Treatment Planning Dose Calculations Research Articles

Synthetic CT for gamma knife radiosurgery dose calculation: A feasibility study

PurposeTo determine if MRI-based synthetic CTs (sCT), generated with no predefined pulse sequence, can be used for inhomogeneity correction in routine gamma knife radiosurgery (GKRS) treatment planning dose calculation. MethodsTwo sets of sCTs were generated from T1post and T2 images using cycleGAN. Twenty-eight patients (18 training, 10 validation) were retrospectively selected. The image quality of the generated sCTs was compared with the original CT (oCT) regarding the HU value preservation using histogram comparison, RMSE and MAE, and structural integrity. Dosimetric comparisons were also made among GKRS plans from 3 calculation approaches: TMR10 (oCT), and convolution (oCT and sCT), at four locations: original disease site, bone/tissue interface, air/tissue interface, and mid-brain. ResultsThe study showed that sCTs and oCTs’ HU were similar, with T2-sCT performing better. TMR10 significantly underdosed the target by a mean of 5.4% compared to the convolution algorithm. There was no significant difference in convolution algorithm shot time between the oCT and sCT generated with T2. The highest and lowest dosimetric differences between the two CTs were observed in the bone and air interface, respectively. Dosimetric differences of 3.3% were observed in sCT predicted from MRI with stereotactic frames, which was not included in the training sets. ConclusionsMRI-based sCT can be utilized for GKRS convolution dose calculation without the unnecessary radiation dose, and sCT without metal artifacts could be generated in framed cases. Larger datasets inclusive of all pulse sequences can improve the training set. Further investigation and validation studies are needed before clinical implementation.

Application of low-dose CT in image-guided radiotherapy based on CT-linac

BackgroundImage-guided radiation therapy (IGRT) involves online medical imaging systems. CT-guided imaging systems not only have high image quality but also allow for direct dose calculation for radiation treatment planning, making it an ideal image guidance method. However, the imaging dose from CT images may pose additional risks to patients. Therefore, the aim of this study was to evaluate the substitutability of using low-dose CT images for IGRT. Materials and methodsThe CIRS-062 phantom, Catphan 604 phantom, CT dose index phantom were used to obtain the conversion curves of the CT values to the relative electron density (RED), image quality, and imaging dose of the low-dose CT, respectively. Catphan 604 phantom and clinical cases were used to analyze the feasibility of low-dose CT for dose calculation. ResultsNo significant differences were observed in the conversion curves of CT values to RED between low-dose and standard-dose CT scans. In terms of image quality, the geometric distortion of low-dose CT was 0.1 mm, which was the same as that of standard-dose CT. The spatial resolution of the images was 0.42–0.45 lp/mm, which was slightly better than that of standard-dose CT (0.40–0.44 lp/mm). The image uniformity was 98.4%–99.0%, slightly worse than that of standard-dose CT (99.4%–99.5%), but still met the clinical requirements. The imaging dose was 2.96–4.62 mGy, significantly lower than that of standard-dose CT (9.71–26.68 mGy), especially for head CT protocol. In terms of dose calculation, the γ passing rates were not lower than 98% for either the Catphan 604 phantom or the clinical cases. ConclusionLow-dose CT can significantly reduce the imaging dose without a significant loss of image quality and can be directly used for dose calculation in radiation treatment planning, providing a safer and more effective image guidance method for IGRT.

RapidBrachyTG43: A Geant4-based TG-43 parameter and dose calculation module for brachytherapy dosimetry.

