How extensive of a 4D dataset is needed to estimate cumulative dose distribution plan evaluation metrics in conformal lung therapy?

Medical Physics

Volumn 34, Issue 1, 2007, Pages 233-245

  Rosu, Mihaela ^a   Balter, James M ^a   Chetty, Indrin J ^a,c   Kessler, Marc L ^a   McShan, Daniel L ^a   Balter, Peter ^b   Ten Haken, Randall K ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

^b UNIVERSITY OF TEXAS   (United States)

^c UNIVERSITY OF NEBRASKA MEDICAL CENTER   (United States)

Author keywords

4D datasets; 4D treatment planning; Monte Carlo; Organ deformation

Indexed keywords

BIOLOGICAL ORGANS; COMPUTERIZED TOMOGRAPHY; CONFORMAL MAPPING; DEFORMATION; LINEAR TRANSFORMATIONS; MONTE CARLO METHODS; PETROLEUM RESERVOIR EVALUATION; POINT CONTACTS;

4D DATASETS; COMPUTED TOMOGRAPHY IMAGES; CONFORMAL RADIOTHERAPY; NORMAL TISSUE COMPLICATION PROBABILITIES; ORGAN DEFORMATION; SATISFACTORY PREDICTIONS; TRANSFORMATION MATRICES; TREATMENT PLANNING;

PROBABILITY DISTRIBUTIONS;

ANATOMY; ARTICLE; BREATHING RATE; CLINICAL ARTICLE; COMPUTER ASSISTED RADIOTHERAPY; COMPUTER ASSISTED TOMOGRAPHY; CONTROLLED STUDY; DOSIMETRY; EXHALATION; HISTOGRAM; HUMAN; INHALATION; LUNG; LUNG CANCER; PHOTON; PREDICTION; PRIORITY JOURNAL; PROBABILITY; RADIATION DOSE; RADIATION DOSE DISTRIBUTION; SCORING SYSTEM; THORAX DEFORMITY;

EID: 33845951339     PISSN: 00942405     EISSN: None     Source Type: Journal    
DOI: 10.1118/1.2400624     Document Type: Article

References (40)

