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PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques

  • Habib Zaidi
  • Issam El Naqa
Review Article

Abstract

Historically, anatomical CT and MR images were used to delineate the gross tumour volumes (GTVs) for radiotherapy treatment planning. The capabilities offered by modern radiation therapy units and the widespread availability of combined PET/CT scanners stimulated the development of biological PET imaging-guided radiation therapy treatment planning with the aim to produce highly conformal radiation dose distribution to the tumour. One of the most difficult issues facing PET-based treatment planning is the accurate delineation of target regions from typical blurred and noisy functional images. The major problems encountered are image segmentation and imperfect system response function. Image segmentation is defined as the process of classifying the voxels of an image into a set of distinct classes. The difficulty in PET image segmentation is compounded by the low spatial resolution and high noise characteristics of PET images. Despite the difficulties and known limitations, several image segmentation approaches have been proposed and used in the clinical setting including thresholding, edge detection, region growing, clustering, stochastic models, deformable models, classifiers and several other approaches. A detailed description of the various approaches proposed in the literature is reviewed. Moreover, we also briefly discuss some important considerations and limitations of the widely used techniques to guide practitioners in the field of radiation oncology. The strategies followed for validation and comparative assessment of various PET segmentation approaches are described. Future opportunities and the current challenges facing the adoption of PET-guided delineation of target volumes and its role in basic and clinical research are also addressed.

Keywords

PET Segmentation Radiation therapy Treatment planning Validation 

Notes

Acknowledgements

This work was supported by the Swiss National Science Foundation under grant SNSF 3152A0-102143 and the National Institutes of Health under grant 1K25CA128809-01A1.

References

  1. 1.
    Townsend DW. Multimodality imaging of structure and function. Phys Med Biol 2008;53:R1–39.PubMedCrossRefGoogle Scholar
  2. 2.
    Hasegawa B, Zaidi H. Dual-modality imaging: more than the sum of its components. In: Zaidi H, editor. Quantitative analysis in nuclear medicine imaging. New York: Springer; 2006. p. 35–81.CrossRefGoogle Scholar
  3. 3.
    Bernier J, Hall EJ, Giaccia A. Radiation oncology: a century of achievements. Nat Rev Cancer 2004;4:737–47.PubMedCrossRefGoogle Scholar
  4. 4.
    Fenwick JD, Tomé WA, Soisson ET, Mehta MP, Rock Mackie T. Tomotherapy and other innovative IMRT delivery systems. Semin Radiat Oncol 2006;16:199–208.PubMedCrossRefGoogle Scholar
  5. 5.
    Ling C, Zhang P, Archambault Y, Bocanek J, Tang G, Losasso T. Commissioning and quality assurance of RapidArc radiotherapy delivery system. Int J Radiat Oncol Biol Phys 2008;72:575–81.PubMedGoogle Scholar
  6. 6.
    Jäkel O, Karger CP, Debus J. The future of heavy ion radiotherapy. Med Phys 2008;35:5653–63.PubMedCrossRefGoogle Scholar
  7. 7.
    ICRU. Prescribing, recording and reporting photon beam therapy. ICRU Report 62. Washington: International Commission on Radiation Units and Measurements; 1999.Google Scholar
  8. 8.
    Austin-Seymour M, Chen GT, Rosenman J, Michalski J, Lindsley K, Goitein M. Tumor and target delineation: current research and future challenges. Int J Radiat Oncol Biol Phys 1995;33:1041–52.PubMedGoogle Scholar
  9. 9.
    Evans PM. Anatomical imaging for radiotherapy. Phys Med Biol 2008;53:R151–91.PubMedCrossRefGoogle Scholar
  10. 10.
    Papiez L, Langer M. On probabilistically defined margins in radiation therapy. Phys Med Biol 2006;51:3921–39.PubMedCrossRefGoogle Scholar
  11. 11.
    Khoo VS, Adams EJ, Saran F, Bedford JL, Perks JR, Warrington AP, et al. A comparison of clinical target volumes determined by CT and MRI for the radiotherapy planning of base of skull meningiomas. Int J Radiat Oncol Biol Phys 2000;46:1309–17.PubMedGoogle Scholar
  12. 12.
    Chaney E, Ibbott G, Hendee WR. Methods for image segmentation should be standardized and calibrated. Med Phys 2005;32:3507–10.PubMedCrossRefGoogle Scholar
  13. 13.
    Ling C, Humm J, Larson S, Amols H, Fuks Z, Leibel S, et al. Towards multidimensional radiotherapy (MD-CRT): biological imaging and biological conformality. Int J Radiat Oncol Biol Phys 2000;47:551–60.PubMedCrossRefGoogle Scholar
  14. 14.
    Zaidi H, Alavi A. Current trends in PET and combined (PET/CT and PET/MR) systems design. PET Clin 2007;2:109–23.CrossRefGoogle Scholar
  15. 15.
    Chapman JD, Bradley JD, Eary JF, Haubner R, Larson SM, Michalski JM, et al. Molecular (functional) imaging for radiotherapy applications: an RTOG symposium. Int J Radiat Oncol Biol Phys 2003;55:294–301.PubMedGoogle Scholar
  16. 16.
    Grégoire V, Haustermans K, Geets X, Roels S, Lonneux M. PET-based treatment planning in radiotherapy: a new standard? J Nucl Med 2007;48:68S–77.PubMedGoogle Scholar
  17. 17.
    Grosu AL, Piert M, Weber WA, Jeremic B, Picchio M, Schratzenstaller U, et al. Positron emission tomography for radiation treatment planning. Strahlenther Onkol 2005;181:483–99.PubMedCrossRefGoogle Scholar
  18. 18.
    Lecchi M, Fossati P, Elisei F, Orecchia R, Lucignani G. Current concepts on imaging in radiotherapy. Eur J Nucl Med Mol Imaging 2008;35:821–37.PubMedCrossRefGoogle Scholar
  19. 19.
    Mah D, Chen CC. Image guidance in radiation oncology treatment planning: the role of imaging technologies on the planning process. Semin Nucl Med 2008;38:114–8.PubMedCrossRefGoogle Scholar
  20. 20.
    Messa C, Di Muzio N, Picchio M, Gilardi MC, Bettinardi V, Fazio F. PET/CT and radiotherapy. Q J Nucl Med Mol Imaging 2006;50:4–14.PubMedGoogle Scholar
  21. 21.
    Zaidi H, Vees H, Wissmeyer M. Molecular PET/CT imaging-guided radiation therapy treatment planning. Acad Radiol 2009;16:1108–33.PubMedCrossRefGoogle Scholar
  22. 22.
    Olabarriaga SD, Smeulders AW. Interaction in the segmentation of medical images: a survey. Med Image Anal 2001;5:127–42.PubMedCrossRefGoogle Scholar
  23. 23.
    Udupa JK, Saha PK. Fuzzy connectedness and image segmentation. Proc IEEE 2003;91:1649–69.CrossRefGoogle Scholar
  24. 24.
    Boudraa A, Zaidi H. Image segmentation techniques in nuclear medicine imaging. In: Zaidi H, editor. Quantitative analysis of nuclear medicine images. New York: Springer; 2006. p. 308–57.CrossRefGoogle Scholar
  25. 25.
    Zaidi H. Medical image segmentation: quo vadis. Comput Methods Programs Biomed 2006;84:63–7.PubMedCrossRefGoogle Scholar
  26. 26.
    van Baardwijk A, Baumert BG, Bosmans G, van Kroonenburgh M, Stroobants S, Gregoire V, et al. The current status of FDG-PET in tumour volume definition in radiotherapy treatment planning. Cancer Treat Rev 2006;32:245–60.PubMedCrossRefGoogle Scholar
  27. 27.