The AAPM TG-43U1 formalism remains the clinical standard for dosimetry of low- and high-energy -emitting brachytherapy sources. TG-43U1 and related reports provide consensus datasets of TG-43 parameters derived from various published measured data and Monte Carlo simulations. These data are used to perform standardized and fast dose calculations for brachytherapy treatmentplanning. Monte Carlo TG-43 dosimetry parameters are commonly derived to characterize novel brachytherapy sources. RapidBrachyTG43 is a module of RapidBrachyMCTPS, a Monte Carlo-based treatment planning system, designed to automate this process, requiring minimal user input to prepare Geant4-based Monte Carlo simulations for a source. RapidBrachyTG43 may also perform a TG-43 dose to water-in-water calculation for a plan, substantially accelerating the same calculation performed using RapidBrachyMCTPS's Monte Carlo dose calculationengine. TG-43 parameters , , , and were calculated using three commercial source models, one each of I, Ir, and Co, and were benchmarked to published data. TG-43 dose to water was calculated for a clinical breast brachytherapy plan and was compared to a Monte Carlo dose calculation with all patient tissues, air, and catheters set towater. TG-43 parameters for the three simulated sources agreed with benchmark datasets within tolerances specified by the High Energy Brachytherapy Dosimetry working group. A gamma index comparison between the TG-43 and Monte Carlo dose-to-water calculations with a dose difference and difference to agreement criterion of 1%/1 mm yielded a 98.9% pass rate, with all relevant dose volume histogram metrics for the plan agreeing within 1%. Performing a TG-43-based dose calculation provided an acceleration of dose-to-water calculation by a factor of165. Determination of TG-43 parameter data for novel brachytherapy sources may now be facilitated by RapidBrachyMCTPS. These parameter datasets and existing consensus or published datasets may also be used to determine the TG-43 dose for a plan inRapidBrachyMCTPS.

Cross-engine transformation-based fast dose calculation for MRI-Linac online treatment planning.

To propose a novel magnetic field dose calculation method based on transformation from pencil beam (PB) to Monte Carlo (MC) distribution for MRI-Linac online treatment planning. The novel magnetic field dose calculation algorithm was established by a PB dose engine and a magnetic field with tissue inhomogeneity influence correction network. The correction network was constructed with a Res-UNet framework, including residual modules and an encoding-decoding path, by inputting three-dimensional PB dose and patient electron density map, and outputting transformed dose distribution. The influences of magnetic fields and tissue heterogeneity were considered and corrected simultaneously in the correction model. A total of 110 clinically treated static beam IMRT plans were collected, including plans for brain, head-and-neck, lung, and rectum cases. A total of 90 cases were used and enhanced to train and validate the model, and the other 20 cases were for test. By comparing the proposed pipeline-generated dose distribution with original input PB dose and corresponding MC dose, the feasibility and effectiveness of the method was evaluated. Results on both beam dose and plan dose accuracy comparisons on all investigated four tumor sites show great consistency between the cross-dose-engine transformation generations and the MC results, with averaged plan mean absolute error of 0.90%±0.13% for the voxel-wise dose difference and 98.33%±1.07% gamma passing rate at the 2%/2mm criteria. The whole PB calculation and transformation process can be completed within second. We have successfully developed a fast novel magnetic field dose calculation pipeline based on transformation from PB distribution to MC distribution for MRI-Linac online treatment planning.

Improving the quality of head and neck radiotherapy CT images using a second image reconstruction set

PurposeTo improve the quality of radiotherapy head and neck CT images through use of an additional image set reconstructed from the raw data of the primary scan, thus allowing parameters such as reconstruction field-of-view (FOV) and kernel to be optimised without impacting on the images used for treatment planning dose calculations. MethodsUsing a Catphan image quality phantom and a Toshiba Aquilion LB CT scanner, qualitative and quantitative measurements were made for different reconstruction kernels and FOV diameters. The preferred FOV diameter and kernels were selected. Clinical images from six patients were reconstructed using those kernels (FC13, FC41, FC44, FC64) and the chosen FOV, 200 mm. The images were ranked to choose the kernel which gave best image quality for organ delineation. The scanner workflow was adjusted to produce for every scan a second image set using the chosen kernel and FOV. Finally, for 10 patient scans, image quality was compared for the two reconstructed images. ResultsThe second image set was produced using kernel FC44 and 200 mm FOV. The primary image set using 550 mm FOV and FC13 was unchanged and contours from the second image set merged onto the first. Oncologists reported increased confidence in contouring in all cases using the new procedure. ConclusionProduction of a second image set, using a reduced reconstruction FOV and a kernel which optimises contrast and sharpness, significantly improves the quality of head and neck CT images for contouring, and avoids any dose increase.

