Automated dose verification in specialized radiotherapy (ADViSR): a tool for Monte Carlo based dose verification

Automation of Monte Carlo-based treatment plan verification for proton therapy

Purpose

Independent calculations of proton therapy plans are an important quality control procedure in treatment planning. When using custom Monte Carlo (MC) models of the beamline, deploying the calculations can be laborious, time consuming, and require in‐depth knowledge of the computational environment. We developed an automated framework to remove these barriers and integrate our MC model into the clinical workflow.

Materials and Methods

The Eclipse Scripting Application Programming Interface was used to initiate the automation process. A series of MATLAB scripts were then used for preprocessing of input data and postprocessing of results. Additional scripts were used to monitor the calculation process and appropriately deploy calculations to an institutional high‐performance computing facility. The automated framework and beamline models were validated against 160 patient specific QA measurements from an ionization chamber array and using a ±3%/3 mm gamma criteria.

Results

The automation reduced the human‐hours required to initiate and run a calculation to 1–2 min without leaving the treatment planning system environment. Validation comparisons had an average passing rate of 99.4% and were performed at depths ranging from 1 to 15 cm.

Conclusion

An automated framework for running MC calculations was developed which enables the calculation of dose and linear energy transfer within a clinically relevant workflow and timeline. The models and framework were validated against patient specific QA measurements and exhibited excellent agreement. Before this implementation, execution was prohibitively complex for an untrained individual and its use restricted to a research environment.

External beam patient dose verification based on the integral quality monitor (IQM®) output signals

BACKGROUND

The Integral Quality Monitor (IQM^®) can essentially measure the integral fluence through a segment and provide real-time information about the accuracy of radiation delivery based on comparisons of measured segment signals and pre-calculated reference values. However, the present IQM chamber cannot calculate the dose in the patient.

AIM

This study aims to make use of IQM field output signals to calculate the number of monitor units (MUs) delivered through an arbitrary treatment field in order to convert Monte Carlo (MC)-generated dose distributions in a patient model into absolute dose.

METHODS

XiO and Monaco treatment planning systems (TPSs) were used to define treatment beam portals for cervix and esophagus conformal radiotherapy as well as prostate intensity-modulated radiotherapy for the translation of patient and beam setup information from DICOM to DOSXYZnrc. The planned beams were simulated in a patient model built from actual patient CT images and each simulated integral field/segment was weighted with its MUs before summation to get the total dose in the plan. The segment beam weights (MUs) were calculated as the ratio of the open-field IQM measured signal and the calculated signal per MU extracted from chamber sensitivity maps. These are the actual MUs delivered not just MUs set. The beam weighting method was evaluated by comparing weighted MC doses with original planned doses using profile and isodose comparisons, dose difference maps, γ analysis and dose-volume histogram (DVH) data.

RESULTS

γ pass rates of up to 98% were found, except for the esophagus plan where the γ pass rate was below 45%. DVH comparisons showed good agreement for most organs, with the largest differences observed in low-density lung. However, these discrepancies can result from differences in dose calculation algorithms or differences in MUs used for dose weighting planned by the TPS and MUs calculated using IQM field output signals. To test this, a 4-field box DOSXYZnrc MC simulation weighted with planned (XiO) MUs was compared with the same simulation weighted with IQM-based MUs. Dose differences of up to 5% were found on the isocentre slice. For XiO versus MC, up to 7% dose differences were found, indicating additional error due to limitations of XiO's superposition algorithm. Dose differences between MC Monaco and MC EGSnrc were less than 3%.

CONCLUSIONS

The most valuable comparison was MC versus MC as it eliminated algorithm discrepancies and evaluated dose differences precisely according to beam weighting. For XiO TPS, care must be taken as dose differences may also arise due to limitations in XiO's planning software, not merely due to differences in MUs. Overall, the IQM was successfully used to compute beam dose weights to accurately reconstruct the patient dose using unweighted MC beams. Our technique can be used for pre-treatment QA provided each segment output is known and an accurate linac source model is available.