Premium
WE‐E‐AUD A‐07: Should a Gaussian Probability Density Function Be Used to Approximate Respiration Induced Dosimetric Effects for Proton Radiotherapy?
Author(s) -
Zhao L,
Sandison G,
Farr J,
Li X
Publication year - 2008
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 - 10.1118/1.2962774
Subject(s) - imaging phantom , proton therapy , dosimetry , nuclear medicine , probability density function , radiation therapy , centroid , radiation treatment planning , physics , convolution (computer science) , standard deviation , proton , mathematics , medicine , statistics , computer science , nuclear physics , artificial intelligence , radiology , geometry , artificial neural network
Purpose: To compare the dose distributions generated by convolving a static dose distribution using a patient specific respiratory probability density function (R‐PDF) with those generated using a generic Gaussian PDF (G‐PDF) for proton therapy of lung cancer. Method and Materials: The R‐PDFs were obtained by identifying the centroid motion of the targets from the 4D‐CT scans of a phantom (CIRS Model 008 Dynamic Thorax) and a representative lung cancer patient. The CMS XiO® Treatment Planning System commissioned with 208 MeV nominal proton beam data from a passive scattering beam line at a proton therapy center was used for the static dose calculation. The dose convolution results from four different G‐PDFs with standard deviations (SD) of 0.2, 0.3, 0.4, and 0.5 multiplying by the peak‐to‐peak motion amplitude (letter “A”, 1.60cm in the phantom and 1.75cm in the patient) were compared to the R‐PDF convolved dose distributions using a commercial dosimetry analysis package (OmniPro I'mRT). Results: Respiration‐induced dose error was 29% and 16% of the prescribed dose (PD) compared to the static doses in the phantom and patient, respectively. The G‐PDF with SD of 0.4A most closely approximates the R‐PDF whilst the maximum dose disagreements (MDDs) between the convolved doses using the two methods were 6% and 4% of the PD in phantom and patient, respectively. When G‐PDFs with SD of 0.2A and 0.5A were used to approximate the R‐PDF, the resulting MDDs were 19% and 12% in the phantom respectively, and 12% and 10% in the patient, respectively. When the G‐PDF with SD of 0.3A was used to approximate the R‐PDF, the resulting MDDs were 10% in the phantom and 8% in the patient. Conclusion: A Gaussian function should not be used to approximate a patient specific respiratory PDF since it can lead to clinically significant dose errors.