AAPM MEDICAL PHYSICS PRACTICE GUIDELINE 5.b: Commissioning and QA of treatment planning dose calculations-Megavoltage photon and electron beams.

The American Association of Physicists in Medicine (AAPM) is a nonprofit professional society whose primary purposes are to advance the science, education, and professional practice of medical physics. The AAPM has more than 8000 members and is the principal organization of medical physicists in the United States. The AAPM will periodically define new practice guidelines for medical physics practice to help advance the science of medical physics and to improve the quality of service to patients throughout the United States. Existing medical physics practice guidelines will be reviewed for the purpose of revision or renewal, as appropriate, on their fifth anniversary or sooner. Each medical physics practice guideline represents a policy statement by the AAPM, has undergone a thorough consensus process in which it has been subjected to extensive review, and requires the approval of the Professional Council. The medical physics practice guidelines recognize that the safe and effective use of diagnostic and therapeutic radiology requires specific training, skills, and techniques, as described in each document. Reproduction or modification of the published practice guidelines and technical standards by those entities not providing these services is not authorized. The following terms are used in the AAPM practice guidelines: Must and Must Not: Used to indicate that adherence to the recommendation is considered necessary to conform to this practice guideline. While must is the term to be used in the guidelines, if an entity that adopts the guideline has shall as the preferred term, the AAPM considers that must and shall have the same meaning.Should and Should Not: Used to indicate a prudent practice to which exceptions may occasionally be made in appropriate circumstances.

The radiosensitizing effect of platinum nanoparticles in proton irradiations is not caused by an enhanced proton energy deposition at the macroscopic scale

Objective. Due to the radiosensitizing effect of biocompatible noble metal nanoparticles (NPs), their administration is considered to potentially increase tumor control in radiotherapy. The underlying physical, chemical and biological mechanisms of the NPs’ radiosensitivity especially when interacting with proton radiation is not conclusive. In the following work, the energy deposition of protons in matter containing platinum nanoparticles (PtNPs) is experimentally investigated. Approach. Surfactant-free monomodal PtNPs with a mean diameter of (40 ± 10) nm and a concentration of 300 μg ml−1, demonstrably leading to a substantial production of reactive oxygen species (ROS), were homogeneously dispersed into cubic gelatin samples serving as tissue-like phantoms. Gelatin samples without PtNPs were used as control. The samples’ dimensions and contrast of the PtNPs were verified in a clinical computed tomography scanner. Fields from a clinical proton machine were used for depth dose and stopping power measurements downstream of both samples types. These experiments were performed with a variety of detectors at a pencil beam scanning beam line as well as a passive beam line with proton energies from about 56–200 MeV. Main results. The samples’ water equivalent ratios in terms of proton stopping as well as the mean proton energy deposition downstream of the samples with ROS-producing PtNPs compared to the samples without PtNPs showed no differences within the experimental uncertainties of about 2%. Significance. This study serves as experimental proof that the radiosensitizing effect of biocompatible PtNPs is not due to a macroscopically increased proton energy deposition, but is more likely caused by a catalytic effect of the PtNPs. Thus, these experiments provide a contribution to the highly discussed radiobiological question of the proton therapy efficiency with noble metal NPs and facilitate initial evidence that the dose calculation in treatment planning is straightforward and not affected by the presence of sensitizing PtNPs.

CT Taramalarında Hastanın Boyuna Göre Absorbe Edilen Dozun Değişimi

Computed Tomography (CT) images taken in cancer treatments are important in treatment portal design and planning. The dose received by the patient in CT scans are not considered in the treatment planning dose calculation and becomes important in calculating the limit doses of the critical organs with critical dose threshold. In this study, the change of the dose received by some of the critical organs, namely the heart, liver, and kidneys, according to the height of the patient was investigated with the NCICT code using the Monte Carlo technique. As a result, doses were changed by the height of the patients.

