Dose calculations for external photon beams in radiotherapy

Towards MR contrast independent synthetic CT generation

The use of synthetic CT (sCT) in the radiotherapy workflow would reduce costs and scan time while removing the uncertainties around working with both MR and CT modalities. The performance of deep learning (DL) solutions for sCT generation is steadily increasing, however most proposed methods were trained and validated on private datasets of a single contrast from a single scanner. Such solutions might not perform equally well on other datasets, limiting their general usability and therefore value. Additionally, functional evaluations of sCTs such as dosimetric comparisons with CT-based dose calculations better show the impact of the methods, but the evaluations are more labor intensive than pixel-wise metrics. To improve the generalization of an sCT model, we propose to incorporate a pre-trained DL model to pre-process the input MR images by generating artificial proton density, T1 and T2 maps (i.e. contrast-independent quantitative maps), which are then used for sCT generation. Using a dataset of only T2w MR images, the robustness towards input MR contrasts of this approach is compared to a model that was trained using the MR images directly. We evaluate the generated sCTs using pixel-wise metrics and calculating mean radiological depths, as an approximation of the mean delivered dose. On T2w images acquired with the same settings as the training dataset, there was no significant difference between the performance of the models. However, when evaluated on T1w images, and a wide range of other contrasts and scanners from both public and private datasets, our approach outperforms the baseline model. Using a dataset of T2w MR images, our proposed model implements synthetic quantitative maps to generate sCT images, improving the generalization towards other contrasts. Our code and trained models are publicly available.

X-ray-Sensitive Doped CaF2-Based MRI Contrast Agents for Local Radiation Dose Measurement

Ionizing radiation has become widely used in medicine, with application in diagnostic techniques, such as computed tomography (CT) and radiation therapy (RT), where X-rays are used to diagnose and treat tumors. The X-rays used in CT and, in particular, in RT can have harmful side effects; hence, an accurate determination of the delivered radiation dose is of utmost importance to minimize any damage to healthy tissues. For this, medical specialists mostly rely on theoretical predictions of the delivered dose or external measurements of the dose. To extend the practical use of ionizing radiation-based medical techniques, such as magnetic resonance imaging (MRI)-guided RT, a more precise measurement of the internal radiation dose internally is required. In this work, a novel approach is presented to measure dose in liquids for potential future in vivo applications. The strategy relies on MRI contrast agents (CAs) that provide a dose-sensitive signal. The demonstrated materials are (citrate-capped) CaF2 nanoparticles (NPs) doped with Eu3+ or Fe2+/Fe3+ ions. Free electrons generated by ionizing radiation allow the reduction of Eu3+, which produces a very small contrast in MRI, to Eu2+, which induces a strong contrast. Oxidative species generated by high-energy X-rays can be measured indirectly using Fe2+ because it oxidizes to Fe3+, increasing the contrast in MRI. Notably, in the results, a strong increase in the proton relaxation rates is observed for the Eu3+-doped NPs at 40 kV. At 6 MV, a significant increase in proton relaxation rates is observed using CaF2 NPs doped with Fe2+/Fe3+ after irradiation. The presented concept shows great promise for use in the clinic to measure in vivo local ionizing radiation dose, as these CAs can be intravenously injected in a saline solution.

"sCT-Feasibility" - a feasibility study for deep learning-based MRI-only brain radiotherapy

BACKGROUND

Radiotherapy (RT) is an important treatment modality for patients with brain malignancies. Traditionally, computed tomography (CT) images are used for RT treatment planning whereas magnetic resonance imaging (MRI) images are used for tumor delineation. Therefore, MRI and CT need to be registered, which is an error prone process. The purpose of this clinical study is to investigate the clinical feasibility of a deep learning-based MRI-only workflow for brain radiotherapy, that eliminates the registration uncertainty through calculation of a synthetic CT (sCT) from MRI data.

METHODS

A total of 54 patients with an indication for radiation treatment of the brain and stereotactic mask immobilization will be recruited. All study patients will receive standard therapy and imaging including both CT and MRI. All patients will receive dedicated RT-MRI scans in treatment position. An sCT will be reconstructed from an acquired MRI DIXON-sequence using a commercially available deep learning solution on which subsequent radiotherapy planning will be performed. Through multiple quality assurance (QA) measures and reviews during the course of the study, the feasibility of an MRI-only workflow and comparative parameters between sCT and standard CT workflow will be investigated holistically. These QA measures include feasibility and quality of image guidance (IGRT) at the linear accelerator using sCT derived digitally reconstructed radiographs in addition to potential dosimetric deviations between the CT and sCT plan. The aim of this clinical study is to establish a brain MRI-only workflow as well as to identify risks and QA mechanisms to ensure a safe integration of deep learning-based sCT into radiotherapy planning and delivery.

