Range and modulation dependencies for proton beam dose per monitor unit calculations

A generalized model for monitor units determination in ocular proton therapy using machine learning: A proof-of-concept study

Objective.Determining and verifying the number of monitor units is crucial to achieving the desired dose distribution in radiotherapy and maintaining treatment efficacy. However, current commercial treatment planning system(s) dedicated to ocular passive eyelines in proton therapy do not provide the number of monitor units for patient-specific plan delivery. Performing specific pre-treatment field measurements, which is time and resource consuming, is usually gold-standard practice. This proof-of-concept study reports on the development of a multi-institutional-based generalized model for monitor units determination in proton therapy for eye melanoma treatments.Approach.To cope with the small number of patients being treated in proton centers, three European institutes participated in this study. Measurements data were collected to address output factor differences across the institutes, especially as function of field size, spread-out Bragg peak modulation width, residual range, and air gap. A generic model for monitor units prediction using a large number of 3748 patients and broad diversity in tumor patterns, was evaluated using six popular machine learning algorithms: (i) decision tree; (ii) random forest, (iii) extra trees, (iv) K-nearest neighbors, (v) gradient boosting, and (vi) the support vector regression. Features used as inputs into each machine learning pipeline were: Spread-out Bragg peak width, range, air gap, fraction and calibration doses. Performance measure was scored using the mean absolute error, which was the difference between predicted and real monitor units, as collected from institutional gold-standard methods.Main results.Predictions across algorithms were accurate within 3% uncertainty for up to 85.2% of the plans and within 10% uncertainty for up to 98.6% of the plans with the extra trees algorithm.Significance.A proof-of-concept of using machine learning-based generic monitor units determination in ocular proton therapy has been demonstrated. This could trigger the development of an independent monitor units calculation tool for clinical use.

Simulation study of proton arc therapy with the compact single-room MEVION-S250 proton therapy system

Aim:

The purpose of this study is to investigate the feasibility of proton arc therapy (PAT) using the double-scattering MEVION-S250 proton system. The treatment planning and dose delivery parameters from PAT were compared with conventional treatment planning techniques.

Materials and methods:

PAT was simulated with multiple conformal and fixed-aperture beams (5–15) using the MEVION-S250-double-scattering proton system. Conformal apertures were simulated with the Eclipse-treatment-planning system: (a) using a static single aperture that provides the best average conformal circular or rectangular apertures to cover the tumour from different angular views (SPAT), and (b) dynamic conformal apertures of the tumour shape at each irradiation angle that can be obtained from a multi-leaf-collimator system (DPAT).

Results:

The DPAT and SPAT plans provided superior dose coverage and sparing of normal tissues in comparison with conventional plans (CPT). The entrance normal tissue and skin doses (<40%) were lowered significantly by delivering dose from different directions over a wider angular view compared to conventional plans that have large entrance dose from only two fields. While the mean and minimum doses from PAT and CPT were comparable, the maximum doses from arc plans were lower than the maximum doses in conventional plans. The SPAT and DPAT plans had comparable dose parameters for regularly shaped targets. The heterogeneity index (HI) was superior in PAT plans which improved with increasing number of beams in arc plans for the different treatment sites. The conformality index (CI) depends on the treatment site and complexity of the shape of the planning target volume where for brain, pancreatic and lung tumours, PAT plans conformality was comparable and sometimes superior to CPT; and HI and CI were generally better in DPAT compared to SPAT.

Conclusions:

PAT plans have superior dose coverage and sparing of normal tissues compared to CPT plans using the MEVION double-scattering system as shown in this simulation study. Ideally, conformal proton arcs require beam shaping and dose delivery with the gantry moving; however, the MEVION double-scattering system lacks a multi-leaf collimator system and cannot deliver dose during gantry rotation. The single aperture conformal proton therapy technique is more time and cost effective compared with conventional techniques that are used currently with the MEVION proton therapy system because of the elimination of the need for patient-specific compensators. In present study, PAT was simulated with the MEVION double-scattering proton therapy system; however, it can be performed also with other proton therapy systems.

MR-based CT metal artifact reduction for head-and-neck photon, electron, and proton radiotherapy

Purpose

We investigated the impact on computed tomography (CT) image quality and photon, electron, and proton head‐and‐neck (H&N) radiotherapy (RT) dose calculations of three CT metal artifact reduction (MAR) approaches: A CT‐based algorithm (oMAR Philips Healthcare), manual water override, and our recently presented, Magnetic Resonance (MR)‐based kerMAR algorithm. We considered the following three hypotheses: I: Manual water override improves MAR over the CT‐ and MR‐based alternatives; II: The automatic algorithms (oMAR and kerMAR) improve MAR over the uncorrected CT; III: kerMAR improves MAR over oMAR.