Characterization of an advanced cone beam CT (CBCT) reconstruction algorithm used for dose calculation on Varian Halcyon linear accelerators

In this study, the performance of a new iterative reconstruction algorithm, the pre-clinical AcurosXB iCBCT algorithm, has been characterized on Varian Halcyon linear accelerators with respect to the potential of radiotherapy dose calculations on CBCT images. The study utilized various phantom setups to verify the accuracy of the pre-clinical algorithm under different scatter conditions and compared dose calculations performed on CBCT images reconstructed with the pre-clinical algorithm to those performed on typical planning CT images. The results indicated that despite showing improvements compared to the existing iCBCT protocol, certain restrictions should be introduced when the pre-clinical AcurosXB iCBCT algorithm was used for dose calculations. Changes in the scatter condition exhibited a larger effect on CBCTs than on planning CTs. Therefore, users should be careful in offsetting the patient and positioning the patient’s arms if the resultant images will be used for dose calculations. In addition, protocols with different kV settings should be approached with caution, where 100 kV protocols should only be used to scan the head and neck area, while the rest of the body should be scanned with the 125 kV and 140 kV protocols. When the patient is set up properly and the appropriate energy is selected for the anatomical area, the uncertainty of using the novel AcurosXB iCBCT algorithm for treatment planning dose calculation is within ±2.0%.

Treatment Planning Study for Microbeam Radiotherapy Using Clinical Patient Data.

Simple SummaryMicrobeam radiotherapy is a novel dose delivery technique in radiation oncology. Preclinical studies have demonstrated preferable dose distributions with reduced damage to normal tissue but similar tumor control compared to conventional radiotherapy. For future clinical applications, realistic treatment plans for patient data are required as well as a method for comparing the spatially fractionated MRT doses with conventional broad beam doses. In this study, we performed MRT treatment planning on real patient data for relevant clinical scenarios. We successfully implemented a sophisticated dose comparison concept based on the equivalent uniform dose. For most scenarios and parameters studied, the clinical dose constraints were met. However, limitations were caused by the lack of treatment plan optimization and dose optimization. Altogether, we demonstrated the feasibility of achieving clinically acceptable MRT dose distributions based on real patient data as a primary major step towards clinical application of MRT.Microbeam radiotherapy (MRT) is a novel, still preclinical dose delivery technique. MRT has shown reduced normal tissue effects at equal tumor control rates compared to conventional radiotherapy. Treatment planning studies are required to permit clinical application. The aim of this study was to establish a dose comparison between MRT and conventional radiotherapy and to identify suitable clinical scenarios for future applications of MRT. We simulated MRT treatment scenarios for clinical patient data using an inhouse developed planning algorithm based on a hybrid Monte Carlo dose calculation and implemented the concept of equivalent uniform dose (EUD) for MRT dose evaluation. The investigated clinical scenarios comprised fractionated radiotherapy of a glioblastoma resection cavity, a lung stereotactic body radiotherapy (SBRT), palliative bone metastasis irradiation, brain metastasis radiosurgery and hypofractionated breast cancer radiotherapy. Clinically acceptable treatment plans were achieved for most analyzed parameters. Lung SBRT seemed the most challenging treatment scenario. Major limitations comprised treatment plan optimization and dose calculation considering the tissue microstructure. This study presents an important step of the development towards clinical MRT. For clinical treatment scenarios using a sophisticated dose comparison concept based on EUD and EQD2, we demonstrated the capability of MRT to achieve clinically acceptable dose distributions.

