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Image Processing and Analysis of PET and Hybrid PET Imaging

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Basic Science of PET Imaging

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Abstract

PET imaging is a main diagnostic modality of different diseases including cancer. In the particular case of cancer, PET is widely used for staging of disease progression, identification of the treatment gross tumor volume, monitoring of disease, as well as prediction of outcomes and personalization of treatment regimens. Among the arsenal of different functional imaging modalities, PET has benefited from early adoption of quantitative image analysis starting from simple standard uptake value (SUV) normalization to more advanced extraction of complex imaging uptake patterns, thanks chiefly to the application of sophisticated image processing algorithms. In this chapter, we discuss the application of image processing techniques to PET imaging with special focus on the oncological radiotherapy domain starting from basic feature extraction to application in target definition using image segmentation/registration and more recent image-based outcome modeling in the radiomics field. We further extend the discussion into hybrid anatomical functional combinations of PET/CT and PET/MR multimodalities.

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References

  1. Verhagen AF, Bootsma GP, Tjan-Heijnen VC, van der Wilt GJ, Cox AL, Brouwer MH, et al. FDG-PET in staging lung cancer: how does it change the algorithm? Lung Cancer. 2004;44:175–81.

    Article  CAS  PubMed  Google Scholar 

  2. 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.

    Article  PubMed  Google Scholar 

  3. 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.

    PubMed  Google Scholar 

  4. Bradley J. Applications for FDG-PET in lung cancer; staging, targeting, and follow-up. Chicago: The Radiological Society of North America; 2004.

    Google Scholar 

  5. Erdi YE, Macapinlac H, Rosenzweig KE, Humm JL, Larson SM, Erdi AK, et al. Use of PET to monitor the response of lung cancer to radiation treatment. Eur J Nucl Med. 2000;27:861–6.

    Article  CAS  PubMed  Google Scholar 

  6. Mac Manus MP, Hicks RJ. PET scanning in lung cancer: current status and future directions. Semin Surg Oncol. 2003;21:149–55.

    Article  PubMed  Google Scholar 

  7. Mac Manus MP, Hicks RJ, Matthews JP, McKenzie A, Rischin D, Salminen EK, et al. Positron emission tomography is superior to computed tomography scanning for response-assessment after radical radiotherapy or chemoradiotherapy in patients with non-small-cell lung cancer. J Clin Oncol. 2003;21:1285–92.

    Article  PubMed  Google Scholar 

  8. MacManus MR, Hicks R, Fisher R, Rischin D, Michael M, Wirth A, et al. FDG-PET-detected extracranial metastasis in patients with non-small cell lung cancer undergoing staging for surgery or radical radiotherapy – survival correlates with metastatic disease burden. Acta Oncol. 2003;42:48–54.

    Article  PubMed  Google Scholar 

  9. Pandit N, Gonen M, Krug L, Larson SM. Prognostic value of [18F]FDG-PET imaging in small cell lung cancer. Eur J Nucl Med Mol Imaging. 2003;30:78–84.

    Article  PubMed  Google Scholar 

  10. Toloza EM, Harpole L, McCrory DC. Noninvasive staging of non-small cell lung cancer: a review of the current evidence. Chest. 2003;123:137S–46.

    Article  PubMed  Google Scholar 

  11. Schwartz DL, Ford E, Rajendran J, Yueh B, Coltrera MD, Virgin J, et al. FDG-PET/CT imaging for preradiotherapy staging of head-and-neck squamous cell carcinoma. Int J Radiat Oncol Biol Phys. 2005;61:129–36.

    Article  PubMed  Google Scholar 

  12. Suarez Fernandez JP, Maldonado Suarez A, Dominguez Grande ML, Santos Ortega M, Rodriguez Villalba S, Garcia Camanaque L, et al. Positron emission tomography (PET) imaging in head and neck cancer. Acta Otorrinolaringol Esp. 2004;55:303–9.

    Article  CAS  PubMed  Google Scholar 

  13. Oyama N, Miller TR, Dehdashti F, Siegel BA, Fischer KC, Michalski JM, et al. 11C-acetate PET imaging of prostate cancer: detection of recurrent disease at PSA relapse. J Nucl Med. 2003;44:549–55.

