A method for generating large datasets of organ geometries for radiotherapy treatment planning studies

1. Olivera GH, Ruchala K, Lu W, Kapatoes J, Reckwerdt P, Jeraj R, et al. Evaluation of patient setup and plan optimization strategies based on deformable dose registration. Int J Radiat Oncol Biol Phys 2003; 57(Suppl): S188-9.10.1016/S0360-3016(03)00984-2Search in Google Scholar

2. Rietzel E, Chen GT, Choi NC, Willet CG. Four-dimensional image-based treatment planning: target volume segmentation and dose calculation in the presence of respiratory motion. Int J Radiat Oncol Biol Phys 2005; 61: 1535-50.10.1016/j.ijrobp.2004.11.037Search in Google Scholar

3. Keall PJ, Joshi S, Vedam SS, Siebers JV, Kini VR, Mohan R. Four-dimensional radiotherapy planning for DMLC-based respiratory motion tracking. Med Phys 2005; 32: 942-51.10.1118/1.1879152Search in Google Scholar

4. Yadav P, Ramasubramanian V, Paliwal BR. Feasibility study on effect and stability of adaptive radiotherapy on kilovoltage cone beam CT. Radiol Oncol 2011; 45: 220-6.10.2478/v10019-011-0024-5Search in Google Scholar

5. Vargas C, Martinez A, Kestin LL, Yan D, Grills I, Brabbins DS, et al. Dosevolume analysis of predictors for chronic rectal toxicity after treatment of prostate cancer with adaptive image-guided radiotherapy Int J Radiat Oncol Biol Phys 2005; 62: 1297-308.Search in Google Scholar

6. Yang Y, Schreibmann E, Li T, Xing L. Dosimetric evaluation of kV cone-beam CT (CBCT) based dose calculation. Phys Med Biol 2007; 52: 685-705.10.1088/0031-9155/52/3/011Search in Google Scholar

7. Wu C, Jeraj R, Lu W, Mackie TR. Fast treatment plan modification with an over-relaxed Cimmino algorithm. Med Phys 2004; 31: 191-200.10.1118/1.1631913Search in Google Scholar

8. Wu C, Jeraj R, Olivera GH, Mackie TR. Re-optimization in adaptive radiotherapy. Phys Med Biol 2002; 47: 3181-95.10.1088/0031-9155/47/17/309Search in Google Scholar

9. Yan D, Vicini F, Wong J, Martinez A. Adaptive radiation therapy. Phys Med Biol 1997; 42: 123-32.10.1088/0031-9155/42/1/008Search in Google Scholar

10. Yan D, Lockman D, Brabbins D, Tyburski L, Martinez A. An off-line strategy for constructing a patientspecific planning target volume in adaptive treatment process for prostate cancer. Int J Radiat Oncol Biol Phys 2000; 48: 289-302.10.1016/S0360-3016(00)00608-8Search in Google Scholar

11. Rehbinder H, Forsgren C, Lof J. Adaptive radiation therapy for compensation of errors in patient setup and treatment delivery. Med Phys 2004; 31: 3363-71.10.1118/1.180976815651619Search in Google Scholar

12. Lam KL, Ten Haken RK, Litzenberg D, Balter JM, Pollock SM. An application of Bayesian statistical methods to adaptive radiotherapy. Phys Med Biol 2005; 50: 3849-58.10.1088/0031-9155/50/16/01316077231Search in Google Scholar

13. Marchant TE, Amer AM, Moore CJ. Measurement of inter and intra fraction organ motion in radiotherapy using cone beam CT projection images. Phys Med Biol 2008; 53: 1087-98.10.1088/0031-9155/53/4/01818263960Search in Google Scholar

14. Peszyńska-Piorun M, Malicki J, Golusinski W. Doses in organs at risk during head & neck radiotherapy using IMRT and 3D-CRT. Radiol Oncol 2012; 46: 328-36.10.2478/v10019-012-0050-y357289523412761Search in Google Scholar

15. Matthiesen C, Ramgopol R, Seavey J, Ahmad S, Herman T. Intensity modulated radiation therapy (IMRT) for the treatment of unicentric Castlemans disease: a case report and review of the use of radiotherapy in the literature. Radiol Oncol 2012; 46: 265-70.10.2478/v10019-012-0008-0347294523077466Search in Google Scholar

16. Oldham M, Letourneau D, Watt L, Hugo G, Yan D, Lockman D, et al. Cone-beam-CT guided radiation therapy: a model for on-line application. Radiother Oncol 2005; 75: 271-8.10.1016/j.radonc.2005.03.02615890419Search in Google Scholar

17. Mohan R, Zhang X, Wang H, Kang Y, Wang X, Liu H, et al. Use of deformed intensity distributions for on-line modification of image-guided IMRT to account for inter-fractional anatomic changes. Int J Radiat Oncol Biol Phys 2005; 61: 1258-66.10.1016/j.ijrobp.2004.11.03315752908Search in Google Scholar