DISCUSSION

Compared to CT, MRI offers a superior soft tissue contrast without additional radiation dose to the patients. However, up to now, even though the dosimetrical equivalence of CT and sCT has been shown in several retrospective studies, MRI-only workflows have still not been widely adopted. The present study aims to determine feasibility and safety of deep learning-based MRI-only radiotherapy in a holistic manner incorporating the whole radiotherapy workflow.

TRIAL REGISTRATION

NCT06106997.

A dose calculation algorithm for boron neutron capture therapy using convolution/superposition method

The convolution/superposition (C/S) method originally designed for photon dose calculation was first applied for developing a treatment planning system for boron neutron capture therapy. The original concept of TEGMA (total energy generated per unit mass) was proposed to represent distinctive dose components from neutron reactions with the elements in the patient's tissue. First, neutron fluence distributions in a homogeneous brain phantom irradiated with an energy-groupwise pencil beam of 2.5 × 2.5 mm2 were calculated using the MCNP6.2 code. Then, a library of energy-groupwise TEGMA and KERMA were generated and stored in the developed C/S code. As a benchmark, dose distributions in a cuboid phantom and a human head phantom were calculated using the developed C/S and PHITS Monte Carlo codes. A neutron beam having a continuous epithermal spectrum and a square field of 22.5 × 22.5 mm2 or a circle field of 22.5 mm in diameter was assumed to be incident on the phantoms. The human head phantom was created by the pre-processing including the voxelization and transformation of test DICOM CT images. The differences in boron doses between C/S and MC ranged from 2% to 6%. In nitrogen doses, the differences were from 4% to 9%. A large discrepancy observed in hydrogen lateral dose profiles could be explained by the differences in cross-section data and recoil-proton transport algorithms of MCNP6.2 and PHITS. With isodose curves normalized at the center of the tumor in the human head phantom, they were almost identical in the range of 60%-110% for both cases. The C/S have underestimated the backscattering neutron and showed a larger absorbed dose gradient around 40% region. The calculation time of C/S using Intel i7-10700 processor was less than 1 min for both phantoms. The calculation time of PHITS using three Intel Xeon E5-2640 v4 processors was 15.5 min for the cuboid phantom and ∼380 min for the human head phantom. The proposed algorithm has the advantages of high speed while promising fair accuracy in BNCT dose calculations.

Deep Learning-Based Prediction of Radiation Therapy Dose Distributions in Nasopharyngeal Carcinomas: A Preliminary Study Incorporating Multiple Features Including Images, Structures, and Dosimetry

Purpose: Intensity-modulated radiotherapy (IMRT) is currently the most important treatment method for nasopharyngeal carcinoma (NPC). This study aimed to enhance prediction accuracy by incorporating dose information into a deep convolutional neural network (CNN) using a multichannel input method. Methods: A target conformal plan (TCP) was created based on the maximum planning target volume (PTV). Input data included TCP dose distribution, images, target structures, and organ-at-risk (OAR) information. The role of target conformal plan dose (TCPD) was assessed by comparing the TCPD-CNN (with dose information) and NonTCPD-CNN models (without dose information) using statistical analyses with the ranked Wilcoxon test (P < .05 considered significant). Results: The TCPD-CNN model showed no statistical differences in predicted target indices, except for PTV60, where differences in the D98% indicator were < 0.5%. For OARs, there were no significant differences in predicted results, except for some small-volume or closely located OARs. On comparing TCPD-CNN and NonTCPD-CNN models, TCPD-CNN's dose-volume histograms closely resembled clinical plans with higher similarity index. Mean dose differences for target structures (predicted TCPD-CNN and NonTCPD-CNN results) were within 3% of the maximum prescription dose for both models. TCPD-CNN and NonTCPD-CNN outcomes were 67.9% and 54.2%, respectively. 3D gamma pass rates of the target structures and the entire body were higher in TCPD-CNN than in the NonTCPD-CNN models (P < .05). Additional evaluation on previously unseen volumetric modulated arc therapy plans revealed that average 3D gamma pass rates of the target structures were larger than 90%. Conclusions: This study presents a novel framework for dose distribution prediction using deep learning and multichannel input, specifically incorporating TCPD information, enhancing prediction accuracy for IMRT in NPC treatment.