    CAS  PubMed  Google Scholar 

  14. Mutic S, Malyapa RS, Grigsby PW, Dehdashti F, Miller TR, Zoberi I, et al. PET-guided IMRT for cervical carcinoma with positive para-aortic lymph nodes-a dose-escalation treatment planning study. Int J Radiat Oncol Biol Phys. 2003;55:28–35.

    Article  PubMed  Google Scholar 

  15. 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.

    Article  PubMed  Google Scholar 

  16. Ciernik IF. Radiotherapy of rectal cancer. Schweiz Rundsch Med Prax. 2004;93:1441–6.

    CAS  Google Scholar 

  17. Castellucci P, Zinzani P, Nanni C, Farsad M, Moretti A, Alinari L, et al. 18F-FDG PET early after radiotherapy in lymphoma patients. Cancer Biother Radiopharm. 2004;19:606–12.

    PubMed  Google Scholar 

  18. Spaepen K, Stroobants S, Verhoef G, Mortelmans L. Positron emission tomography with [(18)F]FDG for therapy response monitoring in lymphoma patients. Eur J Nucl Med Mol Imaging. 2003;30 Suppl 1:S97–105.

    Article  PubMed  Google Scholar 

  19. Fogarty GB, Tartaglia CJ, Peters LJ. Primary melanoma of the oesophagus well palliated by radiotherapy. Br J Radiol. 2004;77:1050–2.

    Article  CAS  PubMed  Google Scholar 

  20. Biersack HJ, Bender H, Palmedo H. FDG-PET in monitoring therapy of breast cancer. Eur J Nucl Med Mol Imaging. 2004;31 Suppl 1:S112–7.

    Article  PubMed  Google Scholar 

  21. Lind P, Igerc I, Beyer T, Reinprecht P, Hausegger K. Advantages and limitations of FDG PET in the follow-up of breast cancer. Eur J Nucl Med Mol Imaging. 2004;31 Suppl 1:S125–34.

    PubMed  Google Scholar 

  22. Zangheri B, Messa C, Picchio M, Gianolli L, Landoni C, Fazio F. PET/CT and breast cancer. Eur J Nucl Med Mol Imaging. 2004;31 Suppl 1:S135–42.

    Article  PubMed  Google Scholar 

  23. Brun E, Kjellen E, Tennvall J, Ohlsson T, Sandell A, Perfekt R, et al. FDG PET studies during treatment: prediction of therapy outcome in head and neck squamous cell carcinoma. Head Neck. 2002;24:127–35.

    Article  PubMed  Google Scholar 

  24. Hope AJ, Saha P, Grigsby PW. FDG-PET in carcinoma of the uterine cervix with endometrial extension. Cancer. 2006;106:196–200.

    Article  PubMed  Google Scholar 

  25. Kalff V, Duong C, Drummond EG, Matthews JP, Hicks RJ. Findings on 18F-FDG PET scans after neoadjuvant chemoradiation provides prognostic stratification in patients with locally advanced rectal carcinoma subsequently treated by radical surgery. J Nucl Med. 2006;47:14–22.

    PubMed  Google Scholar 

  26. Hicks RJ, Mac Manus MP, Matthews JP, Hogg A, Binns D, Rischin D, et al. Early FDG-PET imaging after radical radiotherapy for non-small-cell lung cancer: inflammatory changes in normal tissues correlate with tumor response and do not confound therapeutic response evaluation. Int J Radiat Oncol Biol Phys. 2004;60:412–8.

    Article  PubMed  Google Scholar 

  27. Grigsby PW, Siegel BA, Dehdashti F, Rader J, Zoberi I. Posttherapy [18F] fluorodeoxyglucose positron emission tomography in carcinoma of the cervix: response and outcome. J Clin Oncol. 2004;22:2167–71.

    Article  PubMed  Google Scholar 

  28. Shields AF. Positron emission tomography measurement of tumor metabolism and growth: its expanding role in oncology. Mol Imaging Biol. 2006;8:141–50.

    Article  PubMed  Google Scholar 

  29. Denecke T, Rau B, Hoffmann KT, Hildebrandt B, Ruf J, Gutberlet M, et al. Comparison of CT, MRI and FDG-PET in response prediction of patients with locally advanced rectal cancer after multimodal preoperative therapy: is there a benefit in using functional imaging? Eur Radiol. 2005;15:1658–66.

