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Incorporating Breathing Trajectory Variations for Radiation Oncology Planning

Explore models for addressing breathing trajectory variations in 4-D planning, including dosimetry, motion, and population statistics. These models help compute dose to moving targets without CT scans and account for trajectory variations with probabilistic integration.

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Incorporating Breathing Trajectory Variations for Radiation Oncology Planning

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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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