Simulating Differential Dosimetry

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Simulating Differential Dosimetry. M. E. Monville1, Z. Kuncic2,3,4, C. Riveros1, P. B.Greer1,5 (1)University of Newcastle, (2) Institute of Medical Physics, (3) School of Physics, (4) University of Sydney, (5) Calvary Mater Hospital. INTRODUCTION. - PowerPoint PPT Presentation

Text of Simulating Differential Dosimetry

  • Simulating Differential Dosimetry

    M. E. Monville1, Z. Kuncic2,3,4, C. Riveros1, P. B.Greer1,5(1)University of Newcastle, (2) Institute of Medical Physics, (3) School of Physics, (4) University of Sydney, (5) Calvary Mater Hospital

  • PROGRAM OBJECTIVE Predict time resolved dose during treatment

    Prompt remedial actions

    Spare patients from error consequences PROGRAM OUTLINE Analytical model forward prediction Real time comparison of delivered dose against analytical calculated dose

    Stochastic model Off-line comparison of analytical calculated dose against stochastic predicted dose INTRODUCTIONThis study is aimed at implementing novel tools for dose delivery real-time verification

  • MaterialsMonte Carlo differential dosimetry prediction tool Built on BEAMnrc and DOSXYZnrc Designed to validate the analytical model predictionsGenerate the phase-space file input to DOSXYZ Uses variance reduction techniques Directional Bremsstrhalung Splitting Bremsstrahlung Cross-Section Enhancement Simulate primary and secondary particles interaction with the Linac head components

    BEAMnrc Transport particles from the phase-space file through the phantom Simulate primary and secondary particles interaction with the phantom Generate the 3D dose distribution in the phantom Use variance reduction techniques 2000 photon splitting 2000 charged particles splitting DOXYZnrc

  • MethodsBins may be smaller equal or bigger than MLC file segmentsMLC file is broken into a number of binsFor each bin perform a simulation pair BEAM + DOSXYZ which generates the 3D dose distributionAdd up the 3D dose matrix contributed by all segmentsSave results and clean-upUnderlying IdeaFour Methods to Break Mlc File into Bins Dynamic Full MLC Use custom BEAM version

    Static Segmented Use standard BEAM

    Stuffed Static Segmented Use standard BEAM

    Dynamic Segmented Use standard BEAM

  • For each bin run a dynamic BEAM simulation using the whole MLC file. Leaves pattern constrained to span only the bin width by drawing random numbers from the bin widthMLC file indices sequence is the CDF for the MLC leaf positions probability A random number uniformly distributed in [0,1] uniquely identifies a segment through inversion of the CDF

    The leaf positions at the random point are obtained through linear interpolation of known leaf positions at the nearest control points Seg0.1299_0.1364Seg0.3506_0.3571Seg0.5390_0.5455Seg0.7338_0.7403Dynamic Full MLC

  • Static SegmentedFor each segment run static BEAM simulation using the segment leaves pattern. The cumulative dose contributed by all segments is approximated by the staircase pattern which assumes the leaf positions are kept constant over the segment. Conceptually similar to the approximation of a Riemann integral through a Riemann sum

    Num. bins = 10

  • Similar to Static SegmentedThe MLC file is enriched with an arbitrary number of fictitious segments whose leaf positions are obtained through linear interpolation of leaf positions of original adjacent segments.The limit of the Riemann sum approaches the integral value as the number of bins grows bigger likewise the staircase cumulative dose approaches the total delivered dose as the number of segments grows biggerStuffed Static SegmentedNum. bins = 1000Num. bins = 100

  • Dynamic SegmentedThe N-segment MLC file is broken into N-1 MLC files containing only two consecutive segments whose indices are set respectively to 0 and 1Run a dynamic BEAM simulation for each 2-segment MLC file the leaf positions is dynamically computed by interpolation of the 2-segment leaf patternsSeg0.0000Seg0.0065 Seg1.000 Example: MLC file made up of the first two originally consecutive segment_0.0000 andsegment_0.0065Intermediate interpolated leaf positions

  • Validation ResultsCompute the cumulative dose by adding up the dose contributed by each segment Monte Carlo Validation Compare the cumulative dose against the dose from a standard dynamic simulation Measurements Validation Compare the cumulative dose against the calibrated Epid measurements

    left side: Standard dynamic simulation

    right-side: Dynamic Full MLC method using variance-reduction DBS & BCSE Dynamic Full MLC simulation Cumulative dose contributed by 155 segments

  • Conclusion Our novel tool is fully automated

    It is designed to run in modern distributed calculus computer clusters with optimum usage of the available computational power

    Two methods are still in the Monte Carlo framework validation stage

    Work in ProgressComputation Improvements We are changing the process scheduling scheme to port our tool to small cluster systems We plan to incorporate parallel BEAM and DOSXYZ into our tool

    Application extensions We need to incorporate the Step-and-Shoot radiation delivery procedure We need to deal with delivery circumstances when the dose rate is not constant like Linac beam hold-offs