Methods

We included a veal shank phantom with/without six metal inserts and nine H&N RT patients with dental implants. We quantified the MAR capabilities by the reduction of outliers in the CT value distribution in regions of interest, and the change in particle range and photon depth at maximum dose.

Results

Water override provided apparent image improvements in the soft tissue region but insignificantly or negatively influenced the dose calculations. We however found significant improvements in image quality and particle range impact, compared to the uncorrected CT, when using oMAR and kerMAR. kerMAR in turn provided superior improvements in terms of high intensity streak suppression compared to oMAR, again with associated impacts on the particle range estimates.

Conclusion

We found no benefits of the water override compared to the rest, and tentatively reject hypothesis I. We however found improvements in the automatic algorithms, and thus support for hypothesis II, and found the MR‐based kerMAR to improve upon oMAR, supporting hypothesis III.

Comparability of three output prediction models for a compact passively double-scattered proton therapy system

The purpose of this study was to investigate comparability of three output prediction models for a compact double‐scattered proton therapy system. Two published output prediction models are commissioned for our Mevion S250 proton therapy system. Model A is a correction‐based model (Sahoo et al., Med Phys, 2008;35(11):5088–5097) and model B is an analytical model which employs a function of r = (R’‐M’)/M’ (Kooy et al., Phys Med Biol, 2005;50:5487–5456) where R’ is defined as depth of distal 100% dose with straggling and M’ is the width between distal 100% dose and proximal 100% dose with straggling instead of the theoretical definition due to more accurate output prediction. The r is converted to ((R‐0.31)‐0.81 × M)/(0.81 × M) with the vendor definition of R (distal 90% dose) and M (distal 90% dose‐to‐proximal 95% dose), where R’ = R‐0.31 (g cm⁻²) and M’ = 0.81 × M (g cm⁻²). In addition, a quartic polynomial fit model (model C) mathematically converted from model B is studied. The outputs of 272 sets of R and M covering the 24 double scattering options are measured. Each model's predicted output is compared to the measured output. For the total dataset, the percent difference between predicted (P) and measured (M) outputs ((P‐M)/M × 100%) were within ±3% using the three different models. The average differences (±standard deviation) were −0.13 ± 0.94%, −0.13 ± 1.20%, and −0.22 ± 1.11% for models A, B, and C, respectively. The p‐values of the t‐test were 0.912 (model A vs. B), 0.061 (model A vs. C), and 0.136 (model B vs. C). For all the options, all three models have clinically acceptable predictions. The differences between models A, B, and C are statistically insignificant; however, model A generally has the potential to more accurately predict the output if a larger dataset for commissioning is used. It is concluded that the models can be comparably used for the compact proton therapy system.

Clinical Implementation of a Proton Dose Verification System Utilizing a GPU Accelerated Monte Carlo Engine

Purpose

To develop a clinical infrastructure that allows for routine Monte Carlo dose calculation verification of spot scanning proton treatment plans and includes a simple biological model to aid in normal tissue protection.

Materials and Methods

A graphical processing unit accelerated Monte Carlo dose engine was used as the calculation engine for dose verification on spot scanning proton plans. An infrastructure was built around this engine that allows for seamless exporting of treatment plans from the treatment planning system and importing of dose distribution from the Monte Carlo calculation via DICOM (digital imaging and communications in medicine). An easy-to-use Web-based interface was developed so that the application could be run from any computer. In addition to the standard relative biological effectiveness = 1.1 for proton therapy, a simple linear equation dependent on dose-weighted linear energy transfer was included. This was used to help detect possible high biological dose in critical structures.

Results

More than 270 patients were treated at our proton center in the first year of operation. Because most plans underwent multiple iterations before final approval, more than 1000 plans have been run through the system from multiple users with minimal downtime. The average time from plan export to importing of the Monte Carlo doses was less than 15 minutes. Treatment plans have been modified based on the nominal Monte Carlo dose or the biological dose.

Conclusion

Monte Carlo dose calculation verification of spot scanning proton treatment plans is feasible in a clinical environment. The 3-dimensional dose verification, particularly near heterogeneities, has resulted in plan modifications. The biological dose data provides actionable feedback for end of range effects, especially in pediatric patients.