Synthetic CT Generation From Multi-Sequence MR Images for Head and Neck MRI-Only Radiotherapy via Cycle-Consistent Generative Adversarial Network

Synthetic CT (sCT) generation with MR image plays an essential role in MR-only radiotherapy, which can be used for electron density estimation and dose calculation in treatment planning. Currently, the reliability of deep learning-based methods for sCT generation depends on two aspects: the MR-CT registration errors, which can eliminate by a cycle-consistent generative adversarial network (CycleGAN) with unpaired training data, and the different anatomy information and contract divergence of various tissue in different sequences MRI which will significantly affect the nonlinear mapping of MR-CT. The purpose of this study is to investigate the effect of different sequence MRI by evaluating the image quality of corresponded sCTs.Multi-sequence MR images (T1, T2, T1C) and plan CT images of 151 patients with nasopharyngeal carcinoma were acquired on the same day in the radiation treatment position. 126 cases were randomly selected as the training set with the same preprocessing. Three unsupervised CycleGAN-based models were respectively trained with different sequence MRI with Architecture, which consists of 2 ResUnet-based generators and 2 Patch-GAN-based discriminators. The remaining 25 CT-MR cases with non-rigid registration were selected as the independent paired testing set. The mean error (ME) and mean absolute error (MAE) between plan CT and sCT of each model were used to measure the CT value estimation accuracy, which was calculated within air (Hounsfield Unit, HU < 100), soft tissue (-100 < HU < 150), bone (HU > 150) and whole body, respectively. Structural similarity index (SSIM) was applied to evaluate the similarity between error (plan CT and sCT.The MEs calculated between plan CT and T1, T2, T1C-based sCT images, were (-0.62 ± 3.06) vs. (-1.81 ± 3.13) vs. (0.13 ± 2.56) within air, (154.92 ± 87.93) vs. (173.27 ± 97.62) vs. (262.78 ± 91.74) within soft tissue, (1363.99 ± 294.94) vs. (337.21 ± 50.4) vs. (661.63 ± 168.43) within bone, (22.12 ± 29.47) vs. (17.05 ± 10.51) vs. (35.28 ± 20.75) within the whole body. The MAEs correspondingly were (4.49 ± 3.13) vs. (4.6 ± 3.47) vs. (4.6 ± 3.47) within air, (186.41 ± 61.75) vs. (197.33 ± 58.62) vs. (293.56 ± 72.72) within soft tissue, (186.41 ± 61.75) vs. (197.33 ± 58.62) vs. (293.56 ± 72.72) within bone, (31.28 ± 28.1) vs. (27.64 ± 10.41) vs. (42.6 ± 23.82) within the whole body. The quantitative analysis between T1, T2, T1C-sCTs indicates that the T1C based generated model achieves higher accuracy than the other models in nasopharyngeal carcinoma.The T1C MRI can provide sufficient anatomy information and tissue contract in synthetic CT prediction of head and neck region, which offers a substantial potential for density estimation and dose calculation in radiotherapy. The generation method and evaluation will investigate synthetic CT prediction with more complex tissue in various body parts.

Doses delivered by portal imaging quality assurance in routine practice of adjuvant breast radiotherapy worth to by monitored and compensated in some cases.

Imaging, in radiotherapy, has become a routine tool for repositioning of the target volume at each session. The repositioning precision, currently infracentimetric, evolves along with the irradiation techniques. This retrospective study aimed to identify practices and doses resulting from the use of high energy planar imaging (portal imaging) in daily practice. A retrospective survey of portal images (PIs) was carried out over 10 years for 2,403 patients and for three linacs (1 Elekta SLi, 2 Varian Clinac) for postoperative mammary irradiations. Images were taken using a standardized number of monitor units (MU) for all patients. Due to the variable sensitivities of the detectors and the possibility of adjustment of the detector-patient distance, the number of MU were 3; 2 and 1 respectively, for Elekta SLi®, Clinac 600® and Clinac 2100®. Then, a representative cumulated dose was calculated in simplified reference conditions (5 cm depth, beam of 10 cm × 10 cm, 6 MV), considering the total number of images taken during the whole treatment course. The consistency between the representative doses and the actual absorbed doses received by the patients was verified by simulating a series of typical cases with the treatment plan dose calculation system. The delivered doses differ significantly between the three linacs. The mean representative dose values by complete treatment were 0.695; 0.241 and 0.216 Gy, respectively, for SLi, Clinac 600 and Clinac 2100. However, 15 patients were exposed to a dose >2 Gy with a maximum dose of 5.05 Gy. The simulated doses were very similar to the representative doses. A significant dose delivery was highlighted by this study. These representative doses are presently communicated weekly to the radiation oncologist for the radiation protection of their patients. Moreover, they should be taken into account in a possible study of long-term stochastic risks.

