A DICOM-RT-based toolbox for the evaluation and verification of radiotherapy plans

Development and commissioning of a multileaf collimator model in Monte Carlo dose calculations for intensity-modulated radiation therapy

A multileaf collimator (MLC) model, "MATMLC," was developed to simulate MLCs for Monte Carlo (MC) dose calculations of intensity-modulated radiation therapy (IMRT). This model describes MLCs using matrices of regions, each of which can be independently defined for its material and geometry, allowing flexibility in simulating MLCs from various manufacturers. The free parameters relevant to the dose calculations with this MLC model included MLC leaf density, interleaf air gap, and leaf geometry. To commission the MLC model and its free parameters for the Varian Millennium MLC-120 (Varian Oncology Systems, Palo Alto, CA), we used the following leaf patterns: (1) MLC-blocked fields to test the effects of leaf transmission and leakage; (2) picket-fence fields to test the effects of the interleaf air gap and tongue-groove design; and (3) abutting-gap fields to test the effects of rounded leaf ends. Transmission ratios and intensity maps for these leaf patterns were calculated with various sets of modeling parameters to determine their dosimetric effects, sensitivities, and their optimal combinations to give the closest agreement with measured results. Upon commissioning the MLC model, we computed dose distributions for clinical IMRT plans using the MC system and verified the results with those from ion chamber and thermoluminescent dosimeter measurements in water phantoms and anthropomorphic phantoms. This study showed that the MLC transmission ratios were strongly dependent on both leaf density and the interleaf air gap. The effect of interleaf air gap and tongue-groove geometry can be determined most effectively through fence-type MLC patterns. Using the commissioned MLC model, we found that the calculated dose from the MC system agreed with the measured data within clinically acceptable criteria from low- to high-dose regions, showing that the model is acceptable for clinical applications.

Absolute dose calculations for Monte Carlo simulations of radiotherapy beams

Monte Carlo (MC) simulations have traditionally been used for single field relative comparisons with experimental data or commercial treatment planning systems (TPS). However, clinical treatment plans commonly involve more than one field. Since the contribution of each field must be accurately quantified, multiple field MC simulations are only possible by employing absolute dosimetry. Therefore, we have developed a rigorous calibration method that allows the incorporation of monitor units (MU) in MC simulations. This absolute dosimetry formalism can be easily implemented by any BEAMnrc/DOSXYZnrc user, and applies to any configuration of open and blocked fields, including intensity-modulated radiation therapy (IMRT) plans. Our approach involves the relationship between the dose scored in the monitor ionization chamber of a radiotherapy linear accelerator (linac), the number of initial particles incident on the target, and the field size. We found that for a 10 x 10 cm2 field of a 6 MV photon beam, 1 MU corresponds, in our model, to 8.129 x 10(13) +/- 1.0% electrons incident on the target and a total dose of 20.87 cGy +/- 1.0% in the monitor chambers of the virtual linac. We present an extensive experimental verification of our MC results for open and intensity-modulated fields, including a dynamic 7-field IMRT plan simulated on the CT data sets of a cylindrical phantom and of a Rando anthropomorphic phantom, which were validated by measurements using ionization chambers and thermoluminescent dosimeters (TLD). Our simulation results are in excellent agreement with experiment, with percentage differences of less than 2%, in general, demonstrating the accuracy of our Monte Carlo absolute dose calculations.

CERR: a computational environment for radiotherapy research

A software environment is described, called the computational environment for radiotherapy research (CERR, pronounced "sir"). CERR partially addresses four broad needs in treatment planning research: (a) it provides a convenient and powerful software environment to develop and prototype treatment planning concepts, (b) it serves as a software integration environment to combine treatment planning software written in multiple languages (MATLAB, FORTRAN, C/C++, JAVA, etc.), together with treatment plan information (computed tomography scans, outlined structures, dose distributions, digital films, etc.), (c) it provides the ability to extract treatment plans from disparate planning systems using the widely available AAPM/RTOG archiving mechanism, and (d) it provides a convenient and powerful tool for sharing and reproducing treatment planning research results. The functional components currently being distributed, including source code, include: (1) an import program which converts the widely available AAPM/RTOG treatment planning format into a MATLAB cell-array data object, facilitating manipulation; (2) viewers which display axial, coronal, and sagittal computed tomography images, structure contours, digital films, and isodose lines or dose colorwash, (3) a suite of contouring tools to edit and/or create anatomical structures, (4) dose-volume and dose-surface histogram calculation and display tools, and (5) various predefined commands. CERR allows the user to retrieve any AAPM/RTOG key word information about the treatment plan archive. The code is relatively self-describing, because it relies on MATLAB structure field name definitions based on the AAPM/RTOG standard. New structure field names can be added dynamically or permanently. New components of arbitrary data type can be stored and accessed without disturbing system operation. CERR has been applied to aid research in dose-volume-outcome modeling, Monte Carlo dose calculation, and treatment planning optimization. In summary, CERR provides a powerful, convenient, and common framework which allows researchers to use common patient data sets, and compare and share research results.