The NCS code of practice for the quality assurance of treatment planning systems (NCS-35)

A subcommittee of the Netherlands Commission on Radiation Dosimetry (NCS) was initiated in 2018 with the task to update and extend a previous publication (NCS-15) on the quality assurance of treatment planning systems (TPS) (Bruinviset al2005). The field of treatment planning has changed considerably since 2005. Whereas the focus of the previous report was more on the technical aspects of the TPS, the scope of this report is broader with a focus on a department wide implementation of the TPS. New sections about education, automated planning, information technology (IT) and updates are therefore added. Although the scope is photon therapy, large parts of this report will also apply to all other treatment modalities. This paper is a condensed version of these guidelines; the full version of the report in English is freely available from the NCS website (http://radiationdosimetry.org/ncs/publications). The paper starts with the scope of this report in relation to earlier reports on this subject. Next, general aspects of the commissioning process are addressed, like e.g. project management, education, and safety. It then focusses more on technical aspects such as beam commissioning and patient modeling, dose representation, dose calculation and (automated) plan optimisation. The final chapters deal with IT-related subjects and scripting, and the process of updating or upgrading the TPS.

PRIMO Monte Carlo software as a tool for commissioning of an external beam radiotherapy treatment planning system

Background

The purpose was to validate the PRIMO Monte Carlo software to be used during the commissioning of a treatment planning system (TPS).

Materials and methods

The Acuros XB v. 16.1 algorithm of the Eclipse was configured for 6 MV and 6 MV flattening-filter-free (FFF) photon beams, from a TrueBeam linac equipped with a high-definition 120-leaf multileaf collimator (MLC). PRIMO v. 0.3.64.1814 software was used with the phase space files provided by Varian and benchmarked against the reference dosimetry dataset published by the Imaging and Radiation Oncology Core-Houston (IROC-H). Thirty Eclipse clinical intensity-modulated radiation therapy (IMRT)/volumetric modulated arc therapy (VMAT) plans were verified in three ways: 1) using the PTW Octavius 4D (O4D) system; 2) the Varian Portal Dosimetry system and 3) the PRIMO software. Clinical validation of PRIMO was completed by comparing the simulated dose distributions on the O4D phantom against dose measurements for these 30 clinical plans. Agreement evaluations were performed using a 3% global/2 mm gamma index analysis.

Results

PRIMO simulations agreed with the benchmark IROC-H data within 2.0% for both energies. Gamma passing rates (GPRs) from the 30 clinical plan verifications were (6 MV/6MV FFF): 99.4% ± 0.5%/99.9% ± 0.1%, 99.8% ± 0.4%/98.9% ± 1.4%, 99.7% ± 0.4%/99.7% ± 0.4%, for the 1), 2) and 3) verification methods, respectively. Agreement between PRIMO simulations on the O4D phantom and 3D dose measurements resulted in GPRs of 97.9% ± 2.4%/99.7% ± 0.4%.

Conclusion

The PRIMO software is a valuable tool for dosimetric verification of clinical plans during the commissioning of the primary TPS.

Automated isocenter optimization approach for treatment planning for gyroscopic radiosurgery

BACKGROUND

Radiosurgery is a well-established treatment for various intracranial tumors. In contrast to other established radiosurgery platforms, the new ZAP-X® allows for self-shielding gyroscopic radiosurgery. Here, treatment beams with variable beam-on times are targeted towards a small number of isocenters. The existing planning framework relies on a heuristic based on random selection or manual selection of isocenters, which often leads to a higher plan quality in clinical practice.

PURPOSE

The purpose of this work is to study an improved approach for radiosurgery treatment planning, which automatically selects the isocenter locations for the treatment of brain tumors and diseases in the head and neck area using the new system ZAP-X® .

METHODS

We propose a new method to automatically obtain the locations of the isocenters, which are essential in gyroscopic radiosurgery treatment planning. First, an optimal treatment plan is created based on a randomly selected nonisocentric candidate beam set. The intersections of the resulting subset of weighted beams are then clustered to find isocenters. This approach is compared to sphere-packing, random selection, and selection by an expert planner for generating isocenters. We retrospectively evaluate plan quality on 10 acoustic neuroma cases.

RESULTS

Isocenters acquired by the method of clustering result in clinically viable plans for all 10 test cases. When using the same number of isocenters, the clustering approach improves coverage on average by 31 percentage points compared to random selection, 15 percentage points compared to sphere packing and 2 percentage points compared to the coverage achieved with the expert selected isocenters. The automatic determination of location and number of isocenters leads, on average, to a coverage of 97 ± 3% with a conformity index of 1.22 ± 0.22, while using 2.46 ± 3.60 fewer isocenters than manually selected. In terms of algorithm performance, all plans were calculated in less than 2 min with an average runtime of 75 ± 25 s.

CONCLUSIONS

This study demonstrates the feasibility of an automatic isocenter selection by clustering in the treatment planning process with the ZAP-X® system. Even in complex cases where the existing approaches fail to produce feasible plans, the clustering method generates plans that are comparable to those produced by expert selected isocenters. Therefore, our approach can help reduce the effort and time required for treatment planning in gyroscopic radiosurgery.