Clinical verification of treatment planning dose calculation in lung SBRT with GATE Monte Carlo simulation code

Purpose This study aims to use GATE/Geant4 simulation code to evaluate the performance of dose calculations with Anisotropic Analytical Algorithm (AAA) in the context of lung SBRT for complex treatments considering images of patients. Methods Four cases of non-small cell lung cancer treated with SBRT were selected for this study. Irradiation plans were created with AAA and recalculated end to end using Monte Carlo (MC) method maintaining field configurations identical to the original plans. Each treatment plan was evaluated in terms of PTV and organs at risk (OARs) using dose-volume histograms (DVH). Dosimetric parameters obtained from DVHs were used to compare AAA and MC. Results The comparison between the AAA and MC DVH using gamma analysis with the passing criteria of 3%/3% showed an average passing rate of more than 90% for the PTV structure and 97% for the OARs. Tightening the criteria to 2%/2% showed a reduction in the average passing rate of the PTV to 86%. The agreement between the AAA and MC dose calculations for PTV dosimetric parameters (V100; V90; Homogeneity index; maximum, minimum and mean dose; CIPaddick and D2cm) was within 18.4%. For OARs, the biggest differences were observed in the spinal cord and the great vessels. Conclusions In general, we did not find significant differences between AAA and MC. The results indicate that AAA could be used in complex SBRT cases that involve a larger number of small treatment fields in the presence of tissue heterogeneities.

Optimisation of variance reduction techniques in EGSnrc Monte Carlo for a 6 MV photon beam of an Elekta Synergy linear accelerator

ObjectiveMonte Carlo (MC) simulations are considered to be the most accurate form of algorithm for dose calculation. However, the main obstacle to using MC, especially in clinical routine, is the simulation time needed to gain results below a desirable level of uncertainty. Variance reduction techniques (VRTs) have been introduced to reduce the simulation time while maintaining the uncertainty at an acceptable level. The aim of this study is to investigate and optimize the VRTs implemented in EGSnrc MC code, BEAMnrc and DOSXYZnrc. MethodologyThe BEAMnrc user code was used to model a 10 × 10 cm2 field size of a 6 MV photon beam from an Elekta Synergy linear accelerator. The DOSXYZnrc user code was used to model a water phantom. The effects of different VRTs on the simulation efficiency were investigated either individually or in combination. The directional bremsstrahlung splitting (DBS) technique was investigated further to find the optimum splitting number and splitting field radius. For DOSXYZnrc, the photon splitting was investigated to find the best combination with the VRTs in BEAMnrc and to find the optimum splitting number. Finally, the best combination of VRTs in both BEAMnrc and DOSXYZnrc was compared with the corresponding phase space (PHSP) simulation source. ResultsThe DBS technique was found to be the most efficient. The optimum splitting number was found to be 10,000 and 15,000 with and without electron splitting, respectively. For the DBS splitting field radius, overestimating by up to 3 cm would be sufficient without causing a significant loss in efficiency. For both BEAMnrc and DOSXYZnrc, the combination of DBS, bremsstrahlung cross-section enhancement, range rejection with 2 MeV and photon splitting (with optimum splitting number of 35) was the most efficient, and was about 8% less efficient than PHSP simulation. ConclusionThe VRTs implemented in EGSnrc MC code made it possible to achieve an acceptably small uncertainty within a reasonable simulation time, if optimised properly. The combination of VRTs presented in this study eliminates the need to spare a large amount of disk space, and where parallel computing could allow for MC dose calculation in real-time adaptive treatment planning.

Comparison of the RayStation photon Monte Carlo dose calculation algorithm against measured data under homogeneous and heterogeneous irradiation geometries.

