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

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

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

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

Outline Dosimetry model Motion model Population model

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

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

Dosimetry model

Geometric dose model doesn’t work for protons

Dosimetry model Because of range effects

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

Dosimetry model Radiological depth of anatomic points are assumed constant

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

Motion model Primary trajectory: from 4D-CT

Motion model Trajectory variations: position change / time

Motion model Motion model = primary + variations

Motion model Variations have a probability distribution

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

Motion model

Trajectory variation histogram is applied to each phase separately

Motion model

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

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

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)

Population model (1/4)

* Take with a grain of salt Drift Magnitude

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)

Maximum of Diaphragm Vertebral landmark Lateral View

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

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

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

Clip 1 Clip 2 Clip 3 RPM

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

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

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)

Population model * Take with a grain of salt Drift Magnitude

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

END OF SLIDE SHOW

Motion model Dosimetry can be either probabilistic or deterministic +

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