Dose calculation algorithms in external beam photon radiation therapy

A deep learning-based dose calculation method for volumetric modulated arc therapy

BACKGROUND

Volumetric modulated arc therapy (VMAT) planning optimization involves iterative adjustment of numerous parameters, and hence requires repeatedly dose recalculation. In this study, we used the deep learning method to develop a fast and accurate dose calculation method for VMAT.

METHODS

The classical 3D UNet was adopted and trained to learn the physics principle of dose calculation. The inputs included the projected fluence map (FM), computed tomography (CT) images, the radiological depth and the source-to-voxel distance (SVD). The projected FM was generated by projecting the accumulated FM between two consecutive control points (CPs) onto the patient's anatomy. The accumulated FM was calculated by simulating the movement of the multi-leaf collimator (MLC) from one CP to the next. The dose, calculated by the treatment planning system (TPS), was used as ground truth. 51 head and neck VMAT plans were used, with 43, 1 and 7 cases as training, validation, and testing datasets, respectively. Correspondingly, 7182, 180 and 1260 CP samples were included in the training, validation, and testing datasets.

RESULTS

This presented method was evaluated by comparing the derived dose distribution to the TPS calculated dose distribution. The dose profiles coincided for both the single CP and the entire plan (summation of all CPs). But the network derived dose was smoother than the TPS calculated dose. Gamma analysis was performed between the network derived dose and the TPS calculated dose. The average gamma pass rate was 96.56%, 98.75%, 98.03% and 99.30% under the criteria of 2% (tolerance) -2 mm (distance to agreement, DTA). 2%-3 mm, 3%-2 mm and 3%-3 mm. No significant difference was observed on the critical indices including the max, mean dose, and the relative volume covered by the 2000 cGy, 4000 cGy and the prescription dose. For one CP, the average computational time of the network and TPS was 0.09s and 0.53s. And for one patient, the average time was 16.51s and 95.60s.

CONCLUSION

The dose distribution derived by the network showed good agreement with the TPS calculated dose distribution. The computational time was reduced to approximate one-sixth of its original duration. Therefore the presented deep learning-based dose calculation method has the potential to be used for planning optimization.

Impact of Dose Calculation Algorithms and Radiobiological Parameters on Prediction of Cardiopulmonary Complications in Left Breast Radiation Therapy

Background

Breast cancer requires evaluating treatment plans using dosimetric and biological parameters. Considering radiation dose distribution and tissue response, healthcare professionals can optimize treatment plans for better outcomes.

Objective

This study aimed to evaluate the effects of the different Dose Calculation Algorithms (DCAs) and Biologically Model-Related Parameters (BMRPs) on the prediction of cardiopulmonary complications due to left breast radiotherapy.

Material and Methods

In this practical study, the treatment plans of 21 female patients were simulated in the Monaco Treatment Planning System (TPS) with a prescribed dose of 50 Gy in 25 fractions. Dose distribution was extracted using the three DCAs [Pencil Beam (PB), Collapsed Cone (CC), and Monte Carlo (MC)]. Cardiopulmonary complications were predicted by Normal Tissue Complication Probability (NTCP) calculations using different dosimetric and biological parameters. The Lyman-Kutcher-Burman (LKB) and Relative-Seriality (RS) models were used to calculate NTCP. The endpoint for NTCP calculation was pneumonitis, pericarditis, and late cardiac mortality. The ANOVA test was used for statistical analysis.

Results

In calculating Tumor Control Probability (TCP), a statistically significant difference was observed between the results of DCAs in the Poisson model. The PB algorithm estimated NTCP as less than others for all Pneumonia BMRPs.

Conclusion

The impact of DCAs and BMRPs differs in the estimation of TCP and NTCP. DCAs have a stronger influence on TCP calculation, providing more effective results. On the other hand, BMRPs are more effective in estimating NTCP. Consequently, parameters for radiobiological indices should be cautiously used s to ensure the appropriate consideration of both DCAs and BMRPs.

Dosimetric comparison and evaluation of two computational algorithms in VMAT treatment plans

PURPOSE

This study aimed to assess the accuracy and dosimetric impact of the Acuros XB (AXB) algorithm compared to the Anisotropic Analytical Algorithm (AAA) in two situations. First, simple phantom geometries were set and analyzed; moreover, volumetric modulated arc therapy (VMAT) clinical plans for Head & Neck and lung cases were calculated and compared.

METHODS

First, a phantom study was performed to compare the algorithms with radiochromic EBT3 film doses using one PMMA slab phantom and two others containing foam or air gap. Subsequently, a clinical study was conducted, including 20 Head & Neck and 15 lung cases irradiated with the VMAT technique. The treatment plans calculated by AXB and AAA were evaluated in terms of planning target volume (PTV) coverage (V95% ), dose received by relevant organs at risk (OARs), and the impact of using AXB with a grid size of 1 mm. Finally, patient-specific quality assurance (PSQA) was performed and compared for 17 treatment plans.