    Article  CAS  PubMed  Google Scholar 

  30. Benz MR, Allen-Auerbach MS, Eilber FC, Chen HJJ, Dry S, Phelps ME, et al. Combined assessment of metabolic and volumetric changes for assessment of tumor response in patients with soft-tissue sarcomas. J Nucl Med. 2008;49:1579–84. doi:10.2967/jnumed.108.053694.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Yang D-H, Min J-J, Jeong Y, Ahn J-S, Kim Y-K, Cho S-H, et al. The combined evaluation of interim contrast-enhanced computerized tomography (CT) and FDG-PET/CT predicts the clinical outcomes and may impact on the therapeutic plans in patients with aggressive non-Hodgkin’s lymphoma. Ann Hematol. 2009;88:425–32.

    Article  PubMed  Google Scholar 

  32. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48:441–6. doi:10.1016/j.ejca.2011.11.036. doi:S0959-8049(11)00999-3 [pii].

    Article  PubMed  PubMed Central  Google Scholar 

  33. Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, et al. Radiomics: the process and the challenges. Magn Reson Imaging. 2012;30:1234–48. doi:10.1016/j.mri.2012.06.010. doi:S0730-725X(12)00220-2 [pii].

    Article  PubMed  PubMed Central  Google Scholar 

  34. El Naqa I, Grigsby P, Apte A, Kidd E, Donnelly E, Khullar D, et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit. 2009;42:1162–71. doi:10.1016/j.patcog.2008.08.011.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Kidd EA, El Naqa I, Siegel BA, Dehdashti F, Grigsby PW. FDG-PET-based prognostic nomograms for locally advanced cervical cancer. Gynecol Oncol. 2012;127:136–40. doi:10.1016/j.ygyno.2012.06.027. doi:S0090-8258(12)00478-7 [pii].

    Article  PubMed  PubMed Central  Google Scholar 

  36. Vaidya M, Creach KM, Frye J, Dehdashti F, Bradley JD, El Naqa I. Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. Radiother Oncol. 2012;102:239–45. doi:10.1016/j.radonc.2011.10.014. doi:S0167-8140(11)00626-8 [pii].

    Article  PubMed  Google Scholar 

  37. El Naqa I. The role of quantitative PET in predicting cancer treatment outcomes. Clin Transl Imaging. 2014;2:305–20. doi:10.1007/s40336-014-0063-1.

    Article  Google Scholar 

  38. Strauss LG, Conti PS. The applications of PET in clinical oncology. J Nucl Med. 1991;32:623–48.

    CAS  PubMed  Google Scholar 

  39. Larson SM, Erdi Y, Akhurst T, Mazumdar M, Macapinlac HA, Finn RD, et al. Tumor treatment response based on visual and quantitative changes in global tumor glycolysis using PET-FDG imaging. The visual response score and the change in total lesion glycolysis. Clin Positron Imaging. 1999;2:159–71. doi:S1095039799000163 [pii].

    Article  PubMed  Google Scholar 

  40. Jain AK. Fundamentals of digital image processing. Englewood Cliffs: Prentice Hall; 1989.

    Google Scholar 

  41. O’Sullivan F, Roy S, O’Sullivan J, Vernon C, Eary J. Incorporation of tumor shape into an assessment of spatial heterogeneity for human sarcomas imaged with FDG-PET. Biostatistics. 2005;6:293–301. doi:10.1093/biostatistics/kxi010.

    Article  PubMed  Google Scholar 

  42. O’Sullivan F, Roy S, Eary J. A statistical measure of tissue heterogeneity with application to 3D PET sarcoma data. Biostatistics. 2003;4:433–48. doi:10.1093/biostatistics/4.3.433.

    Article  PubMed  Google Scholar 

  43. Castleman KR. Digital image processing. Englewood Cliffs: Prentice Hall; 1996.

    Google Scholar 

  44. Haralick R, Shanmugam K, Dinstein I. Texture features for image classification. IEEE Trans Sys Man Cyb SMC. 1973;3:610–21.

    Article  Google Scholar 

  45. Zhang J, Tan T. Brief review of invariant texture analysis methods. Pattern Recognit. 2002;35:735–47. doi:http://dx.doi.org/10.1016/S0031-3203(01)00074-7.