    Greco C, Rosenzweig K, Cascini GL, Tamburrini O. Current status of PET/CT for tumour volume definition in radiotherapy treatment planning for non-small cell lung cancer (NSCLC). Lung Cancer 2007;57:125–34.PubMedCrossRefGoogle Scholar
  28. 28.
    Graves EE, Quon A, Loo Jr BW. RT_Image: an open-source tool for investigating PET in radiation oncology. Technol Cancer Res Treat 2007;6:111–21.PubMedGoogle Scholar
  29. 29.
    Ahn PH, Garg MK. Positron emission tomography/computed tomography for target delineation in head and neck cancers. Semin Nucl Med 2008;38:141–8.PubMedCrossRefGoogle Scholar
  30. 30.
    Rahn AN, Baum RP, Adamietz IA, Adams S, Sengupta S, Mose S, et al. Value of 18F fluorodeoxyglucose positron emission tomography in radiotherapy planning of head-neck tumors. Strahlenther Onkol 1998;174:358–64. German.PubMedCrossRefGoogle Scholar
  31. 31.
    Munley MT, Marks LB, Scarfone C, Sibley GS, Patz Jr EF, Turkington TG, et al. Multimodality nuclear medicine imaging in three-dimensional radiation treatment planning for lung cancer: challenges and prospects. Lung Cancer 1999;23:105–14.PubMedCrossRefGoogle Scholar
  32. 32.
    Gross MW, Weber WA, Feldmann HJ, Bartenstein P, Schwaiger M, Molls M. The value of F-18-fluorodeoxyglucose PET for the 3-D radiation treatment planning of malignant gliomas. Int J Radiat Oncol Biol Phys 1998;41:989–95.PubMedGoogle Scholar
  33. 33.
    Kiffer JD, Berlangieri SU, Scott AM, Quong G, Feigen M, Schumer W, et al. The contribution of 18F-fluoro-2-deoxy-glucose positron emission tomographic imaging to radiotherapy planning in lung cancer. Lung Cancer 1998;19:167–77.PubMedCrossRefGoogle Scholar
  34. 34.
    Scarfone C, Jaszczak RJ, Gilland DR, Greer KL, Munley MT, Marks LB, et al. Quantitative pulmonary single photon emission computed tomography for radiotherapy applications. Med Phys 1999;26:1579–88.PubMedCrossRefGoogle Scholar
  35. 35.
    Nestle U, Walter K, Schmidt S, Licht N, Nieder C, Motaref B, et al. 18F-deoxyglucose positron emission tomography (FDG-PET) for the planning of radiotherapy in lung cancer: high impact in patients with atelectasis. Int J Radiat Oncol Biol Phys 1999;44:593–7.PubMedCrossRefGoogle Scholar
  36. 36.
    Vanuytsel LJ, Vansteenkiste JF, Stroobants SG, De Leyn PR, De Wever W, Verbeken EK, et al. The impact of 18F-fluoro-2-deoxy–glucose positron emission tomography (FDG-PET) lymph node staging on the radiation treatment volumes in patients with non-small cell lung cancer. Radiother Oncol 2000;55:317–24.PubMedCrossRefGoogle Scholar
  37. 37.
    Levivier M, Wikier D, Goldman S, David P, Metens T, Massager N, et al. Integration of the metabolic data of positron emission tomography in the dosimetry planning of radiosurgery with the gamma knife: early experience with brain tumors. Technical note. J Neurosurg 2000;93 Suppl 3:233–8.PubMedGoogle Scholar
  38. 38.
    Mah K, Caldwell CB, Ung YC, Danjoux CE, Balogh JM, Ganguli SN, et al. The impact of (18)FDG-PET on target and critical organs in CT-based treatment planning of patients with poorly defined non-small-cell lung carcinoma: a prospective study. Int J Radiat Oncol Biol Phys 2002;52:339–50.PubMedGoogle Scholar
  39. 39.
    Paulino AC, Thorstad WL, Fox T. Role of fusion in radiotherapy treatment planning. Semin Nucl Med 2003;33:238–43.PubMedCrossRefGoogle Scholar
  40. 40.
    Scarfone C, Lavely WC, Cmelak AJ, Delbeke D, Martin WH, Billheimer D, et al. Prospective feasibility trial of radiotherapy target definition for head and neck cancer using 3-dimensional PET and CT imaging. J Nucl Med 2004;45:543–52.PubMedGoogle Scholar
  41. 41.
    Yap JT, Carney JP, Hall NC, Townsend DW. Image-guided cancer therapy using PET/CT. Cancer J 2004;10:221–33.PubMedCrossRefGoogle Scholar
  42. 42.
    Bradley JD, Perez CA, Dehdashti F, Siegel BA. Implementing biologic target volumes in radiation treatment planning for non-small cell lung cancer. J Nucl Med 2004;45 Suppl 1:96S–101.PubMedGoogle Scholar
  43. 43.
    Brunetti J, Caggiano A, Rosenbluth B, Vialotti C. Technical aspects of positron emission tomography/computed tomography fusion planning. Semin Nucl Med 2008;38:129–36.PubMedCrossRefGoogle Scholar
  44. 44.
    Pan T, Mawlawi O. PET/CT in radiation oncology. Med Phys 2008;35:4955–66.PubMedCrossRefGoogle Scholar
  45. 45.
    Nestle U, Weber W, Hentschel M, Grosu A-L. Biological imaging in radiation therapy: role of positron emission tomography. Phys Med Biol 2009;54:R1–25.PubMedCrossRefGoogle Scholar
  46. 46.
    Macapinlac HA. Clinical applications of positron emission tomography/computed tomography treatment planning. Semin Nucl Med 2008;38:137–40.PubMedCrossRefGoogle Scholar
  47. 47.
    Czernin J, Allen-Auerbach M, Schelbert HR. Improvements in cancer staging with PET/CT: literature-based evidence as of September 2006. J Nucl Med 2007;48:78S–88.PubMedGoogle Scholar
  48. 48.
    Bradley J, Thorstad WL, Mutic S, Miller TR, Dehdashti F, Siegel BA, et al. Impact of FDG-PET on radiation therapy volume delineation in non-small-cell lung cancer. Int J Radiat Oncol Biol Phys 2004;59:78–86.PubMedGoogle Scholar
  49. 49.
    Xing L, Siebers J, Keall P. Computational challenges for image-guided radiation therapy: framework and current research. Semin Radiat Oncol 2007;17:245–57.PubMedCrossRefGoogle Scholar
  50. 50.
    Stroom J, Blaauwgeers H, van Baardwijk A, Boersma L, Lebesque J, Theuws J, et al. Feasibility of pathology-correlated lung imaging for accurate target definition of lung tumors. Int J Radiat Oncol Biol Phys 2007;69:267–75.PubMedGoogle Scholar
  51. 51.
    Caldwell CB, Mah K, Skinner M, Danjoux CE. Can PET provide the 3D extent of tumor motion for individualized internal target volumes? A phantom study of the limitations of CT and the promise of PET. Int J Radiat Oncol Biol Phys 2003;55:1381–93.PubMedGoogle Scholar
  52. 52.
    Nestle U, Kremp S, Grosu AL. Practical integration of [18F]-FDG-PET and PET-CT in the planning of radiotherapy for non-small cell lung cancer (NSCLC): the technical basis, ICRU-target volumes, problems, perspectives. Radiother Oncol 2006;81:209–25.PubMedCrossRefGoogle Scholar
  53. 53.
    Grosu AL, Weber WA, Astner ST, Adam M, Krause BJ, Schwaiger M, et al. 11C-methionine PET improves the target volume delineation of meningiomas treated with stereotactic fractionated radiotherapy. Int J Radiat Oncol Biol Phys 2006;66:339–44.PubMedGoogle Scholar
  54. 54.
