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

INTRODUCTION

This study is aimed at implementing novel tools for dose delivery real-time

verification

Materials

Monte Carlo differential dosimetry prediction tool

• Built on BEAMnrc and DOSXYZnrc

• Designed to validate the analytical model predictions

Generate 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

Methods

Bins may be smaller equal or bigger than MLC file segments

1. MLC file is broken into a number of bins

2. For each bin perform a simulation pair BEAM + DOSXYZ which generates the 3D dose distribution

3. Add up the 3D dose matrix contributed by all segments

4. Save results and clean-up

Underlying Idea

Four 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_0.3571

Seg0.5390_0.5455 Seg0.7338_0.7403

Dynamic 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 bigger

Stuffed Static Segmented

Num. bins = 1000

Num. 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 patterns

Seg0.0000 Seg0.0065 Seg1.000

Example: MLC file made up of the first two originally consecutive segment_0.0000 andsegment_0.0065

Intermediate 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 Progress

Computation 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