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Models for breathing trajectory variations

MASSACHUSETTS GENERAL HOSPITAL. RADIATION ONCOLOGY. Models for breathing trajectory variations. Gregory C. Sharp Massachusetts General Hospital Feb 19, 2010. Problem statement. How should we incorporate breathing trajectory variations into 4-D planning ?. Problem statement.

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Models for breathing trajectory variations

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  1. MASSACHUSETTSGENERAL HOSPITAL RADIATION ONCOLOGY Models for breathingtrajectory variations Gregory C. SharpMassachusetts General HospitalFeb 19, 2010

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

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

  4. Outline • Dosimetry model • Motion model • Population model

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

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

  7. Dosimetry model

  8. Dosimetry model

  9. Dosimetry model

  10. Dosimetry model • Geometric dose model doesn’t work for protons

  11. Dosimetry model • Because of range effects

  12. Dosimetry model • Modified geometric dose model • Use radiological depth instead of position

  13. Dosimetry model • Radiological depth of anatomic points are assumed constant

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

  15. Motion model • Primary trajectory: from 4D-CT

  16. Motion model • Trajectory variations: position change / time

  17. Motion model • Motion model = primary + variations

  18. Motion model • Variations have a probability distribution

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

  20. Motion model

  21. Motion model

  22. Motion model • Trajectory variation histogram is applied to each phase separately

  23. Motion model

  24. Motion model • Caveats: • No “interplay” effect (beams delivered in sequence) • Amplitude variations neglected

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

  26. 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)

  27. Population model (1/4)

  28. Population model (1/4)

  29. Population model (1/4)

  30. Population model (1/4) Drift Magnitude * Take with a grain of salt

  31. 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)

  32. Lateral View Vertebral landmark Maximum of Diaphragm

  33. Population model (2/4) • This study • Median s = 0.55 cm • Yorke (JACMP ‘2005) • m = 0.63 cm • Mean s = 0.42 cm

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

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

  36. Clip 1 Clip 2 Clip 3 RPM

  37. CLIP #2: Exhale baseline drift SI = 5 mm LR = 2 mm 90 secs 20 secs 80 secs AP = 2 mm 4 minutes

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

  39. 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)

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

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

  42. END OF SLIDE SHOW

  43. Motion model • Dosimetry can be either probabilistic or deterministic +

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