RESULTS

Phantom dose calculations showed a better agreement of AXB with the film measurements. In the clinical study, AXB plans exhibited lower Conformity Index and PTV V95% , higher maximum PTV dose, and lower mean and minimum PTV doses for all anatomical sites. The most notable differences were detected in regions of intense heterogeneity. AXB predicted lower doses for the OARs, while the calculation time with a grid size of 1 mm was remarkably higher. Regarding PSQA, although AAA was found to exhibit slightly higher gamma passing rates, the difference did not affect the AXB treatment plan quality.

CONCLUSIONS

AXB demonstrated higher accuracy than AAA in dose calculations of both phantom and clinical conditions, specifically in interface regions, making it suitable for sites with large heterogeneities. Hence, such dosimetric differences between the two algorithms should always be considered in clinical practice.

A Monte Carlo study on dose distribution of an orthovoltage radiation therapy system

It is important to plan radiotherapy treatment and establish optimal dose distribution to reduce the chances of side effects and injury. Because there are no commercially available tools for calculating dose distribution in orthovoltage radiotherapy in companion animals, we developed an algorithm to accomplish this and verified its characteristics using tumor disease cases. First, we used the Monte Carlo method to develop an algorithm to calculate the dose distribution of orthovoltage radiotherapy (280 kVp; MBR-320, Hitachi Medical Corporation, Tokyo, Japan) using BEAMnrc at our clinic. Using development of Monte Carlo method, dose distribution for tumor and normal organs were evaluated in brain tumors, squamous cell carcinomas of the head, and feline nasal lymphomas. In all cases of brain tumors, the mean dose delivered to the GTV ranged from 36.2 to 76.1% of the prescribed dose due to the decrease through the skull. In the nasal lymphoma in cats, the eyes with covered a 2 mm-thick lead plate, the respective average dose to the eyes was 71.8% and 89.9% less than that to the uncovered eyes. The findings may be useful for informed decision making in orthovoltage radiotherapy with more effective and targeted irradiation and data collection allowing detailed informed consent.

Comparative study of the collapsed cone convolution and Monte Carlo algorithms for radiation therapy planning of canine sinonasal tumors reveals significant dosimetric differences

Computer-based radiation therapy requires high targeting and dosimetric precision. Analytical dosimetric algorithms typically are fast and clinically viable but can have increasing errors near air-bone interfaces. These are commonly found within dogs undergoing radiation planning for sinonasal cancer. This retrospective methods comparison study is designed to compare the dosimetry of both tumor volumes and organs at risk and quantify the differences between collapsed cone convolution (CCC) and Monte Carlo (MC) algorithms. Canine sinonasal tumor plans were optimized with CCC and then recalculated by MC with identical control points and monitor units. Planning target volume (PTV)_air , PTV_{soft tissue} , and PTV_bone were created to analyze the dose discrepancy within the PTV. Thirty imaging sets of dogs were included. Monte Carlo served as the gold standard calculation for the dosimetric comparison. Collapsed cone convolution overestimated the mean dose (D_mean ) to PTV and PTV_{soft tissue} by 0.9% and 0.5%, respectively (both P < 0.001). Collapsed cone convolution overestimated D_mean to PTV_bone by 3% (P < 0.001). Collapsed cone convolution underestimated the near-maximum dose (D₂ ) to PTV_air by 1.1% (P < 0.001), and underestimated conformity index and homogeneity index in PTV (both P < 0.001). Mean doses of contralateral and ipsilateral eyes were overestimated by CCC by 1.6% and 1.7%, respectively (both P < 0.001). Near-maximum doses of skin and brain were overestimated by CCC by 2.2% and 0.7%, respectively (both P < 0.001). As clinical accessibility of Monte Carlo becomes more widespread, dose constraints may need to be re-evaluated with appropriate plan evaluation and follow-up.

Dose Calculation Algorithms for External Radiation Therapy: An Overview for Practitioners

Radiation therapy (RT) is a constantly evolving therapeutic technique; improvements are continuously being introduced for both methodological and practical aspects. Among the features that have undergone a huge evolution in recent decades, dose calculation algorithms are still rapidly changing. This process is propelled by the awareness that the agreement between the delivered and calculated doses is of paramount relevance in RT, since it could largely affect clinical outcomes. The aim of this work is to provide an overall picture of the main dose calculation algorithms currently used in RT, summarizing their underlying physical models and mathematical bases, and highlighting their strengths and weaknesses, referring to the most recent studies on algorithm comparisons. This handy guide is meant to provide a clear and concise overview of the topic, which will prove useful in helping clinical medical physicists to perform their responsibilities more effectively and efficiently, increasing patient benefits and improving the overall quality of the management of radiation treatment.