This work compares Monte Carlo dose calculations performed using the RayStation treatment planning system against data measured on a Varian Truebeam linear accelerator with 6 MV and 10 MV FFF photon beams. The dosimetric performance of the RayStation Monte Carlo calculations was evaluated in a variety of irradiation geometries employing homogeneous and heterogeneous phantoms. Profile and depth dose comparisons against measurement were carried out in relative mode using the gamma index as a quantitative measure of similarity within the central high dose regions. The results demonstrate that the treatment planning system dose calculation engine agrees with measurement to within 2%/1 mm for more than 95% of the data points in the high dose regions for all test cases. A systematic underestimation was observed at the tail of the profile penumbra and out of field, with mean differences generally<0.5mm or 1% of curve dose maximum respectively. Out of field agreement varied between evaluated beam models. The RayStation implementation of photon Monte Carlo dose calculations show good agreement with measured data for the range of scenarios considered in this work and is deemed sufficiently accurate for introduction into clinical use.

DeepMC: a deep learning method for efficient Monte Carlo beamlet dose calculation by predictive denoising in magnetic resonance-guided radiotherapy

Emerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation quality and speed, creating an unmet need for the acceleration of Monte Carlo (MC) dose calculation. Existing deep learning approaches to denoise the final plan MC dose fail to achieve the accuracy and speed requirements of large-scale beamlet dose calculation in the presence of a strong magnetic field for online adaptive radiotherapy planning. Our deep learning dose calculation method, DeepMC, addresses these needs by predicting low-noise dose from extremely noisy (but fast) MC-simulated dose and anatomical inputs, thus enabling significant acceleration. DeepMC simultaneously reduces MC sampling noise and predicts corrupted dose buildup at tissue-air material interfaces resulting from MR-field induced electron return effects. Here we demonstrate our model’s ability to accelerate dose calculation for daily treatment planning by a factor of 38 over traditional low-noise MC simulation with clinically meaningful accuracy in deliverable dose and treatment delivery parameters. As a post-processing approach, DeepMC provides compounded acceleration of large-scale dose calculation when used alongside established MC acceleration techniques in variance reduction and graphics processing unit-based MC simulation.

Evaluation of image quality of a novel computed tomography metal artifact management technique on an anthropomorphic head and neck phantom.

Background and purposeArtefacts caused by dental amalgam implants present a common challenge in computed tomography (CT) and therefore treatment planning dose calculations. The goal was to perform a quantitative image quality analysis of our Artifact Management for Proton Planning (AMPP) algorithm which used gantry tilts for managing metal artefacts on Head and Neck (HN) CT scans and major vendors’ commercial approaches.Materials and methodsMetal artefact reduction (MAR) algorithms were evaluated using an anthropomorphic phantom with a removable jaw for the acquisition of images with and without (baseline) metal artifacts. AMPP made use of two angled CT scans to generate one artifact-reduced image set. The MAR algorithms from four vendors were applied to the images with artefacts and the analysis was performed with respective baselines. Planar HU difference maps and volumetric HU differences were analyzed.ResultsAMPP algorithm outperformed all vendors’ commercial approaches in the elimination of artefacts in the oropharyngeal region, showing the lowest percent of pixels outside +− 20 HU criteria, 4%; whereas those in the MAR-corrected images ranged from 26% to 67%. In the region of interest within the affected slices, the commercial MAR algorithms showed inconsistent performance, whereas the AMPP algorithm performed consistently well throughout the phantom’s posterior region.ConclusionsA novel MAR algorithm was evaluated and compared to four commercial algorithms using an anthropomorphic phantom. Unanimously, the analysis showed the AMPP algorithm outperformed vendors’ commercial approaches, showing the potential to be broadly implemented, improve visualizations in patient anatomy and provide accurate HU information.

Synthetic computed tomography data allows for accurate absorbed dose calculations in a magnetic resonance imaging only workflow for head and neck radiotherapy.