1. J. M. Balter, " Uncertainties in CT-based radiation therapy treatment planning associated with patient breathing., " Int. J. Radiat. Oncol., Biol., Phys. 36, 167-174 (1996).
2. S. Shimizu, " Impact of respiratory movement on the computed tomographic images of small lung tumors in three-dimensional (3D) radiotherapy., " Int. J. Radiat. Oncol., Biol., Phys. 46, 1127-1133 (2000).
3. J. Wong, " The use of active breathing control (ABC) to reduce margin for breathing motion., " Int. J. Radiat. Oncol., Biol., Phys. 44, 911-999 (1999).
4. L. A. Dawson, " The reproducibility of organ position using active breathing control (ABC) during liver radiotherapy., " Int. J. Radiat. Oncol., Biol., Phys. 51, 1410-1421 (2001).
5. J. Hanley, " Deep inspiration breath-hold technique for lung tumors: The potential value of target immobilization and reduced lung density in dose escalation., " Int. J. Radiat. Oncol., Biol., Phys. 45, 603-611 (1999).
6. K. E. Rosenzweig, " The deep inspiration breath-hold technique in the treatment of inoperable non-small-cell lung cancer., " Int. J. Radiat. Oncol., Biol., Phys., 48, 81-87 (2000).
7. D. Mah, " Technical aspects of the deep inspiration breath-hold technique in the treatment of thoracic cancer., " Int. J. Radiat. Oncol., Biol., Phys. 48, 1175-1185 (2000).
8. S. S. Vedam, " Acquiring a four-dimensional computed tomography dataset using an external respiratory signal., " Phys. Med. Biol. 48, 45-62 (2003).
9. D. Low, A method for the reconstruction of four-dimensional synchronized CT scans acquired during free breathing., Med. Phys. 30, 1254-1263 (2003).
10. T. Pan, " 4D-CT imaging of a volume influenced by respiratory motion on multi-slice CT., " Med. Phys. 31, 333-340 (2004).
11. P. J. Keall, " Time-The fourth dimension in radiotherapy (ASTRO Panel Discussion)., " Int. J. Radiat. Oncol., Biol., Phys., 57, S8-9 (2003).
12. G. C. Sharp, " Prediction of respiratory tumour motion for real-time image-guided radiotherapy., " Phys. Med. Biol. 49, 425-440 (2004).
13. S. S. Vedam, " Predicting respiratory motion for four-dimensional radiotherapy., " Med. Phys. 31, 2274-2283 (2004).
14. A. E. Lujan, " A method for incorporating organ motion due to breathing into 3D dose calculations., " Med. Phys. 26, 715-720 (1999).
15. M. Unser, " Splines: a perfect fit for signal and image processing., " IEEE Signal Process. Mag. 16, 22-38 (1999).
16. M. L. Kessler and M. Roberson, " Image registration and data fusion for radiotherapy treatment planning., " in New Technologies in Radiation, edited by, W. Schlegel,,, T. Bortfeld,, and, A. L. Grosu, (Springer-Verlag, New York, 2005), pp. 41-52.
17. M. L. Kessler, " Deformable image registration using multiresolution B-splines., " Med. Phys. 31, 1792 (2004).
18. J. Nocedal and S. Wright, Numerical Optimization (Springer-Verlag, New York, 1999).
19. M. J. Fitzpatrick, " Displacement-based binning of time-dependent computed tomography image data sets., " Med. Phys. 33, 235-246 (2006).
20. N. Wink, C. Panknin, and T. D. Solberg, " Phase versus amplitude sorting of 4D-CT data., " J. Appl. Clin. Med. Phys. 7, 77-85 (2006).
21. E. Schreibmann, G. T. Chen, and L. Xing, " Image interpolation in 4D CT using a B-Spline deformable registration model., " Int. J. Radiat. Oncol., Biol., Phys. 64, 1537-1550 (2006).
22. J. Sempau, S. J. Wilderman, and A. F. Bielajew, " DPM, a fast, accurate Monte Carlo code optimized for photon and electron radiotherapy treatment planning dose calculations., " Phys. Med. Biol. 45, 2263-2291 (2000).
23. I. J. Chetty, " Photon beam relative dose validation of the DPM Monte Carlo code in lung-equivalent media., " Med. Phys. 30, 563-573 (2003).
24. I. J. Chetty, " Reporting and analyzing statistical uncertainties in Monte Carlo-based treatment planning., " Int. J. Radiat. Oncol., Biol., Phys. 65, 1249-1259 (2006).
25. M. Rosu, " Dose reconstruction in deforming lung anatomy: Dose grid size effects and clinical implications., " Med. Phys. 32, 2487-2495 (2005).
26. A. Niemierko, " A generalized concept of equivalent uniform dose (EUD)., " Med. Phys. 26, 1100 (1999).
27. J. Lyman, " Complication probability as assessed from dose volume histograms., " Radiat. Res. 104, S13-19 (1985).
28. G. J. Kutcher and C. Burman, " Calculation of complication probability factors for non-uniform normal tissue irradiation: The effective volume method., " Int. J. Radiat. Oncol., Biol., Phys. 16, 1623-1630 (1989).
29. Y. Seppenwoolde, " Comparing different NTCP models that predict the incidence of radiation pneumonitis. Normal tissue complication probability., " Int. J. Radiat. Oncol., Biol., Phys. 55, 724-735 (2003).
30. Y. Seppenwoolde, " Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy., " Int. J. Radiat. Oncol., Biol., Phys. 53, 822-834 (2002).
31. M. Rosu, " The impact of 4D breathing motion effects versus tissue heterogeneity in lung cancer treatment planning., " Med. Phys. 33, 2232 (2006).
32. S. Flampouri, " Estimation of the delivered patient dose in lung IMRT treatment based on deformable registration of 4D-CT data and Monte Carlo simulations., " Phys. Med. Biol. 51, 2763-2779 (2006).
33. M. Engelsman, " The effect of breathing and set-up errors on the cumulative dose to a lung tumor., " Radiother. Oncol. 60, 95-105 (2001).
34. A. E. Lujan, J. M. Balter, and R. K. Ten Haken, " A method for incorporating organ motion due to breathing into 3D dose calculations: Sensitivity to variations in motion., " Med. Phys. 30, 2643-2649 (2003).
35. L. Kestin, " Breathing variation during thoracic radiations for adaptive radiotherapy of lung cancer., " Int. J. Radiat. Oncol., Biol., Phys. 60, S610 (2004).
36. M. J. Murphy, " The effectiveness of breath-holding to stabilize lung and pancreas tumors during radiosurgery., " Int. J. Radiat. Oncol., Biol., Phys. 53, 475-482 (2002).
37. D. Yan, " Target margin for respiratory motion: Optimal patient/organ positioning for treatment planning is critical., " Radiother. Oncol. 64, Supplement 1, S259-S260 (2002).
38. J. Liang, " Minimization of target margin by adapting treatment planning to target respiratory motion., " Int. J. Radiat. Oncol., Biol., Phys., Suppl. 57, Suppl. 1, S233-S234 (2003).
39. J. W. Wolthaus, " Mid-ventilation CT scan construction from four-dimensional respiration-correlated CT scans for radiotherapy planning of lung cancer patients., " Int. J. Radiat. Oncol., Biol., Phys. 65, 1560-1571 (2006).
40. M. Schwarz, " Impact of geometrical uncertainties on 3D CRT and IMRT dose distributions for lung cancer treatment., " Int. J. Radiat. Oncol., Biol., Phys. 65, 1260-1269 (2006).