    Kalff V, Hicks RJ, MacManus MP, Binns DS, McKenzie AF, Ware RE, et al. Clinical impact of (18)F fluorodeoxyglucose positron emission tomography in patients with non-small-cell lung cancer: a prospective study. J Clin Oncol 2001;19:111–8.PubMedGoogle Scholar
  55. 55.
    Caldwell CB, Mah K, Ung YC, Danjoux CE, Balogh JM, Ganguli SN, et al. Observer variation in contouring gross tumor volume in patients with poorly defined non-small-cell lung tumors on CT: the impact of 18FDG-hybrid PET fusion. Int J Radiat Oncol Biol Phys 2001;51:923–31.PubMedGoogle Scholar
  56. 56.
    Fox JL, Rengan R, O’Meara W, Yorke E, Erdi Y, Nehmeh S, et al. Does registration of PET and planning CT images decrease interobserver and intraobserver variation in delineating tumor volumes for non-small-cell lung cancer? Int J Radiat Oncol Biol Phys 2005;62:70–5.PubMedCrossRefGoogle Scholar
  57. 57.
    van Baardwijk A, Bosmans G, Boersma L, Buijsen J, Wanders S, Hochstenbag M, et al. PET-CT-based auto-contouring in non-small-cell lung cancer correlates with pathology and reduces interobserver variability in the delineation of the primary tumor and involved nodal volumes. Int J Radiat Oncol Biol Phys 2007;68:771–8.PubMedGoogle Scholar
  58. 58.
    Steenbakkers RJHM, Duppen JC, Fitton I, Deurloo KEI, Zijp LJ, Comans EFI, et al. Reduction of observer variation using matched CT-PET for lung cancer delineation: a three-dimensional analysis. Int J Radiat Oncol Biol Phys 2006;64:435–48.PubMedGoogle Scholar
  59. 59.
    Sovik A, Malinen E, Olsen DR. Strategies for biologic image-guided dose escalation: a review. Int J Radiat Oncol Biol Phys 2009;73:650–8.PubMedGoogle Scholar
  60. 60.
    Basu S. Selecting the optimal image segmentation strategy in the era of multitracer multimodality imaging: a critical step for image-guided radiation therapy. Eur J Nucl Med Mol Imaging 2009;36:180–1.PubMedCrossRefGoogle Scholar
  61. 61.
    Soret M, Bacharach SL, Buvat I. Partial-volume effect in PET tumor imaging. J Nucl Med 2007;48:932–45.PubMedCrossRefGoogle Scholar
  62. 62.
    Rousset O, Rahmim A, Alavi A, Zaidi H. Partial volume correction strategies in PET. PET Clin 2007;2:235–49.CrossRefGoogle Scholar
  63. 63.
    Rahmim A, Rousset O, Zaidi H. Strategies for motion tracking and correction in PET. PET Clin 2007;2:251–66.CrossRefGoogle Scholar
  64. 64.
    Nehmeh SA, Erdi YE. Respiratory motion in positron emission tomography/computed tomography: a review. Semin Nucl Med 2008;38:167–76.PubMedCrossRefGoogle Scholar
  65. 65.
    Li T, Thorndyke B, Schreibmann E, Yang Y, Xing L. Model-based image reconstruction for four-dimensional PET. Med Phys 2006;33:1288–98.PubMedCrossRefGoogle Scholar
  66. 66.
    Qiao F, Pan T, Clark J, John W, Mawlawi O. Joint model of motion and anatomy for PET image reconstruction. Med Phys 2007;34:4626–39.PubMedCrossRefGoogle Scholar
  67. 67.
    Lamare F, Ledesma Carbayo MJ, Cresson T, Kontaxakis G, Santos A, Cheze Le Rest C, et al. List-mode-based reconstruction for respiratory motion correction in PET using non-rigid body transformations. Phys Med Biol 2007;52:5187–204.PubMedCrossRefGoogle Scholar
  68. 68.
    Rahmim A, Dinelle K, Cheng J-C, Shilov MA, Segars WP, Lidstone SC, et al. Accurate event-driven motion compensation in high-resolution PET incorporating scattered and random events. IEEE Trans Med Imaging 2008;27:1018–33.PubMedCrossRefGoogle Scholar
  69. 69.
    Büther F, Dawood M, Stegger L, Wübbeling F, Schäfers M, Schober O, et al. List mode-driven cardiac and respiratory gating in PET. J Nucl Med 2009;50:674–81.PubMedCrossRefGoogle Scholar
  70. 70.
    Rahmim A, Tang J, Zaidi H. Four-dimensional (4D) image reconstruction strategies in dynamic PET: beyond conventional independent frame reconstruction. Med Phys 2009;36:3654–70.PubMedCrossRefGoogle Scholar
  71. 71.
    Perez CA. Principles and practice of radiation oncology. 4th ed. Philadelphia: Lippincott Williams & Wilkins; 2004.Google Scholar
  72. 72.
    Otsu N. A thresholding selection method from gray-level histograms. IEEE Trans Syst Man Cybern 1979;9:62–6.CrossRefGoogle Scholar
  73. 73.
    Reddi SS, Rudin SF, Keshavan HR. An optimal multiple threshold scheme for image segmentation. IEEE Trans Syst Man Cybern 1984;14:661–5.Google Scholar
  74. 74.
    Kittler J, Illingworth J. Minimum error thresholding. Pattern Recognit 1986;19:41–7.CrossRefGoogle Scholar
  75. 75.
    Pal NR, Pal SK. A review on image segmentation techniques. Pattern Recognit 1993;26:1277–94.CrossRefGoogle Scholar
  76. 76.
    Huang S-C. Anatomy of SUV. Nucl Med Biol 2000;27:643–6.PubMedCrossRefGoogle Scholar
  77. 77.
    Keyes JW Jr. SUV: standard uptake value or silly useless value? J Nucl Med 1995;36:1836–9.PubMedGoogle Scholar
  78. 78.
    Basu S, Zaidi H, Houseni M, Udupa J, Acton P, Torigian D, et al. Novel quantitative techniques for assessing regional and global function and structure based on modern imaging modalities: implications for normal variation, aging and diseased states. Semin Nucl Med 2007;37:223–39.PubMedCrossRefGoogle Scholar
  79. 79.
    Boellaard R. Standards for PET image acquisition and quantitative data analysis. J Nucl Med 2009;50:11S–20.PubMedCrossRefGoogle Scholar
  80. 80.
    Erdi YE, Mawlawi O, Larson SM, Imbriaco M, Yeung H, Finn R, et al. Segmentation of lung lesion volume by adaptive positron emission tomography image thresholding. Cancer 1997;80:2505–9.PubMedCrossRefGoogle Scholar
  81. 81.
    Miller TR, Grigsby PW. Measurement of tumor volume by PET to evaluate prognosis in patients with advanced cervical cancer treated by radiation therapy. Int J Radiat Oncol Biol Phys 2002;53:353–9.PubMedGoogle Scholar
  82. 82.
    Biehl KJ, Kong FM, Dehdashti F, Jin JY, Mutic S, El Naqa I, et al. 18F-FDG PET definition of gross tumor volume for radiotherapy of non-small cell lung cancer: is a single standardized uptake value threshold approach appropriate? J Nucl Med 2006;47:1808–12.PubMedGoogle Scholar
  83. 83.
    Ford EC, Kinahan PE, Hanlon L, Alessio A, Rajendran J, Schwartz DL, et al. Tumor delineation using PET in head and neck cancers: threshold contouring and lesion volumes. Med Phys 2006;33:4280–8.PubMedCrossRefGoogle Scholar
  84. 84.
    Zaidi H. Organ volume estimation using SPECT. IEEE Trans Nucl Sci 1996;43:2174–82.CrossRefGoogle Scholar
  85. 85.