  46. Castellano G, Bonilha L, Li LM, Cendes F. Texture analysis of medical images. Clin Radiol. 2004;59:1061–9. doi:http://dx.doi.org/10.1016/j.crad.2004.07.008.

  47. Chicklore S, Goh V, Siddique M, Roy A, Marsden P, Cook GR. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging. 2013;40:133–40. doi:10.1007/s00259-012-2247-0.

    Article  PubMed  Google Scholar 

  48. Tixier F, Le Rest CC, Hatt M, Albarghach N, Pradier O, Metges J-P, et al. Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J Nucl Med. 2011;52:369–78. doi:10.2967/jnumed.110.082404.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Cheng N-M, Dean Fang Y-H, Tung-Chieh Chang J, Huang C-G, Tsan D-L, Ng S-H, et al. Textural features of pretreatment 18F-FDG PET/CT images: prognostic significance in patients with advanced T-stage oropharyngeal squamous cell carcinoma. J Nucl Med. 2013;54:1703–9. doi:10.2967/jnumed.112.119289.

    Article  CAS  PubMed  Google Scholar 

  50. Cook GJR, Yip C, Siddique M, Goh V, Chicklore S, Roy A, et al. Are pretreatment 18F-FDG PET tumor textural features in non–small cell lung cancer associated with response and survival after chemoradiotherapy? J Nucl Med. 2013;54:19–26. doi:10.2967/jnumed.112.107375.

    Article  PubMed  Google Scholar 

  51. Szczypinski PM, Strzelecki M, Materka A, Klepaczko A. MaZda – a software package for image texture analysis. Comput Methods Programs Biomed. 2009;94:66–76. doi:10.1016/j.cmpb.2008.08.005.

    Article  PubMed  Google Scholar 

  52. Watabe H, Ikoma Y, Kimura Y, Naganawa M, Shidahara M. PET kinetic analysis – compartmental model. Ann Nucl Med. 2006;20:583–8.

    Article  CAS  PubMed  Google Scholar 

  53. Graham MM, Peterson LM, Hayward RM. Comparison of simplified quantitative analyses of FDG uptake. Nucl Med Biol. 2000;27:647–55.

    Article  CAS  PubMed  Google Scholar 

  54. Patlak CS, Blasberg RG. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Generalizations. J Cereb Blood Flow Metab. 1985;5:584–90.

    Article  CAS  PubMed  Google Scholar 

  55. Choi NC, Fischman AJ, Niemierko A, Ryu JS, Lynch T, Wain J, et al. Dose–response relationship between probability of pathologic tumor control and glucose metabolic rate measured with FDG PET after preoperative chemoradiotherapy in locally advanced non-small-cell lung cancer. Int J Radiat Oncol Biol Phys. 2002;54:1024–35. doi:S0360301602030389 [pii].

    Article  PubMed  Google Scholar 

  56. Thorwarth D, Eschmann S-M, Holzner F, Paulsen F, Alber M. Combined uptake of [18F]FDG and [18F]FMISO correlates with radiation therapy outcome in head-and-neck cancer patients. Radiother Oncol. 2006;80:151–6.

    Article  CAS  PubMed  Google Scholar 

  57. Thorwarth D, Eschmann S-M, Paulsen F, Alber M. A model of reoxygenation dynamics of head-and-neck tumors based on serial 18F-fluoromisonidazole positron emission tomography investigations. Int J Radiat Oncol Biol Phys. 2007;68:515–21.

    Article  CAS  PubMed  Google Scholar 

  58. Turkheimer FE, Aston JA, Asselin MC, Hinz R. Multi-resolution Bayesian regression in PET dynamic studies using wavelets. Neuroimage. 2006;32:111–21. doi:10.1016/j.neuroimage.2006.03.002. doi:S1053-8119(06)00180-7 [pii].

    Article  CAS  PubMed  Google Scholar 

  59. Milker-Zabel S, Zabel-du Bois A, Henze M, Huber P, Schulz-Ertner D, Hoess A, et al. Improved target volume definition for fractionated stereotactic radiotherapy in patients with intracranial meningiomas by correlation of CT, MRI, and [68Ga]-DOTATOC-PET. Int J Radiat Oncol Biol Phys. 2006;65:222–7.

