Premium
SU‐E‐T‐601: Patient Specific Margin Selection to Compensate for Intrafraction Motion during External Beam Radiation Therapy of the Lung
Author(s) -
Foster K,
Barnett R
Publication year - 2011
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3612563
Subject(s) - convolution (computer science) , breathing , standard deviation , imaging phantom , probability density function , physics , nuclear medicine , dosimetry , beam (structure) , optics , mathematics , computer science , statistics , medicine , artificial intelligence , artificial neural network , anatomy
Purpose: Attaining a positive outcome from external beam radiation therapy (RT) is heavily dependent on delivering the prescribed radiation dose to the intended structures. RT of lung cancers is complicated by target motion caused by the patientˈs natural breathing during treatment delivery, commonly known as ‘intrafraction motion’. This target motion must be managed to help ensure successful treatment. Methods: In order to accommodate target motion on a patient specific basis, we have employed a convolution model to predict the ‘blurred’ dose distribution delivered in the presence of known target motion. The convolution model requires two inputs: the planned ‘static’ dose distribution and a probability distribution function (PDF) describing the position of the target over the course of a breathing cycle. The model was experimentally verified by Gafchromic EBT2 film measurements. In a simulation study, 502 unique patient breathing traces were used to generate corresponding blurred dose distributions. Results: Analysis of the differences between the static and blurred dose distributions with respect to characteristics of the PDFs revealed strong trends between dose coverage metrics (percent mean dose difference and penumbral width) and features of the PDFs (amplitude, standard deviation, magnitude of maximum gradient and location of maximum gradient). In particular the magnitude of the maximum PDF gradient showed a clear inverse relationship with penumbral width, while the location of the maximum PDF gradient (measured relative to the PDFˈs geometric center) has a clear positive correlation with penumbral width. Conclusion: The convolution model has been verified for intrafraction target motion and analysis of changes present in the blurred dose distributions highlighted trends which can be used by physicians to guide target margin selection on a patient specific basis.