1. M. Aristophanous et al. Clinical utility of 4D FDG-PET/CT scans in radiation treatment planning. International Journal of Radiation Oncology* Biology* Physics. 82 (1), 99-105, (2012).
2. D.W. Townsend, T. Beyer, and T.M. Blodgett, PET/CT scanners: a hardware approach to image fusion. Seminars in nuclear medicine. 33 (3), (2003(.
3. H. Zaidi, and B. Hasegawa, Determination of the attenuation map in emission tomography. Journal of Nuclear Medicine. 44 (2), 291-315, (2003).
4. D.W. Townsend et al. PET/CT today and tomorrow. The journal of nuclear medicine. 45, (2004).
5. A.S. Nehmeh et al. Effect of respiratory gating on quantifying PET images of lung cancer. Journal of nuclear medicine. 43 (7), (2002).
6. C. Liu et al. The impact of respiratory motion on tumor quantification and delineation in static PET/CT imaging. Physics in medicine and biology. 54 (24), (2009).
7. T-C. Huang and Y-C. Wang, Deformation effect on SUVmax changes in thoracic tumors using 4-D PET/CT scan. PLoS One. 8 (3), (2013).
8. Sh. Nagamachi et al. Reproducibility of deep inspiration breath-hold 18F-FDG PET/CT technique in diagnosing cancer located in the area affected by respiratory motion. Journal of Nuclear Medicine. 49 (1), (2008).
9. T-C. Huang et al. Respiratory motion reduction in PET/CT using abdominal compression for lung cancer patients. PloS one. 9 (5), (2014).
10. J. Wang, Motion Correction Algorithm of Lung Tumors for Respiratory Gated PET Images. (2009).
11. P. Geramifar et al. Respiratory-induced errors in tumor quantification and delineation in CT attenuation-corrected PET images: effects of tumor size, tumor location, and respiratory trace: a simulation study using the 4D XCAT phantom. Molecular Imaging and Biology. 15 (6), 655-665, (2013).
12. J. Vandemeulebroucke, Motion modelling and estimation for image guided radiation therapy. Diss. Ph.D. thesis, L’Institut National des Sciences Appliqu’ees de Lyon, (2010).
13. P. Mishra et al. Adaptation and applications of a realistic digital phantom based on patient lung tumor trajectories. Physics in Medicine & Biology. 57 (11), (2012).
14. C. Lee et al. Hybrid computational phantoms of the male and female newborn patient: NURBS-based whole-body models. Physics in Medicine & Biology. 52 (12), (2007).
15. K. Thielemans et al. STIR: software for tomographic image reconstruction release. Physics in Medicine & Biology. 57 (4), (2012).
16. A.M. Loening and S.S. Gambhir, AMIDE: a free software tool for multimodality medical image analysis. Molecular imaging. 2 (3), (2003).
17. C.A. Schneider, S.R. Wayne, and K.W. Eliceiri, NIH Image to ImageJ: 25 years of image analysis. Nature methods. 9 (7), (2012).
18. T.J. Collins, ImageJ for microscopy. Biotechniques. 43 (1), 25-30 (2007).
19. V. Girish, and A. Vijayalakshmi, Affordable image analysis using NIH Image/ImageJ. Indian journal of cancer. 41 (1), (2004).
20. Y. Sugawara et al. Reevaluation of the standardized uptake value for FDG: variations with body weight and methods for correction. Radiology. 213 (2), 521-525, (1999).
21. N.C. Krak et al. Measuring [18F] FDG uptake in breast cancer during chemotherapy: comparison of analytical methods. European journal of nuclear medicine and molecular imaging. 30 (5), 674-681, (2003).
22. R.L. Wahl et al. From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. Journal of nuclear medicine: official publication, Society of Nuclear Medicine. 50 (1), (2009).
23. H. Young et al. Measurement of clinical and subclinical tumour response using [18F]-fluorodeoxyglucose and positron emission tomography: review and 1999 EORTC recommendations. European journal of cancer. 35 (13), 1773-1782, (1999).
24. M.R. Ay et al. Comparative assessment of energy-mapping approaches in CT-based attenuation correction for PET. Molecular Imaging and Biology. 13 (1), 187-198 (2011).