AbstractID: 9430 Title: Application of Monte Carlo based IMRT inverse planning in
clinical reality
Monte Carlo (MC) based inverse planning optimizes the beamlet intensities based on the MC calculated beamlet dose. This planning
approach in theory minimizes the introduction of systematic and convergence errors to treatment plans. This work applies a MC based
inverse planning technique in clinical reality. Voxels of 0.3x0.3x0.3 cm3 were used to represent the 3D patient volume with a beamlet
size of 1x1 cm2. The dose distribution of individual beamlet was calculated using the MCNP (Monte Carlo N-Particle) Monte Carlo
code. The beamlet intensities were optimized using an evolutionary programming algorithm. The treatment plans at the sites of spine,
pancreas and lung were respectively generated. Each plan contained 5 to 7 beam angles with 70 to 148 beamlets at each angle. The
time spent on calculating all beamlets of individual beam angle was between 133 and 272 minutes, while 10 to 90 minutes were spent
on the intensity optimization. The clinical benefit of the MC technique and the difficulties of a non-MC one were demonstrated
through comparing the generated plan of the spine case with the plan produced by the Corvus system that uses a pencil beam
algorithm. Currently, the lengthy beamlet dose calculation time limits the feasibility of the MC technique in planning the volume of
cases in routine practice. However, the MC technique is beneficial to the cases in which existing tissue heterogeneity is adverse to the
dose computation accuracy and a reduction in the systematic and/or convergence errors can make a clinical difference.