Few studies on magnetic resonance imaging (MRI) only head and neck radiation treatment planning exist, and none using a generally available software. The aim of this study was to evaluate the accuracy of absorbed dose for head and neck synthetic computed tomography data (sCT) generated by a commercial convolutional neural network-based algorithm. For 44 head and neck cancer patients, sCT were generated and the geometry was validated against computed tomography data (CT). The clinical CT based treatment plan was transferred to the sCT and recalculated without re-optimization, and differences in relative absorbed dose were determined for dose-volume-histogram (DVH) parameters and the 3D volume. For overall body, the results of the geometric validation were (Mean±1sd): Mean error -5±10HU, mean absolute error 67±14HU, Dice similarity coefficient 0.98±0.05, and Hausdorff distance difference 4.2±1.7mm. Water equivalent depth difference for region Th1-C7, mid mandible and mid nose were -0.3±3.4, 1.1±2.0 and 0.7±3.8mm respectively. The maximum mean deviation in absorbed dose for all DVH parameters was 0.30% (0.12Gy). The absorbed doses were considered equivalent (p-value<0.001) and the mean 3D gamma passing rate was 99.4 (range: 95.7-99.9%). The convolutional neural network-based algorithm generates sCT which allows for accurate absorbed dose calculations for MRI-only head and neck radiation treatment planning. The sCT allows for statistically equivalent absorbed dose calculations compared to CT based radiotherapy.

Validation of the preconfigured Varian Ethos Acuros XB Beam Model for treatment planning dose calculations: A dosimetric study.

Varian (Palo Alto, California, United States) recently released an online adaptation treatment platform, Ethos, which has introduced a new Dose Preview and Automated Plan Generation module despite sharing identical beam data with the existing Halcyon linac. The module incorporates a preconfigured beam model and the Acuros XB algorithm (Ethos AXB model) to generate final dose calculations from an initial fluence optimization. In this study, we comprehensively validated the accuracy of the Ethos AXB model by comparing it against the Halcyon AXB model, the Halcyon Anisotropic Analytical Algorithm (AAA) model, and measurements acquired on an Ethos linac. Results indicated that the Ethos AXB model demonstrated a comparable if not superior dosimetric accuracy to the Halcyon AXB model in basic and complex calculations, and at the same time its dosimetric accuracy in modulated and heterogeneous plans was better than that of the Halcyon AAA model. Despite the fact that the same algorithm was utilized, the Ethos AXB model and the Halcyon AXB model still exhibited variations across a range of tests, although these variations were predominantly insignificant in the clinical environment. The accuracy of the Ethos AXB model has been successfully verified in this study and is considered appropriate for the current clinical scope. On the basis of this study, clinical physicists can perform a data validation instead of a full data commissioning when implementing the Ethos system, thereby adopting a more efficient approach for Ethos installation.

A Monte Carlo model of synchrotron radiotherapy shows good agreement with experimental dosimetry measurements: Data from the imaging and medical beamline at the Australian Synchrotron

Experimental measurement of Synchrotron Radiotherapy (SyncRT) doses is challenging, especially for Microbeam Radiotherapy (MRT), which is characterised by very high dynamic ranges with spatial resolutions on the micrometer scale. Monte Carlo (MC) simulation is considered a gold standard for accurate dose calculation in radiotherapy, and is therefore routinely relied upon to produce verification data.We present a MC model for Australian Synchrotron’s Imaging and Medical Beamline (IMBL), which is capable of generating accurate dosimetry data to inform and/or verify SyncRT experiments. Our MC model showed excellent agreement with dosimetric measurement for Synchrotron Broadbeam Radiotherapy (SBBR). Our MC model is also the first to achieve validation for MRT, using two methods of dosimetry, to within clinical tolerances of 5% for a 20×20 mm2 field size, except for surface measurements at 5 mm depth, which remained to within good agreement of 7.5%. Our experimental methodology has allowed us to control measurement uncertainties for MRT doses to within 5–6%, which has also not been previously achieved, and provides a confidence which until now has been lacking in MRT validation studies.The MC model is suitable for SyncRT dose calculation of clinically relevant field sizes at the IMBL, and can be extended to include medical beamlines at other Synchrotron facilities as well. The presented MC model will be used as a validation tool for treatment planning dose calculation algorithms, and is an important step towards veterinary SyncRT trials at the Australian Synchrotron.