    Yaremko B, Riauka T, Robinson D, Murray B, Alexander A, McEwan A, et al. Thresholding in PET images of static and moving targets. Phys Med Biol 2005;50:5969–82.PubMedCrossRefGoogle Scholar
  86. 86.
    Paulino AC, Koshy M, Howell R, Schuster D, Davis LW. Comparison of CT- and FDG-PET-defined gross tumor volume in intensity-modulated radiotherapy for head-and-neck cancer. Int J Radiat Oncol Biol Phys 2005;61:1385–92.PubMedGoogle Scholar
  87. 87.
    Schinagl DA, Vogel WV, Hoffmann AL, van Dalen JA, Oyen WJ, Kaanders JH. Comparison of five segmentation tools for 18F-fluoro-deoxy-glucose-positron emission tomography-based target volume definition in head and neck cancer. Int J Radiat Oncol Biol Phys 2007;69:1282–9.PubMedGoogle Scholar
  88. 88.
    Vees H, Senthamizhchelvan S, Miralbell R, Weber D, Ratib O, Zaidi H. Assessment of various strategies for 18F-FET PET-guided delineation of target volumes in high-grade glioma patients. Eur J Nucl Med Mol Imaging 2009;36:182–93.PubMedCrossRefGoogle Scholar
  89. 89.
    Hatt M, Cheze le Rest C, Turzo A, Roux C, Visvikis D. A fuzzy locally adaptive Bayesian segmentation approach for volume determination in PET. IEEE Trans Med Imaging 2009;28:881–93.PubMedCrossRefGoogle Scholar
  90. 90.
    Black QC, Grills IS, Kestin LL, Wong CY, Wong JW, Martinez AA, et al. Defining a radiotherapy target with positron emission tomography. Int J Radiat Oncol Biol Phys 2004;60:1272–82.PubMedGoogle Scholar
  91. 91.
    Daisne JF, Sibomana M, Bol A, Doumont T, Lonneux M, Grégoire V. Tri-dimensional automatic segmentation of PET volumes based on measured source-to-background ratios: influence of reconstruction algorithms. Radiother Oncol 2003;69:247–50.PubMedCrossRefGoogle Scholar
  92. 92.
    Brambilla M, Matheoud R, Secco C, Loi G, Krengli M, Inglese E. Threshold segmentation for PET target volume delineation in radiation treatment planning: the role of target-to-background ratio and target size. Med Phys 2008;35:1207–13.PubMedCrossRefGoogle Scholar
  93. 93.
    Drever L, Robinson DM, McEwan A, Roa W. A local contrast based approach to threshold segmentation for PET target volume delineation. Med Phys 2006;33:1583–94.PubMedCrossRefGoogle Scholar
  94. 94.
    Nestle U, Kremp S, Schaefer-Schuler A, Sebastian-Welsch C, Hellwig D, Rübe C, et al. Comparison of different methods for delineation of 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-small cell lung cancer. J Nucl Med 2005;46:1342–8.PubMedGoogle Scholar
  95. 95.
    Schaefer A, Kremp S, Hellwig D, Rübe C, Kirsch C-M, Nestle U. A contrast-oriented algorithm for FDG-PET-based delineation of tumour volumes for the radiotherapy of lung cancer: derivation from phantom measurements and validation in patient data. Eur J Nucl Med Mol Imaging 2008;35:1989–99.PubMedCrossRefGoogle Scholar
  96. 96.
    Jentzen W, Freudenberg L, Eising EG, Heinze M, Brandau W, Bockisch A. Segmentation of PET volumes by iterative image thresholding. J Nucl Med 2007;48:108–14.PubMedGoogle Scholar
  97. 97.
    Nehmeh SA, El-Zeftawy H, Greco C, Schwartz J, Erdi YE, Kirov A, et al. An iterative technique to segment PET lesions using a Monte Carlo based mathematical model. Med Phys 2009;36:4803–9.PubMedCrossRefGoogle Scholar
  98. 98.
    Greco C, Nehmeh SA, Schöder H, Gönen M, Raphael B, Stambuk HE, et al. Evaluation of different methods of 18F-FDG-PET target volume delineation in the radiotherapy of head and neck cancer. Am J Clin Oncol 2008;31:439–45.PubMedCrossRefGoogle Scholar
  99. 99.
    Marr D, Hildreth E. Theory of edge detection. Proc R Soc Lond B Biol Sci 1980;207:187–217.PubMedCrossRefGoogle Scholar
  100. 100.
    Huertas A, Medioni G. Detection of intensity changes with subpixel accuracy using Laplacian-Gaussian masks. IEEE Trans Pattern Anal Mach Intell 1986;8:651–64.CrossRefGoogle Scholar
  101. 101.
    Canny JF. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 1986;8:679–98.CrossRefGoogle Scholar
  102. 102.
    Drever LA, Roa W, McEwan A, Robinson D. Comparison of three image segmentation techniques for target volume delineation in positron emission tomography. J Appl Clin Med Phys 2007;8:93–109.PubMedCrossRefGoogle Scholar
  103. 103.
    Geets X, Lee J, Bol A, Lonneux M, Grégoire V. A gradient-based method for segmenting FDG-PET images: methodology and validation. Eur J Nucl Med Mol Imaging 2007;34:1427–38.PubMedCrossRefGoogle Scholar
  104. 104.
    El Naqa I, Bradley J, Deasy J, Biehl K, Laforest R, Low D. Improved analysis of PET images for radiation therapy. 14th International Conference on the Use of Computers in Radiation Therapy. Seoul, Korea; 2004. pp 361–63.Google Scholar
  105. 105.
    Hsu C-Y, Liu C-Y, Chen C-M. Automatic segmentation of liver PET images. Comput Med Imaging Graph 2008;32:601–10.PubMedCrossRefGoogle Scholar
  106. 106.
    Li H, Thorstad WL, Biehl KJ, Laforest R, Su Y, Shoghi KI, et al. A novel PET tumor delineation method based on adaptive region-growing and dual-front active contours. Med Phys 2008;35:3711–21. Erratum. pp 5958.PubMedCrossRefGoogle Scholar
  107. 107.
    Sethian JA. Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and material science. 2nd ed. Cambridge: Cambridge University Press; 1999.Google Scholar
  108. 108.
    Xu C, Pham DL, Prince JL. Image segmentation using deformable models. In: Sonka M, Fitzpatrick JM, editors. Handbook of medical imaging: medical image processing and analysis. Bellingham: SPIE Press; 2002. pp. 129–74.Google Scholar
  109. 109.
    Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. Int J Comput Vis 1988;1:321–31.CrossRefGoogle Scholar
  110. 110.
    Kass M, Witkin A, Terzopoulos. Snakes: active contour models. First International Conference on Computer Vision. London; 1987. pp. 259–68.Google Scholar
  111. 111.
    Liang J, McInerney T, Terzopoulos D. United snakes. Med Image Anal 2006;10:215–33.PubMedCrossRefGoogle Scholar
  112. 112.
    Xu C, Prince JL. Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 1998;7:359–69.PubMedCrossRefGoogle Scholar
  113. 113.
    Osher S, Sethian JA. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J Comput Phys 1988;79:12–49.CrossRefGoogle Scholar
  114. 114.
    Duda RO, Hart PE, Stork DG. Pattern classification. 2nd ed. New York: Wiley; 2001.Google Scholar
  115. 115.
    Jain AK, Duin RPW, Mao J. Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 2000;22:4–37.CrossRefGoogle Scholar
  116. 116.
    Clarke LP, Velthuizen RP, Phuphanich S, Schellenberg JD, Arrington JA, Silbiger M. MRI: stability of three supervised segmentation techniques. Magn Reson Imaging 1993;11:95–106.PubMedCrossRefGoogle Scholar
  117. 117.