Feasibility of streamline upwind Petrov-Galerkin angular stabilization of the linear Boltzmann transport equation with magnetic fields

To accurately model dose in a magnetic field, the Lorentz force must be included in the traditional linear Boltzmann transport equation (LBTE). Both angular and spatial stabilization are required to deterministically solve this equation. In this work, a streamline upwind Petrov-Galerkin (SUPG) method is applied to achieve angular stabilization of the LBTE with magnetic fields. The spectral radius of the angular SUPG method is evaluated using a Fourier analysis method to characterize the convergence properties. Simulations are then performed on homogeneous phantoms and two heterogeneous slab geometry phantoms containing water, bone, lung/air and water for 0.5 T parallel and 1.5 T perpendicular magnetic field configurations. Fourier analysis determined that the spectral radius of the SUPG scheme is unaffected by magnetic field strength and the SUPG free parameter, indicating that the Gauss-Seidel source iteration method is unconditionally stable and the convergence rate is not degraded with increasing magnetic field strength. 100% of simulation points passed a 3D gamma analysis at a 2%/2 mm (3%/3 mm) gamma criterion for both magnetic field configurations in the homogeneous phantom study, with the exception of the 1.5 T perpendicular magnetic field in the pure lung phantom where a 77.4% (87.0%) pass rate was achieved. Simulations in the lung slab geometry phantom resulted in 100% of points passing a 2%/2 mm gamma analysis in a 0.5 T parallel magnetic field, and 97.7% (98.8%) of points passing a 2%/2 mm (3%/3 mm) gamma criterion in a 1.5 T perpendicular magnetic field. For the air slab geometry phantom, 72.1% (79.2%) of points passed a 2%/2 mm gamma criterion in a 0.5 T parallel magnetic field and 90.3% (92.8%) passed the same gamma criterion in a 1.5 T perpendicular magnetic field. While the novel SUPG angular stabilization method shows feasibility in some cases, it was found that the accuracy of this method was degraded for very low density media such as air.

Investigating the effect of dental implant materials with different densities on radiotherapy dose distribution using Monte-Carlo simulation and pencil beam convolution algorithm

OBJECTIVES

The aim of this study was to investigate the effect of dental implant materials with different physical densities on dose distribution for head and neck cancer radiotherapy planning.

METHODS

Titanium (Ti), Titanium alloy (Ti-6Al-4V), Zirconia (Y-TZP), Zirconium oxide (ZrO₂), Alumina (Al₂O₃) and polyetheretherketone (PEEK) dental implant materials were used for determination of implant material effect on dose distribution. Dental implant effect was investigated by using pencil beam convolution (PBC) algorithm of Eclipse treatment planning systems (TPS) and Monte Carlo (MC) simulation technique. 6 MV photon beam of the Varian 2300 C/D linear accelerator was simulated by EGSnrc-based BEAMnrc MC code system.

RESULTS

Reasonable consistency was determined for percentage depth dose (PDD) curves between MC simulation and water phantom measurements at 6.4 MeV initial electron energy. The consistency between modelled linear accelerator PDD curve calculations and water-phantom PDD measurements were compatible within 1 % range. The dose increase in front of the dental implant calculated by MC simulation is in the range of 0.4-20.2%. We found by MC and PBC calculations that the differences in dose increase in front of the dental implant materials is in the range of 0.1-17.2% and is dependent on the physical density of the dental implant.

CONCLUSIONS

Dose increase for Zirconia was noted to be maximum while PEEK implant dose increase was minimum among the whole dental implant materials studied. This study revealed that the Eclipse TPS PBC algorithm could not accurately estimate the backscatter radiation from dental implant materials.

Investigation of AAA dose calculation algorithm accuracy in surface and buildup region for 6MV photon beam using markus parallel-plate ion chamber

