Program Information
Modelling of Dose Deposition by Convolution Algorithm for Stereotactic Radiation Therapy QA
S Chan1* , M Cheung1 , D Li1 , L Lee1 , L Xing3 , A Chan1,2 , (1) Department of Clincial Oncology, Prince of Wales Hospital, Hong Kong SAR, China, (2) State Key Laboratory of Oncology in South China, The Chinese University of Hong Kong, Hong Kong SAR, China, (3) Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California 94305, USA
Presentations
MO-RPM-GePD-T-6 (Monday, July 31, 2017) 3:45 PM - 4:15 PM Room: Therapy ePoster Lounge
Purpose: To establish a model for pre-treatment verification of Stereotactic Radiation Therapy (SRT) plans and to calculate the dose deposition by convolution algorithm.
Methods: The SRT plan was generated in BrainLab iPlanNet TPS and the beam parameters such as MLC positions and dose index were exported for modelling. For each beam, the modelling consisted of the stimulation of the beamlet map, the conversion of the beamlet map into the fluence map at 5cm of water, and the computation of the dose plane at 5cm depth of water from the fluence map. The fluence map was generated by applying an intensity profile as a correction map to the beamlet map and ultimately convolved with a kernel to compute the dose plane. The SRT plan was also mapped to a square water phantom (30×30×10cm³) in the TPS and the dose planes were exported for pre-treatment verification. Both the image subtraction and the correlation coefficient were calculated to quantify the difference and the correlation respectively between the calculated dose plane and the planned one. Their central axis (CAX) doses were also reported and the percentage discrepancies were calculated for reference. Ten SRT plans were selected for the study and mapped to the square water phantom. All the computation work was performed in Matlab.
Results: In all selected cases, the calculated dose planes are in a good agreement with the planned ones. Their correlation coefficients are found to be over 0.99 which indicate a good modelling of the dose deposition. Their CAX doses are also computed with less than 1% discrepancy with respect to total dose. The computation takes less than 5 seconds for a typical 7-field plan and the pre-treatment verification is done effectively.
Conclusion: A model is established for pre-treatment verification of SRT plans and the dose deposition by convolution is implemented.
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