    Vaidyanathan M, Clarke LP, Velthuizen RP, Phuphanich S, Bensaid AM, Hall LO, et al. Comparison of supervised MRI segmentation methods for tumor volume determination during therapy. Magn Reson Imaging 1995;13:719–28.PubMedCrossRefGoogle Scholar
  118. 118.
    Suri JS, Singh S, Reden L. Computer vision and pattern recognition techniques for 2-D and 3-D MR cerebral cortical segmentation (part I): a state-of-the-art review. Pattern Anal Appl 2002;5:46–76.CrossRefGoogle Scholar
  119. 119.
    El Naqa I, Yang Y. Techniques in the detection of microcalcification (MC) clusters in digital mammograms. In: Leondes T, editor. Medical imaging systems: technology and applications. Singapore: World Scientific Publishing Co. Pte. Ltd.; 2005. pp. 15–36.Google Scholar
  120. 120.
    Boudraa AE, Champier J, Cinotti L, Bordet JC, Lavenne F, Mallet JJ. Delineation and quantitation of brain lesions by fuzzy clustering in positron emission tomography. Comput Med Imaging Graph 1996;20:31–41.PubMedCrossRefGoogle Scholar
  121. 121.
    Zhu W, Jiang T. Automation segmentation of PET image for brain tumors. IEEE Nucl Sci Symp Conf Rec 2003;4:2627–29.Google Scholar
  122. 122.
    Kim J, Wen L, Eberl S, Fulton R, Feng DD. Use of anatomical priors in the segmentation of PET lung tumor images. IEEE Nucl Sci Symp Conf Rec 2007;4:4242–45.Google Scholar
  123. 123.
    Belhassen S and Zaidi H. A novel fuzzy C-means algorithm for unsupervised heterogeneous tumor quantification in PET. Med Phys 2010;37:1309–1324.Google Scholar
  124. 124.
    Zaidi H, Diaz-Gomez M, Boudraa AO, Slosman DO. Fuzzy clustering-based segmented attenuation correction in whole-body PET imaging. Phys Med Biol 2002;47:1143–60.PubMedCrossRefGoogle Scholar
  125. 125.
    Acton PD, Pilowsky LS, Kung HF, Ell PJ. Automatic segmentation of dynamic neuroreceptor single-photon emission tomography images using fuzzy clustering. Eur J Nucl Med 1999;26:581–90.PubMedCrossRefGoogle Scholar
  126. 126.
    Bezdek JC, Hall LO, Clark MC, Goldgof DB, Clarke LP. Medical image analysis with fuzzy models. Stat Methods Med Res 1997;6:191–214.PubMedCrossRefGoogle Scholar
  127. 127.
    Jain AK, Murty MN, Flynn PJ. Data clustering: a review. ACM Comput Surv 1999;31:264–323.CrossRefGoogle Scholar
  128. 128.
    De Luca A, Termini S. A definition of non-probabilistic entropy in the setting of fuzzy sets theory. Inform Control 1972;20:301–12.CrossRefGoogle Scholar
  129. 129.
    Hall LO, Bensaid AM, Clarke LP, Velthuizen RP, Silbiger MS, Bezdek JC. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Trans Neural Netw 1992;3:672–82.PubMedCrossRefGoogle Scholar
  130. 130.
    Pham DL, Prince JL. An adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities. Pattern Recognit Lett 1999;20:57–68.CrossRefGoogle Scholar
  131. 131.
    Janssen MH, Aerts HJ, Ollers MC, Bosmans G, Lee JA, Buijsen J, et al. Tumor delineation based on time-activity curve differences assessed with dynamic fluorodeoxyglucose positron emission tomography-computed tomography in rectal cancer patients. Int J Radiat Oncol Biol Phys 2009;73:456–65.PubMedGoogle Scholar
  132. 132.
    Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 1990;12:629–39.CrossRefGoogle Scholar
  133. 133.
    Montgomery D, Amira A, Zaidi H. Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model. Med Phys 2007;34:722–36.PubMedCrossRefGoogle Scholar
  134. 134.
    Aristophanous M, Penney BC, Martel MK, Pelizzari CA. A Gaussian mixture model for definition of lung tumor volumes in positron emission tomography. Med Phys 2007;34:4223–35.PubMedCrossRefGoogle Scholar
  135. 135.
    Van Leemput K, Maes F, Vandermeulen D, Suetens P. Automated model-based tissue classification of MR images of the brain. IEEE Trans Med Imaging 1999;18:897–908.PubMedCrossRefGoogle Scholar
  136. 136.
    Ashburner J, Friston KJ. Unified segmentation. Neuroimage 2005;26:839–51.PubMedCrossRefGoogle Scholar
  137. 137.
    Hatt M, Lamare F, Boussion N, Turzo A, Collet C, Salzenstein F, et al. Fuzzy hidden Markov chains segmentation for volume determination and quantitation in PET. Phys Med Biol 2007;52:3467–91.PubMedCrossRefGoogle Scholar
  138. 138.
    Salzenstein F, Pieczynski W. Parameter estimation in hidden fuzzy Markovian fields and image segmentation. Graph Models Image Process 1997;59:205–20.CrossRefGoogle Scholar
  139. 139.
    Long DT, King MA, Sheehan J. Comparative evaluation of image segmentation methods for volume quantitation in SPECT. Med Phys 1992;19:483–9.PubMedCrossRefGoogle Scholar
  140. 140.
    Chan TF, Vese LA. Active contours without edges. IEEE Trans Image Process 2001;10:266–77.PubMedCrossRefGoogle Scholar
  141. 141.
    Guido A, Fuccio L, Rombi B, Castellucci P, Cecconi A, Bunkheila F, et al. Combined (18)F-FDG-PET/CT imaging in radiotherapy target delineation for head-and-neck cancer. Int J Radiat Oncol Biol Phys 2009;73:759–63.PubMedGoogle Scholar
  142. 142.
    Ciernik IF, Dizendorf E, Baumert BG, Reiner B, Burger C, Davis JB, et al. Radiation treatment planning with an integrated positron emission and computer tomography (PET/CT): a feasibility study. Int J Radiat Oncol Biol Phys 2003;57:853–63.PubMedGoogle Scholar
  143. 143.
    El Naqa I, Yang D, Apte A, Khullar D, Mutic S, Zheng J, et al. Concurrent multimodality image segmentation by active contours for radiotherapy treatment planning. Med Phys 2007;34:4738–49.PubMedCrossRefGoogle Scholar
  144. 144.
    Jannin P, Krupinski E, Warfield S. Validation in medical image processing. IEEE Trans Med Imaging 2006;25:1405–9.PubMedCrossRefGoogle Scholar
  145. 145.
    Slomka P, Baum R. Multimodality image registration with software: state-of-the-art. Eur J Nucl Med Mol Imaging 2009;36:S44–55.PubMedCrossRefGoogle Scholar
  146. 146.
    Fiorino C, Reni M, Bolognesi A, Cattaneo GM, Calandrino R. Intra- and inter-observer variability in contouring prostate and seminal vesicles: implications for conformal treatment planning. Radiother Oncol 1998;47:285–92.PubMedCrossRefGoogle Scholar
  147. 147.
    Giraud P, Elles S, Helfre S, De Rycke Y, Servois V, Carette MF, et al. Conformal radiotherapy for lung cancer: different delineation of the gross tumor volume (GTV) by radiologists and radiation oncologists. Radiother Oncol 2002;62:27–36.PubMedCrossRefGoogle Scholar
  148. 148.
    Belhassen S, Llina Fuentes CS, Dekker A, De Ruysscher D, Ratib O, Zaidi H. Comparative methods for 18F-FDG PET-based delineation of target volumes in non-small-cell lung cancer [abstract]. J Nucl Med 2009;50:27P.Google Scholar
  149. 149.