    Article  PubMed  Google Scholar 

  60. Tofts PS, Brix G, Buckley DL, Evelhoch JL, Henderson E, Knopp MV, et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging. 1999;10:223–32. doi:10.1002/(SICI)1522-2586(199909)10:3<223::AID-JMRI2>3.0.CO;2-S [pii].

    Article  CAS  PubMed  Google Scholar 

  61. Hajnal JV, Hawkes DJ, Hill DLG. Medical image registration. Boca Raton: CRC Press; 2001.

    Book  Google Scholar 

  62. Yang D, Zheng J, Nofal A, Wu Y, Deasy J, El Naqa I. Techniques and software tool for 3D multimodality medical image segmentation Journal of radiation oncology informatics. 2009:accepted.

    Google Scholar 

  63. Yang D, Brame S, El Naqa I, Aditya A, Wu Y, Goddu SM, et al. Technical note: DIRART – a software suite for deformable image registration and adaptive radiotherapy research. Med Phys. 2011;38:67–77.

    Article  PubMed  Google Scholar 

  64. Pham DL, Xu C, Prince JL. Current methods in medical image segmentation. Annu Rev Biomed Eng. 2000;2:315–37.

    Article  CAS  PubMed  Google Scholar 

  65. Suri JS, Setarehdan SK, Singh S. Advanced algorithmic approaches to medical image segmentation: state-of-the-art applications in cardiology, neurology, mammography, and pathology. New York: Springer; 2002.

    Book  Google Scholar 

  66. Zaidi H, Abdoli M, Fuentes CL, El Naqa IM. Comparative methods for PET image segmentation in pharyngolaryngeal squamous cell carcinoma. Eur J Nucl Med Mol Imaging. 2012;39(5):881–91. doi:10.1007/s00259-011-2053-0.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Zaidi H, El Naqa I. PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques. Eur J Nucl Med Mol Imaging. 2010;37:2165–87. doi:10.1007/s00259-010-1423-3.

    Article  PubMed  Google Scholar 

  68. Yoo TS, Ackerman MJ, Lorensen WE, Schroeder W, Chalana V, Aylward S, et al. Engineering and algorithm design for an image processing API: a technical report on ITK – the insight toolkit. In: Proceedings of medicine meets virtual reality. 2002;586–92.

    Google Scholar 

  69. Papademetris X, Jackowski M, Rajeevan N, Constable RT, Staib L. BioImage suite: an integrated medical image analysis suite. Insight J. 2005;2006:209.

    Google Scholar 

  70. McAuliffe MJ, Lalonde FM, McGarry D, Gandler W, Csaky K, Trus BL. Medical image processing, analysis & visualization in clinical research. In: Proceedings of the fourteenth IEEE symposium on computer-based medical systems. IEEE Computer Society. 2001.

    Google Scholar 

  71. Abramoff MD, Magelhaes PJ, Ram SJ. Image processing with imageJ. Biophotonics Int. 2004;11:36–42.

    Google Scholar 

  72. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, et al. 3D Slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging. 2012;30:1323–41. doi:10.1016/j.mri.2012.05.001.

    Article  PubMed  PubMed Central  Google Scholar 

  73. 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.

    Article  PubMed  Google Scholar 

  74. Sebbahi A, Herment A, de Cesare A, Mousseaux E. Multimodality cardiovascular image segmentation using a deformable contour model. Comput Med Imaging Graph. 1997;21:79–89.

    Article  CAS  PubMed  Google Scholar 

  75. Zheng J, Naqa IE, Rowold FE, Pilgram TK, Woodard PK, Saffitz JE, et al. Quantitative assessment of coronary artery plaque vulnerability by high-resolution magnetic resonance imaging and computational biomechanics: a pilot study ex vivo. Magn Reson Med. 2005;54:1360–8.

    Article  PubMed  PubMed Central  Google Scholar 

  76. 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 

  77. 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 (The International Society for Optical Engineering) Press; 2002. p. 129–74.

    Google Scholar 

  78. Suri JS, Kecheng L, Singh S, Laxminarayan SN, Xiaolan Z, Reden L. Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review. IEEE Trans Inf Technol Biomed. 2002;6:8–28.

    Article  PubMed  Google Scholar 

  79. Aubert G. Mathematical problems in image processing: partial differential equations and the calculus of variations. 2nd ed. New York: Springer; 2006.