18. Zehtabian M, Faghihi R, Mosleh-Shirazi MA, Shakibafard AR, Mohammadi M, Baradaran-Ghahfarokhi M. A fast model for prediction of respiratory lung motion for image-guided radiotherapy: a feasibility study. Int J Radiat Res 2012; 10: 73-81.Search in Google Scholar

19. Strojan P. Jereb S, Borsos I, But-Hadzic J, Zidar N. Radiotherapy for inverted papilloma: a case report and review of the literature. Radiol Oncol 2013; 47: 71-6.10.2478/v10019-012-0045-8357383723450506Search in Google Scholar

20. Lötjönen J, Kivisto S, Koikkalainen J, Smutek D, Lauerma K. Statistical shape model of atria, ventricles and epicardium from short- and long-axis MR images. Med Image Anal 2004; 8: 371-86.10.1016/j.media.2004.06.01315450230Search in Google Scholar

21. Cootes TF, Taylor CJ, Cooper D, Graham J. Active shape models - their training and application. Comput Vis Image Underst 1995; 61: 38-59.10.1006/cviu.1995.1004Search in Google Scholar

22. Söhn M, Birkner M, Yan D, Alber M. Modeling individual geometric variation based on dominant eigenmodes of organ deformation: implementation and evaluation. Phys Med Biol 2005; 50: 5893-908.10.1088/0031-9155/50/24/00916333162Search in Google Scholar

23. Lötjönen J, Antila K, Lamminmaki E, Koikkalainen J, Lilja M. Artificial enlargement of a training set for statistical shape models: Application to cardiac images. Functional imaging and modeling of heart. Proceedings. Book series: Lecture notes in computer science 2005; 3504: 92-101.Search in Google Scholar

24. Tölli T, Koikkalainen J, Lauerma K, Lötjönen J. Artificially enlarged training set in image segmentation. Medical image computing and computer-assisted intervention. Proceedings. PT 1 Book series: Lecture notes in computer science 2006; 4190: 75-82.Search in Google Scholar

25. Dryden I, Mardia K. Statistical shape analysis. New York: John Wiley & Sons; 1998.Search in Google Scholar

26. Small C. The statistical theory of shape. Berlin: Springer; 1996.10.1007/978-1-4612-4032-7Search in Google Scholar

27. Ge Y, Maurer Jr C, Fitzpatrick J. Surface based 3-D image registration using the Iterative Closest Point algorithm with a closest point transform. Proc SPIE. Book series: Lecture notes in computer science 1996: 358-67.10.1117/12.237938Search in Google Scholar

28. Cootes TF, Hill A, Taylor CJ, Haslam J. The use of active shape models for locating structures in medical images. Image Vis Comput 1994; 12: 355-66.10.1016/0262-8856(94)90060-4Search in Google Scholar

29. Rajamani KT, Styner MA, Talib H, Zheng G, Nolte LP, MA Gonzalez Ballester MA. Statistical deformable bone models for robust 3D surface extrapolation from sparse data. Med Image Anal 2007; 11: 99-109.10.1016/j.media.2006.05.00117349939Search in Google Scholar

30. Cootes TF, Taylor CJ. A mixture model for representing shape variation. Image Vis Comp 1999; 8: 567-74.Search in Google Scholar

31. McLachlan G, Basford KE. Mixture models: inference and applications to clustering. New York: Dekker; 1988.Search in Google Scholar

32. Tendick F, Downes M, Goktekin T, Cavusoglu M, Feygin D, Wu X, et al. A virtual environment testbed for training laparoscopic surgical skills. Presence: Teleoperators Virt Environ 2000; 9: 236-55.10.1162/105474600566772Search in Google Scholar

33. Basdogan C, Ho CH, Srinivasan MA. Virtual environments in medical training: graphical and haptic simulation of laparoscopic common bile duct exploration. IEEE/ASME Trans Mechatronics 2001; 6: 269-85.10.1109/3516.951365Search in Google Scholar

34. Lorenz C, Krahnstover N. 3D statistical shape models for medical image segmentation. roceedings. 2nd International Conference on 3-D Digital Imaging and Modeling 1999: 414-23.Search in Google Scholar

35. Xie H, Qin H. Automatic knot determination of NURBS for interactive geometric design. IEEE International Conference on Shape Modeling and Applications 2001: 267-76.Search in Google Scholar

36. Fletcher P, Lu C, Pizer S, Joshi S. Principal geodesic analysis for the study of nonlinear statistics of shape. IEEE Trans Med Imaging 2004; 23: 995-1005.10.1109/TMI.2004.83179315338733Search in Google Scholar

37. Üzümcü M, Frangi AF, Sonka M, Reiber, Lelieveldt. ICA vs. PCA active appearance models: application to cardiac MR segmentation. Medical image computing and computer-assisted intervention. Proceedings. PT 1 Book series: Lecture notes in computer science 2003; 2878: 451-8.Search in Google Scholar