    Boucher L, Rodrigue S, Lecomte R, Bénard F. Respiratory gating for 3-dimensional PET of the thorax: feasibility and initial results. J Nucl Med 2004;45:214–9.PubMedGoogle Scholar
  150. 150.
    El Naqa I, Low DA, Bradley JD, Vicic M, Deasy JO. Deblurring of breathing motion artifacts in thoracic PET images by deconvolution methods. Med Phys 2006;33:3587–600.PubMedCrossRefGoogle Scholar
  151. 151.
    Turkington TG, Degrado TR, Sampson WH. Small spheres for lesion detection phantoms. IEEE Nucl Sci Symp Conf Rec 2001;4:2234–37.Google Scholar
  152. 152.
    Bazañez-Borgert M, Bundschuh RA, Herz M, Martínez MJ, Schwaiger M, Ziegler SI. Radioactive spheres without inactive wall for lesion simulation in PET. Z Med Phys 2008;18:37–42.PubMedGoogle Scholar
  153. 153.
    Zaidi H, Xu XG. Computational anthropomorphic models of the human anatomy: the path to realistic Monte Carlo modeling in radiological sciences. Annu Rev Biomed Eng 2007;9:471–500.PubMedCrossRefGoogle Scholar
  154. 154.
    Zaidi H, Tsui BMW. Review of computational anthropomorphic anatomical and physiological models. Proc IEEE 2009;97:1938–53.CrossRefGoogle Scholar
  155. 155.
    Segars WP. Development and application of the new dynamic NURBS-based cardiac-torso (NCAT) phantom [PhD Thesis]: University of North Carolina, Chapel Hill, NC, USA; 2001.Google Scholar
  156. 156.
    Piegl L, Tiller W. The NURBS book. New York: Springer; 1997.Google Scholar
  157. 157.
    Segars WP, Tsui BMW. MCAT to XCAT: the evolution of 4D computerized phantoms for imaging research. Proc IEEE 2009;97:1954–68.CrossRefGoogle Scholar
  158. 158.
    Aristophanous M, Penney BC, Pelizzari CA. The development and testing of a digital PET phantom for the evaluation of tumor volume segmentation techniques. Med Phys 2008;35:3331–42.PubMedCrossRefGoogle Scholar
  159. 159.
    Tomei S, Reilhac A, Visvikis D, Odet C, Giammarile F, Mognetti T, et al. Development of a database of realistic simulated whole body 18F-FDG images for lymphoma. Proc IEEE Nuclear Science Symposium and Medical Imaging Conference. Dresden, Germany: IEEE; 2008. pp. 4958–63.Google Scholar
  160. 160.
    Le Maitre A, Segars WP, Marache S, Reilhac A, Hatt M, Tomei S, et al. Incorporating patient specific variability in the simulation of realistic whole body 18F-FDG distributions for oncology applications. Proc IEEE 2009;97:2026–38.CrossRefGoogle Scholar
  161. 161.
    Zaidi H, Herrmann Scheurer A, Morel C. An object-oriented Monte Carlo simulator for 3D positron tomographs. Comput Methods Programs Biomed 1999;58:133–45.PubMedCrossRefGoogle Scholar
  162. 162.
    Jan S, Santin G, Strul D, Staelens S, Assie K, Autret D, et al. GATE: a simulation toolkit for PET and SPECT. Phys Med Biol 2004;49:4543–61.PubMedCrossRefGoogle Scholar
  163. 163.
    Harrison RL, Vannoy SD, Haynor DR, Gillispie SB, Kaplan MS, Lewellen TK. Preliminary experience with the photon history generator module for a public-domain simulation system for emission tomography. Records of IEEE Nuclear Science Symposium and Medical Imaging Conference; 1993. pp. 1154–58.Google Scholar
  164. 164.
    Ay M, Zaidi H. Development and validation of MCNP4C-based Monte Carlo simulator for fan- and cone-beam x-ray CT. Phys Med Biol 2005;50:4863–85.PubMedCrossRefGoogle Scholar
  165. 165.
    Kyriakou Y, Riedel T, Kalender WA. Combining deterministic and Monte Carlo calculations for fast estimation of scatter intensities in CT. Phys Med Biol 2006;51:4567–86.PubMedCrossRefGoogle Scholar
  166. 166.
    Malusek A, Sandborg M, Carlsson GA. CTmod-A toolkit for Monte Carlo simulation of projections including scatter in computed tomography. Comput Methods Programs Biomed 2008;90:167–78.PubMedCrossRefGoogle Scholar
  167. 167.
    Ay M, Zaidi H. Assessment of errors caused by X-ray scatter and use of contrast medium when using CT-based attenuation correction in PET. Eur J Nucl Med Mol Imaging 2006;33:1301–13.PubMedCrossRefGoogle Scholar
  168. 168.
    Zhang YJ. A survey on evaluation methods for image segmentation. Pattern Recognit Lett 1996;29:1335–46.Google Scholar
  169. 169.
    Zou KH, Warfield SK, Bharatha A, Tempany CM, Kaus MR, Haker SJ, et al. Statistical validation of image segmentation quality based on a spatial overlap index. Acad Radiol 2004;11:178–89.PubMedCrossRefGoogle Scholar
  170. 170.
    Edwards PJ, Nijmeh AD, McGurk M, Odell E, Fenlon MR, Marsden PK, et al. Validation of PET imaging by alignment to histology slices. Int Conf Med Image Comput Comput Assist Interv 2005;8:968–75.Google Scholar
  171. 171.
    Daisne JF, Duprez T, Weynand B, Lonneux M, Hamoir M, Reychler H, et al. Tumor volume in pharyngolaryngeal squamous cell carcinoma: comparison at CT, MR imaging, and FDG PET and validation with surgical specimen. Radiology 2004;233:93–100.PubMedCrossRefGoogle Scholar
  172. 172.
    Mamede M, Abreu ELP, Oliva MR, Nosé V, Mamon H, Gerbaudo VH. FDG-PET/CT tumor segmentation-derived indices of metabolic activity to assess response to neoadjuvant therapy and progression-free survival in esophageal cancer: correlation with histopathology results. Am J Clin Oncol 2007;30:377–88.PubMedCrossRefGoogle Scholar
  173. 173.
    Burri RJ, Rangaswamy B, Kostakoglu L, Hoch B, Genden EM, Som PM, et al. Correlation of positron emission tomography standard uptake value and pathologic specimen size in cancer of the head and neck. Int J Radiat Oncol Biol Phys 2008;71:682–8.PubMedGoogle Scholar
  174. 174.
    Venel Y, Garhi H, de Muret A, Baulieu J-L, Barillot I, Prunier-Aesch C. Comparaison de six méthodes de segmentation du volume tumoral sur la 18F-FDG TEP-TDM avec le volume de référence anatomopathologique dans les cancers bronchopulmonaires non à petites cellules. Médecine Nucléaire 2008;32:339–53.CrossRefGoogle Scholar
  175. 175.
    Seitz O, Chambron-Pinho N, Middendorp M, Sader R, Mack M, Vogl TJ, et al. 18F-Fluorodeoxyglucose-PET/CT to evaluate tumor, nodal disease, and gross tumor volume of oropharyngeal and oral cavity cancer: comparison with MR imaging and validation with surgical specimen. Neuroradiology 2009;51:677–86.PubMedCrossRefGoogle Scholar
  176. 176.
    Yu J, Li X, Xing L, Mu D, Fu Z, Sun X, et al. Comparison of tumor volumes as determined by pathologic examination and FDG-PET/CT images of non-small-cell lung cancer: a pilot study. Int J Radiat Oncol Biol Phys 2009;75:1468–74.PubMedGoogle Scholar
  177. 177.