    Google Scholar 

  80. Osher S, Fedkiw RP. Level set methods and dynamic implicit surfaces. New York: Springer; 2003.

    Book  Google Scholar 

  81. Kass M, Witkin A, Terzopoulos. Snakes: active contour models. In: First international conference on computer vision, London. 1987;259–68.

    Google Scholar 

  82. 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.

    Article  PubMed  Google Scholar 

  83. Wong CY, Schmidt J, Bong JS, Chundru S, Kestin L, Yan D, et al. Correlating metabolic and anatomic responses of primary lung cancers to radiotherapy by combined F-18 FDG PET-CT imaging. Radiat Oncol. 2007;2:18. doi:10.1186/1748-717X-2-18. doi:1748-717X-2-18 [pii].

    Article  PubMed  PubMed Central  Google Scholar 

  84. Coon D, Gokhale AS, Burton SA, Heron DE, Ozhasoglu C, Christie N. Fractionated stereotactic body radiation therapy in the treatment of primary, recurrent, and metastatic lung tumors: the role of positron emission tomography/computed tomography-based treatment planning. Clin Lung Cancer. 2008;9:217–21. doi:10.3816/CLC.2008.n.032. doi:935437162W233526 [pii].

    Article  PubMed  Google Scholar 

  85. Steel GG. Basic clinical radiobiology. 3rd ed. London/New York/Arnold: Oxford University Press; 2002.

    Google Scholar 

  86. Webb S. The physics of three-dimensional radiation therapy: conformal radiotherapy, radiosurgery, and treatment planning. Bristol/Philadelphia: Institute of Physics Pub; 2001.

    Google Scholar 

  87. El Naqa I. Outcomes modeling. In: Starkschall G, Siochi C, editors. Informatics in radiation oncology. Boca Raton: CRC Press/Taylor and Francis; 2013. p. 257–75.

    Google Scholar 

  88. El Naqa I, Suneja G, Lindsay PE, Hope AJ, Alaly JR, Vicic M, et al. Dose response explorer: an integrated open-source tool for exploring and modelling radiotherapy dose-volume outcome relationships. Phys Med Biol. 2006;51:5719–35.

    Article  PubMed  Google Scholar 

  89. El-Naqa I, Yang Y, Galatsanos NP, Nishikawa RM, Wernick MN. A similarity learning approach to content-based image retrieval: application to digital mammography. IEEE Trans Med Imaging. 2004;23:1233–44. doi:10.1109/TMI.2004.834601.

    Article  PubMed  Google Scholar 

  90. Deasy JO, El Naqa I. Image-based modeling of normal tissue complication probability for radiation therapy. In: Mehta M, Bentzen S, editors. Radiation oncology advances. New York: Springer; 2007.

    Google Scholar 

  91. El Naqa I, Bradley JD, Lindsay PE, Blanco AI, Vicic M, Hope AJ, et al. Multi-variable modeling of radiotherapy outcomes including dose-volume and clinical factors. Int J Radiat Oncol Biol Phys. 2006;64:1275–86.

    Article  PubMed  Google Scholar 

  92. Moses WW. Fundamental limits of spatial resolution in PET. Nucl Instrum Meth Phys Res A Accelerators Spectrometers Detectors Assoc Equipment. 2011;648 Suppl 1:S236–40. doi:10.1016/j.nima.2010.11.092.

    Article  CAS  Google Scholar 

  93. Tong S, Alessio AM, Kinahan PE. Image reconstruction for PET/CT scanners: past achievements and future challenges. Imaging Med. 2010;2:529–45. doi:10.2217/iim.10.49.

    Article  PubMed  PubMed Central  Google Scholar 

  94. Nahmias C, Wahl LM. Reproducibility of standardized uptake value measurements determined by 18F-FDG PET in malignant tumors. J Nucl Med. 2008;49:1804–8. doi:10.2967/jnumed.108.054239.

    Article  PubMed  Google Scholar 

  95. Kinahan PE, Fletcher JW. Positron emission tomography-computed tomography standardized uptake values in clinical practice and assessing response to therapy. Semi Ultrasound CT MRI. 2010;31:496–505. doi:http://dx.doi.org/10.1053/j.sult.2010.10.001.