Implementation of an improved dose-per-MU model for double-scattered proton beams to address interbeamline modulation width variability

Because treatment planning systems (TPSs) generally do not provide monitor units (MUs) for double‐scattered proton plans, models to predict MUs as a function of the range and the nominal modulation width requested of the beam delivery system, such as the one developed by the MGH group, have been proposed. For a given nominal modulation width, however, the measured modulation width depends on the accuracy of the vendor's calibration process and may differ from this nominal value, and also from one beamline to the next. Although such a difference can be replicated in our TPS, the output dependence on range and modulation width for each beam option or suboption has to be modeled separately for each beamline in order to achieve maximal 3% inaccuracy. As a consequence, the MGH output model may not be directly transferable. This work, therefore, serves to extend the model to more general clinic situations. In this paper, a parameterized linear‐quadratic transformation is introduced to convert the nominal modulation width to the measured modulation width for each beam option or suboption on a per‐beamline basis. Fit parameters are derived for each beamline from measurements of 60 reference beams spanning the minimum and maximum ranges, and modulation widths from 2 cm to full range per option or suboption. Using the modeled modulation width, we extract the MGH parameters for the output dependence on range and modulation width. Our method has been tested with 1784 patient‐specific fields delivered across three different beamlines at our facility. For these fields, all measured outputs fall within 3%, and 64.4% fall within 1%, of our model. Using a parameterized linear‐quadratic modulation width, MU calculation models can be established on a per‐beamline basis for each double scattering beam option or suboption.

PACS number: 87.53.Qc

Dose monitoring and output correction for the effects of scanning field changes with uniform scanning proton beam

PURPOSE

The output of a proton beam is affected by proton energy, Spread-Out Bragg Peak (SOBP) width, aperture size, dose rate, and the point of measurement. In a uniform scanning proton beam (USPB), the scanning field size is adjusted (including the vertical length and the horizontal width) according to the treatment field size with appropriate margins to reduce secondary neutron production. Different scanning field settings result in beam output variations that are investigated in this study.

METHODS

The measurements are performed with a parallel plate Markus chamber at the center of SOBP under the reference condition with 16 cm range, 10 cm SOBP, and 5 cm air gap. The effect of dose rate on field output is studied by varying proton beam current from 0.5 to 7 nA. The effects of scanning field settings are studied by varying independently the field width and length from 12 x 12 to 30 x 30 cm2.

RESULTS

The results demonstrate that scanning field variations can produce output variation up to 3.80%. In addition, larger output variation is observed with scanning field changes along the stem direction of the patient dose monitor (PDM). By investigating the underlying physics of incomplete charge collection and the stem effects of the PDM, an analytical model is proposed to calculate USPB output with consideration of the scanning field area and the PDM stem length that is irradiated. The average absolute difference between the measured output and calculated output using our new correction model are within 0.13 and 0.08% for the 20 and 30 cm snouts, respectively.

CONCLUSIONS

This study proposes a correction model for accurate USPB output calculation, which takes account of scanning field settings and the PDM stem effects. This model may be used to extend the existing output calculation model from one snout size to other snout sizes with customized scanning field settings. The study is especially useful for calculating field output for treatment without individualized patient specific measurements.

A sector-integration method for dose/MU calculation in a uniform scanning proton beam

An accurate, simple and time-saving sector integration method for calculating the proton output (dose/monitor unit, MU) is presented based on the following treatment field parameters: aperture shape, aperture size, measuring position, beam range and beam modulation. The model is validated with dose/MU values for 431 fields previously measured at our center. The measurements were obtained in a uniform scanning proton beam with a parallel plate ionization chamber in a water phantom. For beam penetration depths of clinical interest (6-27 cm water), dose/MU values were measured as a function of spread-out Bragg peak (SOBP) extent and aperture diameter. First, 90 randomly selected fields were used to derive the model parameters, which were used to compute the dose/MU values for the remaining 341 fields. The min, max, average and the standard deviation of the difference between the calculated and the measured dose/MU values of the 341 fields were used to evaluate the accuracy and stability, for different energy ranges, aperture sizes, measurement positions and SOBP values. The experimental results of the five different functional sets showed that the calculation model is accurate with calculation errors ranging from -2.4% to 3.3%, and 99% of the errors are less than +/-2%. The accuracy increases with higher energy, larger SOBP and bigger aperture size. The average error in the dose/MU calculation for small fields (field size <25 cm(2)) is 0.31 +/- 0.96 (%).