    Yu HM, Liu YF, Hou M, Liu J, Li XN, Yu JM. Evaluation of gross tumor size using CT, (18)F-FDG PET, integrated (18)F-FDG PET/CT and pathological analysis in non-small cell lung cancer. Eur J Radiol 2009;75:1468–74.Google Scholar
  178. 178.
    Dahele M, Hwang D, Peressotti C, Sun L, Kusano M, Okhai S, et al. Developing a methodology for three-dimensional correlation of PET-CT images and whole-mount histopathology in non-small-cell lung cancer. Curr Oncol 2008;15:62–9.PubMedCrossRefGoogle Scholar
  179. 179.
    Christian N, Lee JA, Bol A, De Bast M, Jordan B, Grégoire V. The limitation of PET imaging for biological adaptive-IMRT assessed in animal models. Radiother Oncol 2009;91:101–16.PubMedCrossRefGoogle Scholar
  180. 180.
    Geets X, Daisne JF, Gregoire V, Hamoir M, Lonneux M. Role of 11-C-methionine positron emission tomography for the delineation of the tumor volume in pharyngo-laryngeal squamous cell carcinoma: comparison with FDG-PET and CT. Radiother Oncol 2004;71:267–73.PubMedCrossRefGoogle Scholar
  181. 181.
    Topkan E, Yavuz AA, Aydin M, Onal C, Yapar F, Yavuz MN. Comparison of CT and PET-CT based planning of radiation therapy in locally advanced pancreatic carcinoma. J Exp Clin Cancer Res 2008;27:41.PubMedCrossRefGoogle Scholar
  182. 182.
    Ford EC, Lavely WC, Frassica DA, Myers LT, Asrari F, Wahl RL, et al. Comparison of FDG-PET/CT and CT for delineation of lumpectomy cavity for partial breast irradiation. Int J Radiat Oncol Biol Phys 2008;71:595–602.PubMedGoogle Scholar
  183. 183.
    Visser EP, Philippens MEP, Kienhorst L, Kaanders JHAM, Corstens FHM, de Geus-Oei L-F, et al. Comparison of tumor volumes derived from glucose metabolic rate maps and SUV maps in dynamic 18F-FDG PET. J Nucl Med 2008;49:892–8.PubMedCrossRefGoogle Scholar
  184. 184.
    Grgic A, Nestle U, Schaefer-Schuler A, Kremp S, Kirsch CM, Hellwig D. FDG-PET-based radiotherapy planning in lung cancer: optimum breathing protocol and patient positioning—an intraindividual comparison. Int J Radiat Oncol Biol Phys 2009;73:103–11.PubMedGoogle Scholar
  185. 185.
    Zou KH, Wells WM, Kikinis R, Warfield SK. Three validation metrics for automated probabilistic image segmentation of brain tumours. Stat Med 2004;23:1259–82.PubMedCrossRefGoogle Scholar
  186. 186.
    Hatt M, Cheze le Rest C, Descourt P, Dekker A, De Ruysscher D, Oellers M, et al. Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications. Int J Radiat Oncol Biol Phys 2010: in press.Google Scholar
  187. 187.
    Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1:307–10.PubMedGoogle Scholar
  188. 188.
    Swensson RG. Unified measurement of observer performance in detecting and localizing target objects on images. Med Phys 1996;23:1709–25.PubMedCrossRefGoogle Scholar
  189. 189.
    Zou KH, Warfield SK, Fielding JR, Tempany CM, William MW, Kaus MR, et al. Statistical validation based on parametric receiver operating characteristic analysis of continuous classification data. Acad Radiol 2003;10:1359–68.PubMedCrossRefGoogle Scholar
  190. 190.
    Henkelman RM, Kay I, Bronskill MJ. Receiver operator characteristic (ROC) analysis without truth. Med Decis Making 1990;10:24–9.PubMedCrossRefGoogle Scholar
  191. 191.
    Beiden SV, Campbell G, Meier KL, Wagner RF. On the problem of ROC analysis without truth: the EM algorithm and the information matrix. Proc SPIE 2000;3981:126–34.CrossRefGoogle Scholar
  192. 192.
    Hoppin JW, Kupinski MA, Kastis GA, Clarkson E, Barrett HH. Objective comparison of quantitative imaging modalities without the use of a gold standard. IEEE Trans Med Imaging 2002;21:441–9.PubMedCrossRefGoogle Scholar
  193. 193.
    Kupinski MA, Hoppin JW, Clarkson E, Barrett HH, Kastis GA. Estimation in medical imaging without a gold standard. Acad Radiol 2002;9:290–7.PubMedCrossRefGoogle Scholar
  194. 194.
    Hoppin JW, Kupinski MA, Wilson DW, Peterson T, Gershman B, Kastis G, et al. Evaluating estimation techniques in medical imaging without a gold standard: experimental validation. Proc SPIE 2003;5034:230–7.CrossRefGoogle Scholar
  195. 195.
    Zaidi H, Ruest T, Schoenahl F, Montandon M-L. Comparative evaluation of statistical brain MR image segmentation algorithms and their impact on partial volume effect correction in PET. Neuroimage 2006;32:1591–607.PubMedCrossRefGoogle Scholar
  196. 196.
    Maes F, Vandermeulen D, Suetens P. Medical image registration using mutual information. Proc IEEE 2003;91:1699–722.CrossRefGoogle Scholar
  197. 197.
    Viola P. Alignment by maximization of mutual information. [PhD Thesis]. Massachusetts Institute of Technology; Cambridge, 1995.Google Scholar
  198. 198.
    Holden M, Hill DL, Denton ER, Jarosz JM, Cox TC, Rohlfing T, et al. Voxel similarity measures for 3-D serial MR brain image registration. IEEE Trans Med Imaging 2000;19:94–102.PubMedCrossRefGoogle Scholar
  199. 199.
    Aerts HJ, Bosmans G, van Baardwijk AA, Dekker AL, Oellers MC, Lambin P, et al. Stability of (18)F-deoxyglucose uptake locations within tumor during radiotherapy for NSCLC: a prospective study. Int J Radiat Oncol Biol Phys 2008;71:1402–7.PubMedGoogle Scholar
  200. 200.
    Kumar R, Dhanpathi H, Basu S, Rubello D, Fanti S, Alavi A. Oncologic PET tracers beyond [(18)F]FDG and the novel quantitative approaches in PET imaging. Q J Nucl Med Mol Imaging 2008;52:50–65.PubMedGoogle Scholar
  201. 201.
    Lewis JS, Welch MJ, Tang L. Workshop on the production, application and clinical translation of “non-standard” PET nuclides: a meeting report. Q J Nucl Med Mol Imaging 2008;52:101–6.PubMedGoogle Scholar
  202. 202.
    Bading JR, Shields AF. Imaging of cell proliferation: status and prospects. J Nucl Med 2008;49 Suppl 2:64S–80.PubMedCrossRefGoogle Scholar
  203. 203.
    Dunphy MPS, Lewis JS. Radiopharmaceuticals in preclinical and clinical development for monitoring of therapy with PET. J Nucl Med 2009;50:106S–21.PubMedCrossRefGoogle Scholar
  204. 204.
    Koch CJ, Evans SM. Non-invasive PET and SPECT imaging of tissue hypoxia using isotopically labeled 2-nitroimidazoles. Adv Exp Med Biol 2003;510:285–92.PubMedGoogle Scholar
  205. 205.
    Grosu AL, Souvatzoglou M, Röper B, Dobritz M, Wiedenmann N, Jacob V, et al. Hypoxia imaging with FAZA-PET and theoretical considerations with regard to dose painting for individualization of radiotherapy in patients with head and neck cancer. Int J Radiat Oncol Biol Phys 2007;69:541–51.PubMedGoogle Scholar
  206. 206.
