2. Models for breathing trajectory variations Gregory C. Sharp Massachusetts General Hospital Feb 19, 2010 MASSACHUSETTS GENERAL HOSPITAL RADIATION ONCOLOGY

3. Problem statement How should we incorporate breathing trajectory variations into 4-D planning ?

4. Problem statement Primary trajectory is volumetric –4D-CT Trajectory variations are non-volumetric –Implanted fiducials –Radiography and fluoroscopy –Electromagnetic transponders –Population statistics

5. Outline Dosimetry model Motion model Population model

6. Dosimetry model Problem statement: How to compute dose to a moving target if we don’t have a CT?

7. Dosimetry model Answer: “Geometric dose model” Dose is fixed in space Target moves within dose cloud

8. Dosimetry model

11. Geometric dose model doesn’t work for protons

12. Dosimetry model Because of range effects

13. Dosimetry model Modified geometric dose model –Use radiological depth instead of position

14. Dosimetry model Radiological depth of anatomic points are assumed constant

15. Dosimetry model Modified geometric model –Treat each beam separately –Project 3D trajectory to 2D –Could be used for photons as well

16. Motion model Primary trajectory: from 4D-CT

17. Motion model Trajectory variations: position change / time

18. Motion model Motion model = primary + variations

19. Motion model Variations have a probability distribution

20. Motion model Integration over known variation curve yields specific histogram of displacements

21. Motion model

23. Trajectory variation histogram is applied to each phase separately

24. Motion model

25. Caveats: –No “interplay” effect (beams delivered in sequence) –Amplitude variations neglected

26. Population model Data sources –Hokkaido RTRT –IRIS radiographic –IRIS fluoro burst –SBRT CBCT (pre/post)

27. Population model (1/4) Hokkaido RTRT –~20 lung cancer patients –Hypofractionated (early stage) –Orthogonal stereo fluoroscopy –Gated treatment –Mixed motion amplitudes (up to 30 mm)

28. Population model (1/4)

31. * Take with a grain of salt Drift Magnitude

32. Population model (2/4) IRIS Radiographs –10 lung cancer patients –Standard fractionation (esp. stage III) –Orthogonal gated radiographs (exhale) –Gated RT –Large motion amplitudes (> 10 mm motion)

33. Maximum of Diaphragm Vertebral landmark Lateral View

35. Population model (2/4) This study – Median  = 0.55 cm Yorke (JACMP ‘2005) –  = 0.63 cm – Mean  = 0.42 cm

37. Population model (2/4) * Take with a grain of salt Drift Magnitude

38. Population model (3/4) IRIS Fluoro –4 liver cancer patients –Orthogonal fluoroscopy –Gated RT –Large motion amplitudes (> 10 mm)

40. Clip 1 Clip 2 Clip 3 RPM

41. SI = 5 mm AP = 2 mm LR = 2 mm 90 secs20 secs80 secs 4 minutes CLIP #2: Exhale baseline drift

42. Population model (3/4) * Take with a grain of salt Drift Magnitude

43. Population model (4/4) SBRT CBCT –~15 lung cancer patients –Hypofractionated (early stage) –Pre-tx and post-tx CBCT –SBRT –Mixed motion amplitudes (range unknown)

44. Population model * Take with a grain of salt Drift Magnitude

45. Summary Dosimetry model –Geometric model –Modified geometric model Motion model –Motion = primary + variations –Motion variations map to dose variation Population model –WIP

46. END OF SLIDE SHOW

47. Motion model Dosimetry can be either probabilistic or deterministic +