In radiotherapy, dose distributions are obtained by using dose calculation algorithms that are implanted in treatment planning systems (TPS). This study aims to compare the surface doses of separate field sizes calculated by different version of The Analytical Anisotropic Algorithm (AAA) and measured by the parallel-plate ion chamber that is admitted as the most reliable dosimetry system for the surface region dose measurements. In order to measure the near surface dose, water equivalent solid phantom was used and measurements were made for 6MV photon beam at 100 cm source-detector distance for 5×5, 10×10, and 20×20 cm2 field sizes. AAA 8.9 and AAA 15.1 versions of the Varian Eclipse TPS were used for surface dose calculations by generating beams with separate field sizes. The doses were read by considering the effective buildup thickness of Markus parallel-plate ion chamber. The surface doses using 6 MV photon beams for 10×10 cm2 field size at 0.07 mm were found to be 11.04%, 26.25%, and 19.69% for AAA v8.9, AAA v15.1 and Markus chamber, respectively. It was seen that for both of the AAA versions and Markus parallel-plate ion chamber, increasing field sizes also increase surface dose. For all field sizes, surface dose was lowest by using AAA v8.9 at 0.07 mm. The different versions of the same TPS algorithms may calculate the surface doses distinctively. After upgrading of TPS algorithms, surface doses should be calculated and compared by measurements with different dosimetry systems to better understand their calculation behaviors in the near surface region.

A Novel Algorithm in Radiation Dosimetry of Regular and Irregular Treatment Fields

Background:

The aim of this study was to design an algorithm for the calculation of monitor unit (MU) in a short time and high precision for different radiotherapy (RT) fields.

Materials and Methods:

The algorithm for calculating MU for the stated patients was designed in MATLAB software. To investigate the efficiency of this algorithm, 11 regular chest fields with the sizes of 7 cm × 7 cm up to 17 cm × 17 cm were considered, and the obtained MUs were compared with MUs of 13 patients which were calculated with a “hand calculation” which is used in some RT centers for the aforementioned fields.

Results:

The maximum percentage of calculation errors of regular fields at the depths of 4 and 10 cm were 1 and 0.8, respectively. The maximum and minimum percentage of calculation errors in irregular fields was 3 and 0.9, respectively. Furthermore, the maximum and minimum errors were 8.8 and 0.14, respectively. In addition, relative percentages of the MUs for irregular fields of chest and supraclavicular were 1.63 and 1.01, respectively.

Conclusion:

Calculation of MUs is suggested to be performed with the novel proposed algorithm, due to reduce the treatment time, and also provide high accuracy and precision compared to hand calculation.

Stability analysis of a deterministic dose calculation for MRI-guided radiotherapy

Modern effort in radiotherapy to address the challenges of tumor localization and motion has led to the development of MRI guided radiotherapy technologies. Accurate dose calculations must properly account for the effects of the MRI magnetic fields. Previous work has investigated the accuracy of a deterministic linear Boltzmann transport equation (LBTE) solver that includes magnetic field, but not the stability of the iterative solution method. In this work, we perform a stability analysis of this deterministic algorithm including an investigation of the convergence rate dependencies on the magnetic field, material density, energy, and anisotropy expansion. The iterative convergence rate of the continuous and discretized LBTE including magnetic fields is determined by analyzing the spectral radius using Fourier analysis for the stationary source iteration (SI) scheme. The spectral radius is calculated when the magnetic field is included (1) as a part of the iteration source, and (2) inside the streaming-collision operator. The non-stationary Krylov subspace solver GMRES is also investigated as a potential method to accelerate the iterative convergence, and an angular parallel computing methodology is investigated as a method to enhance the efficiency of the calculation. SI is found to be unstable when the magnetic field is part of the iteration source, but unconditionally stable when the magnetic field is included in the streaming-collision operator. The discretized LBTE with magnetic fields using a space-angle upwind stabilized discontinuous finite element method (DFEM) was also found to be unconditionally stable, but the spectral radius rapidly reaches unity for very low-density media and increasing magnetic field strengths indicating arbitrarily slow convergence rates. However, GMRES is shown to significantly accelerate the DFEM convergence rate showing only a weak dependence on the magnetic field. In addition, the use of an angular parallel computing strategy is shown to potentially increase the efficiency of the dose calculation.

Going the distance: validation of Acuros and AAA at an extended SSD of 400 cm

Accurate dose calculation and treatment delivery is essential for total body irradiation (TBI). In an effort to verify the accuracy of TBI dose calculation at our institution, we evaluated both the Varian Eclipse AAA and Acuros algorithms to predict dose distributions at an extended source‐to‐surface distance (SSD) of 400 cm. Measurements were compared to calculated values for a 6 MV beam in physical and virtual phantoms at 400 cm SSD using open beams for both 5×5 and 40×40 cm2 field sizes. Inline and crossline profiles were acquired at equivalent depths of 5 cm, 10 cm, and 20 cm. Depth‐dose curves were acquired using EBT2 film and an ion chamber for both field sizes. Finally, a RANDO phantom was used to simulate an actual TBI treatment. At this extended SSD, care must be taken using the planning system as there is good relative agreement between measured and calculated profiles for both algorithms, but there are deviations in terms of the absolute dose. Acuros has better agreement than AAA in the penumbra region.

PACS number(s): 87.55.kd