    Jager PL, Chirakal R, Marriott CJ, Brouwers AH, Koopmans KP, Gulenchyn KY. 6-L-18F-fluorodihydroxyphenylalanine PET in neuroendocrine tumors: basic aspects and emerging clinical applications. J Nucl Med 2008;49:573–86.PubMedCrossRefGoogle Scholar
  207. 207.
    Tang BN, Van Simaeys G, Devriendt D, Sadeghi N, Dewitte O, Massager N, et al. Three-dimensional Gaussian model to define brain metastasis limits on (11)C-methionine PET. Radiother Oncol 2008;89:270–7.PubMedCrossRefGoogle Scholar
  208. 208.
    Ciernik IF, Brown DW, Schmid D, Hany T, Egli P, Davis JB. 3D-segmentation of the 18F-choline PET signal for target volume definition in radiation therapy of the prostate. Technol Cancer Res Treat 2007;6:23–30.PubMedGoogle Scholar
  209. 209.
    Wang H, Vees H, Miralbell R, Wissmeyer M, Steiner C, Ratib O, et al. (18)F-fluorocholine PET-guided target volume delineation techniques for partial prostate re-irradiation in local recurrent prostate cancer. Radiother Oncol 2009;93:220–5.PubMedCrossRefGoogle Scholar
  210. 210.
    Weber D, Wang H, Cozzi L, Dipasquale G, Khan H, Ratib O, et al. RapidArc, intensity modulated photon and proton techniques for recurrent prostate cancer in previously irradiated patients: a treatment planning comparison study. Radiat Oncol 2009;4:34.PubMedCrossRefGoogle Scholar
  211. 211.
    Patel DA, Chang ST, Goodman KA, Quon A, Thorndyke B, Gambhir SS, et al. Impact of integrated PET/CT on variability of target volume delineation in rectal cancer. Technol Cancer Res Treat 2007;6:31–6.PubMedGoogle Scholar
  212. 212.
    Weber DC, Zilli T, Buchegger F, Casanova N, Haller G, Rouzaud M, et al. [(18)F]Fluoroethyltyrosine-positron emission tomography-guided radiotherapy for high-grade glioma. Radiat Oncol 2008;3:44.PubMedCrossRefGoogle Scholar
  213. 213.
    Zaidi H, Mawlawi O. Simultaneous PET/MR will replace PET/CT as the molecular multimodality imaging platform of choice. Med Phys 2007;34:1525–8.PubMedCrossRefGoogle Scholar
  214. 214.
    Pichler BJ, Wehrl HF, Kolb A, Judenhofer MS. Positron emission tomography/magnetic resonance imaging: the next generation of multimodality imaging? Semin Nucl Med 2008;38:199–208.PubMedCrossRefGoogle Scholar
  215. 215.
    Hillner BE, Siegel BA, Liu D, Shields AF, Gareen IF, Hanna L, et al. Impact of positron emission tomography/computed tomography and positron emission tomography (PET) alone on expected management of patients with cancer: initial results from the National Oncologic PET Registry. J Clin Oncol 2008;26:2155–61.PubMedCrossRefGoogle Scholar
  216. 216.
    Riegel AC, Berson AM, Destian S, Ng T, Tena LB, Mitnick RJ, et al. Variability of gross tumor volume delineation in head-and-neck cancer using CT and PET/CT fusion. Int J Radiat Oncol Biol Phys 2006;65:726–32.PubMedGoogle Scholar
  217. 217.
    Davis JB, Reiner B, Huser M, Burger C, Szekely G, Ciernik IF. Assessment of (18)F PET signals for automatic target volume definition in radiotherapy treatment planning. Radiother Oncol 2006;80:43–50.PubMedCrossRefGoogle Scholar
  218. 218.
    Drever L, Roa W, McEwan A, Robinson D. Iterative threshold segmentation for PET target volume delineation. Med Phys 2007;34:1253–65.PubMedCrossRefGoogle Scholar
  219. 219.
    Vauclin S, Doyeux K, Hapdey S, Edet-Sanson A, Vera P, Gardin I. Development of a generic thresholding algorithm for the delineation of 18FDG-PET-positive tissue: application to the comparison of three thresholding models. Phys Med Biol 2009;54:6901–16.PubMedCrossRefGoogle Scholar
  220. 220.
    Day E, Betler J, Parda D, Reitz B, Kirichenko A, Mohammadi S, et al. A region growing method for tumor volume segmentation on PET images for rectal and anal cancer patients. Med Phys 2009;36:4349–58.PubMedCrossRefGoogle Scholar
  221. 221.
    van Dalen JA, Hoffmann AL, Dicken V, Vogel WV, Wiering B, Ruers TJ, et al. A novel iterative method for lesion delineation and volumetric quantification with FDG PET. Nucl Med Commun 2007;28:485–93.PubMedCrossRefGoogle Scholar
  222. 222.
    Erdi YE, Rosenzweig K, Erdi AK, Macapinlac HA, Hu Y-C, Braban LE, et al. Radiotherapy treatment planning for patients with non-small cell lung cancer using positron emission tomography (PET). Radiother Oncol 2002;62:51–60.PubMedCrossRefGoogle Scholar
  223. 223.
    Vrieze O, Haustermans K, De Wever W, Lerut T, Van Cutsem E, Ectors N, et al. Is there a role for FGD-PET in radiotherapy planning in esophageal carcinoma? Radiother Oncol 2004;73:269–75.PubMedCrossRefGoogle Scholar
  224. 224.
    van Loon J, Offermann C, Bosmans G, Wanders R, Dekker A, Borger J, et al. 18FDG-PET based radiation planning of mediastinal lymph nodes in limited disease small cell lung cancer changes radiotherapy fields: a planning study. Radiother Oncol 2008;87:49–54.PubMedCrossRefGoogle Scholar
  225. 225.
    Breen SL, Publicover J, De Silva S, Pond G, Brock K, O’Sullivan B, et al. Intraobserver and interobserver variability in GTV delineation on FDG-PET-CT images of head and neck cancers. Int J Radiat Oncol Biol Phys 2007;68:763–70.PubMedGoogle Scholar
  226. 226.
    Schinagl DA, Hoffmann AL, Vogel WV, van Dalen JA, Verstappen SM, Oyen WJ, et al. Can FDG-PET assist in radiotherapy target volume definition of metastatic lymph nodes in head-and-neck cancer? Radiother Oncol 2009;91:95–100.PubMedCrossRefGoogle Scholar
  227. 227.
    Murakami R, Uozumi H, Hirai T, Nishimura R, Katsuragawa S, Shiraishi S, et al. Impact of FDG-PET/CT fused imaging on tumor volume assessment of head-and-neck squamous cell carcinoma: intermethod and interobserver variations. Acta Radiol 2008;49:693–9.PubMedCrossRefGoogle Scholar
  228. 228.
    El-Bassiouni M, Ciernik IF, Davis JB, El-Attar I, Reiner B, Burger C, et al. [18FDG] PET-CT-based intensity-modulated radiotherapy treatment planning of head and neck cancer. Int J Radiat Oncol Biol Phys 2007;69:286–93.PubMedGoogle Scholar
  229. 229.
    Deantonio L, Beldi D, Gambaro G, Loi G, Brambilla M, Inglese E, et al. FDG-PET/CT imaging for staging and radiotherapy treatment planning of head and neck carcinoma. Radiat Oncol 2008;3:29.PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Division of Nuclear MedicineGeneva University HospitalGeneva 4Switzerland
  2. 2.Geneva Neuroscience CenterGeneva UniversityGenevaSwitzerland
  3. 3.Department of Radiation OncologyWashington University School of MedicineSt. LouisUSA

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