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Conventional IMRT dose verification using film and ion chamber measurements is useful but limited with respect to the actual dose distribution received by the patient. The Monte Carlo simulation has been introduced as an independent dose verification tool for IMRT using the patient CT data and MLC leaf sequence files, which validates the dose calculation accuracy but not the plan delivery accuracy. In this work, we propose a Monte Carlo based IMRT dose verification method that reconstructs the patient dose distribution using the patient CT, actual beam data based on the information from the record and verify system (R/V), and the MLC log files obtained during dose delivery that record the MLC leaf positions and MUs delivered. Comparing the Monte Carlo dose calculation with the original IMRT plan using these data simultaneously validates the accuracy of both the IMRT dose calculation and beam delivery. Such log file based Monte Carlo simulations are expected to be employed as a useful and efficient IMRT QA modality to validate the dose delivered to the patient. We have run Monte Carlo simulations for eight IMRT prostate plans using this method and the results for the target dose were consistent with the original CORVUS treatment plans to within 3.0% and 2.0% with and without heterogeneity corrections in the dose calculation. However, significant dose deviations in nearby critical structures have been observed. The results showed that up to 9.0% of the bladder dose and up to 38.0% of the rectum dose, to which leaf position errors were found to contribute <2%, were underestimated by the CORVUS treatment planning system. The concept of average leaf position error has been defined to analyze MLC leaf position errors for an IMRT plan. A linear correlation between the target dose error and the average position error has been found based on log file based Monte Carlo simulations, showing that an average position error of 0.2 mm can result in a target dose error of about 1.0%.

Intensity-modulated beam profiles are generated by an inverse planning or optimization algorithm, a process that, being computationally complex and intensive, is inherently susceptible to noise and numerical artifacts. These artifacts make delivery of the beams more difficult, oftentimes for little, if any, observable improvement in the dose distributions. In this work we examine two approaches for smoothing the beam profiles. The first approach is to smooth the beam profiles subsequent to each iteration in the optimization process (method A). The second approach is to include a term within the objective function that specifies the smoothness of the profiles as an optimization criterion (method B). The two methods were applied to a phantom study as well as three clinical sites: paraspinal, nasopharynx, and prostate. For the paraspinal and nasopharynx cases, which have critical organs with low tolerance doses in close proximity, method B produced sharper dose gradients, better target dose homogeneity, and more critical organ sparing. In the less demanding prostate case, the two methods give similar results. In addition, method B is more efficient during optimization, requiring fewer iterations, but less efficient during DMLC delivery, requiring a longer beam-on time.

The purpose of this work is to model electron contamination in clinical photon beams and to commission the source model using measured data for Monte Carlo treatment planning. In this work, a planar source is used to represent the contaminant electrons at a plane above the upper jaws. The source size depends on the dimensions of the field size at the isocentre. The energy spectra of the contaminant electrons are predetermined using Monte Carlo simulations for photon beams from different clinical accelerators. A 'random creep' method is employed to derive the weight of the electron contamination source by matching Monte Carlo calculated monoenergetic photon and electron percent depth-dose (PDD) curves with measured PDD curves. We have integrated this electron contamination source into a previously developed multiple source model and validated the model for photon beams from Siemens PRIMUS accelerators. The EGS4 based Monte Carlo user code BEAM and MCSIM were used for linac head sinulation and dose calculation. The Monte Carlo calculated dose distributions were compared with measured data. Our results showed good agreement (less than 2% or 2 mm) for 6, 10 and 18 MV photon beams.