  96. Hatt M, Cheze-Le Rest C, Aboagye EO, Kenny LM, Rosso L, Turkheimer FE, et al. Reproducibility of 18F-FDG and 3′-deoxy-3′-18F-fluorothymidine PET tumor volume measurements. J Nucl Med. 2010;51:1368–76. doi:10.2967/jnumed.110.078501.

    Article  CAS  PubMed  Google Scholar 

  97. Galavis PE, Hollensen C, Jallow N, Paliwal B, Jeraj R. Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters. Acta Oncol. 2010;49:1012–6. doi:10.3109/0284186X.2010.498437.

    Article  PubMed  PubMed Central  Google Scholar 

  98. Cheng NM, Fang YH, Yen TC. The promise and limits of PET texture analysis. Ann Nucl Med. 2013;27:867–9. doi:10.1007/s12149-013-0759-8.

    Article  CAS  PubMed  Google Scholar 

  99. Tixier F, Hatt M, Le Rest CC, Le Pogam A, Corcos L, Visvikis D. Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET. J Nucl Med. 2012;53:693–700. doi:10.2967/jnumed.111.099127.

    Article  PubMed  PubMed Central  Google Scholar 

  100. El Naqa I, Kawrakow I, Fippel M, Siebers JV, Lindsay PE, Wickerhauser MV, et al. A comparison of Monte Carlo dose calculation denoising techniques. Phys Med Biol. 2005;50:909–22. doi:10.1088/0031-9155/50/5/014. doi:S0031-9155(05)86128-7 [pii].

    Article  PubMed  Google Scholar 

  101. Le Pogam A, Hanzouli H, Hatt M, Cheze Le Rest C, Visvikis D. Denoising of PET images by combining wavelets and curvelets for improved preservation of resolution and quantitation. Med Image Anal. 2013;17:877–91. doi:10.1016/j.media.2013.05.005.

    Article  PubMed  Google Scholar 

  102. El Naqa I, Li R, Murphy MJ, editors. Machine learning in radiation oncology: theory and application. 1st ed. Cham: Springer International Publishing; 2015.

    Google Scholar 

  103. El Naqa I, Bradley J, Deasy J. Machine learning methods for radiobiological outcome modeling. In: Mehta M, Paliwal B, Bentzen S, editors. Physical, chemical, and biological targeting in radiation oncology. Madison: Medical Physics Publishing; 2005.

    Google Scholar 

  104. El-Naqa I, Yang Y, Galatsanos NP, Nishikawa RM, Wernick MN. A similarity learning approach to content-based image retrieval: application to digital mammography. IEEE Trans Med Imaging. 2004;23:1233–44.

    Article  PubMed  Google Scholar 

  105. El-Naqa I, Yang Y, Wernick MN, Galatsanos NP, Nishikawa RM. A support vector machine approach for detection of microcalcifications. IEEE Trans Med Imaging. 2002;21:1552–63.

    Article  PubMed  Google Scholar 

  106. Schèolkopf B, Tsuda K, Vert J-P. Kernel methods in computational biology. Cambridge, MA: MIT Press; 2004.

    Google Scholar 

  107. Shawe-Taylor J, Cristianini N. Kernel methods for pattern analysis. Cambridge/New York: Cambridge University Press; 2004.

    Book  Google Scholar 

  108. Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning: data mining, inference, and prediction: with 200 full-color illustrations. New York: Springer; 2001.

    Google Scholar 

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Authors and Affiliations

  1. Department of Radiation Oncology, Physics Division, University of Michigan, 519 W. William St, Argus Bldg 1, Ann Arbor, MI, 48103-4943, USA

    Issam El Naqa PhD, DABR

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  1. Issam El Naqa PhD, DABR
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Correspondence to Issam El Naqa PhD, DABR .

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  1. Medical Biophysics Department of Physics Faculty of Science, Helwan University, Cairo, Egypt

    Magdy M. Khalil

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El Naqa, I. (2017). Image Processing and Analysis of PET and Hybrid PET Imaging. In: Khalil, M. (eds) Basic Science of PET Imaging. Springer, Cham. https://doi.org/10.1007/978-3-319-40070-9_12

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  • DOI: https://doi.org/10.1007/978-3-319-40070-9_12

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