Compton scattered data using a matched pair of conic projector and backprojector. Comput. Methods Prog. Biomed. 131, 27–36 (2016)CrossRefGoogle Scholar
  58. 57.
    A. Andreyev etal., Fast image reconstruction for Compton camera using stochastic origin ensemble approach. Med. Phys. 38, 429–438 (2011)CrossRefGoogle Scholar
  59. 58.
    A. Sitek, Representation of photon limited data emission tomography using origin ensemble. Phys. Med. Biol. 53, 3201–3216 (2008)CrossRefGoogle Scholar
  60. 59.
    A. Andreyev etal., in Stochastic Image Reconstruction Method for Compton Camera. Proc. IEEE Nucl. Sci. Symp. Conf. Record (NSS/MIC) (2009), pp. 2985–2988Google Scholar
  61. 60.
    A. Andreyev etal., Resolution recovery for Compton camera using origin ensemble algorithm. Med. Phys. 43, 4866–4876 (2016)CrossRefGoogle Scholar
  62. 61.
    D. Mackin etal., Evaluation of stochastic reconstruction algorithm for use in Compton camera imaging and beam range verification from secondary gamma emission during proton therapy. Phys. Med. Biol. 57, 3537–3553 (2012)CrossRefGoogle Scholar
  63. 62.
    F.X. Avila-Soto etal., in Parallelization for Fast Image Reconstruction Using the Stochastic Origin Ensemble Method for Proton Beam Therapy. REU Site: Interdisciplinary Program in High Performance Computing (2015)Google Scholar
  64. 63.
  65. 64.
    P.P. Provenzano etal., Collagen density promotes mammary tumor initiation and progression. BMC Med. 6, 11 (2008)CrossRefGoogle Scholar
  66. 65.
    L. Cherkezyan etal., Nanoscale changes in chromatin organization represent the initial steps of tumorigenesis: a transmission electron microscopy study. BMC Cancer 14, 189 (2014)CrossRefGoogle Scholar
  67. 66.
    L. Jones etal., HEXITEC ASIC—a pixellated readout chip for CZT detectors. Nucl. Instrum. Methods Phys. Res., Sect. A 604, p34–p37 (2009)CrossRefGoogle Scholar
  68. 67.
    D. O’Flynn etal., Explosive detection using pixellated X-ray diffraction (PixD). J. Instrum. 8, P03007 (2013)CrossRefGoogle Scholar
  69. 68.
    C. Christodoulou etal., Multivariate analysis of pixelated diffraction data. J. Instrum. 6, C12027 (2011)CrossRefGoogle Scholar
  70. 69.
    R. Moss etal., miniPixD: a compact sample analysis system which combines X-ray imaging and diffraction. J. Instrum. 12, P02001 (2017)CrossRefGoogle Scholar
  71. 70.
    F.J. Fleming etal., Intraoperative margin assessment and re-excision rate in breast conserving surgery. Eur. J. Surg. Oncol. 30, 233–237 (2004)CrossRefGoogle Scholar
  72. 71.
    M.S. Moran etal., Society of Surgical Oncology – American Society for Radiation Oncology consensus guideline on margins for breast-conserving surgery with whole-breast irradiation in stages I and II invasive breast cancer. Int. J. Radiat. Oncol. Biol. Phys. 88, 553–564 (2014)CrossRefGoogle Scholar
  73. 72.
    J.K. Pijanka etal., A wide-angle X-ray fibre diffraction method for quantifying collagen orientation across large tissue areas: application to the human eyeball coat. J. Appl. Crystallogr. 46, 1481–1489 (2013)CrossRefGoogle Scholar
  74. 73.
    S. Pani etal., Characterization of breast tissue using energy-dispersive X-ray diffraction computed tomography. Appl. Radiat. Isot. 68, 1980–1987 (2010)CrossRefGoogle Scholar
  75. 74.
    F.B. de la Cuesta etal., Coherent X-ray diffraction from collagenous soft tissues. Proc. Natl. Acad. Sci. U. S. A. 106, 15297–15301 (2009)CrossRefGoogle Scholar
  76. 75.
    G. Kidane etal., X-ray scatter signatures for normal and neoplastic breast tissues. Phys. Med. Biol. 44, 1791–1802 (1999)CrossRefGoogle Scholar
  77. 76.
    M. Costa etal., Diagnosis applications of non-crystalline diffraction of collagen fibres: breast cancer and skin diseases. Lect. Notes Phys. 776, 265–280 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Robert Moss
    • 1
  • Andrea Gutierrez
    • 1
  • Amany Amin
    • 2
    • 3
  • Chiaki Crews
    • 1
  • Robert Speller
    • 1
  • Francesco Iacoviello
    • 4
  • Paul Shearing
    • 4
  • Sarah Vinnicombe
    • 5
    • 6
  • Selina Kolokytha
    • 7
  1. 1.Department of Medical Physics & Biomedical EngineeringUniversity College LondonLondonUK
  2. 2.John Radcliffe HospitalOxfordUK
  3. 3.St Bartholomew’s HospitalLondonUK
  4. 4.Electrochemical Innovation Lab, Department of Chemical EngineeringUniversity College LondonLondonUK
  5. 5.The Breast Unit, CheltenhamGloucestershire Hospitals NHS Foundation TrustGloucesterUK
  6. 6.The University of DundeeDundeeUK
  7. 7.Empa, Centre for X-Ray AnalyticsSwiss Federal Laboratories for Materials Science and TechnologyDübendorfSwitzerland

Personalised recommendations