Monte Carlo modelling and validation of the elekta synergy medical linear accelerator equipped with radiosurgical cones

Monte Carlo simulations of medical linear accelerator heads help in visualizing the energy spectrum and angular spread of photons and electrons, energy deposition, and scattering from each of the head components. Hence, the purpose of this study was to validate the Monte Carlo model of the Elekta synergy medical linear accelerator equipped with stereotactic radio surgical connical collimators. For this, the Elekta synergy medical linear accelerator was modelled using the EGSnrc Monte Carlo code. The model results were validated using the measured data. The primary electron beam parameters, beam size, and energy were tuned to match the measured data; a dose profile with a field size of 40 × 40 cm² and percentage depth dose with a field size of 10 × 10 cm² were matched during tuning. The validation of the modelled data with the measurement results was performed using gamma analysis, point dose, and field size comparisons. For small radiation fields, relative output factors were also compared. The gamma analysis revealed good agreement between the Monte Carlo modeling results and the measured data. A gamma pass rate of more than 95% was obtained for field sizes of 40 × 40 cm² to 2 × 2 cm² with gamma criteria of 1% and 1 mm for the dose difference (DD) and distance to agreement (DTA), respectively; this gamma pass rate was more than 98% for the corresponding values of 2% and 2 mm for the DD and DTA, respectively. A gamma pass rate of more than 99% was obtained for a percentage depth dose with 1 mm and 1% criteria. The field size was also in good agreement with the measurement results, and the maximum deviation observed was 1.1%. The stereotactic cone field also passed this analysis with a gamma pass rate of more than 98% for dose profiles and 99% for the percentage depth dose. The small field output factor exhibited a deviation of 4.3%, 3.4%, and 1.9% for field sizes of 5 mm, 7.5 mm, and 10 mm, respectively. Thus, the Monte Carlo model of the Elekta Linear accelerator was successfully validated. The validation of radio surgical cones passed the analysis in terms of the dose profiles and percentage depth dose. The small field relative output factors exhibited deviations of up to 4.3%, and to resolve this, detector-specific and field-specific correction factors must be derived.

Application of a linear interpolation algorithm in radiation therapy dosimetry for 3D dose point acquisition

Air-vented ion chambers are generally used in radiation therapy dosimetry to determine the absorbed radiation dose with superior precision. However, in ion chamber detector arrays, the number of array elements and their spacing do not provide sufficient spatial sampling, which can be overcome by interpolating measured data. Herein, we investigated the potential principle of the linear interpolation algorithm in volumetric dose reconstruction based on computed tomography images in the volumetric modulated arc therapy (VMAT) technique and evaluated how the ion chamber spacing and anatomical mass density affect the accuracy of interpolating new data points. Plane measurement doses on 83 VMAT treatment plans at different anatomical sites were acquired using Octavius 729, Octavius1500, and MatriXX ion chamber detector arrays, followed by the linear interpolation to reconstruct volumetric doses. Dosimetric differences in planning target volumes (PTVs) and organs at risk (OARs) between treatment planning system and reconstruction were evaluated by dose volume histogram metrics. The average percentage dose deviations in the mean dose (D_mean) of PTVs reconstructed by 729 and 1500 arrays ranged from 4.7 to 7.3% and from 1.5 to 2.3%, while the maximum dose (D_max) counterparts ranged from 2.3 to 5.5% and from 1.6 to 7.6%, respectively. The average percentage dose/volume deviations of mixed PTVs and OARs in the abdomen/gastric and pelvic sites were 7.6%, 3.5%, and 7.2%, while mediastinum and lung plans showed slightly larger values of 8.7%, 5.1%, and 8.9% for 729, 1500, and MatriXX detector arrays, respectively. Our findings indicated that the smaller the spacing between neighbouring detectors and the more ion chambers present, the smaller the error in interpolating new data points. Anatomical regions with small local mass density inhomogeneity were associated with superior dose reconstruction. Given a large mass density difference in the various human anatomical structures and the characteristics of the linear interpolation algorithm, we suggest that an alternative data interpolation method should be used in radiotherapy dosimetry.

Monte Carlo modeling of Halcyon and Ethos radiotherapy beam using CAD geometry: validation and IAEA-compliant phase space

Objective.A Monte Carlo (MC) model of a Halcyon and Ethos (Varian Medical Systems, a Siemens Healthineers Company) radiotherapy beam was validated and field-independent phase space (PHSP) files were recorded above the dual-layer multileaf collimators (MLC).Approach.The treatment head geometry was modeled according to engineering drawings and the dual-layer MLC was imported from CAD (computer-aided design) files. The information for the incident electron beam was achieved from an iterative electromagnetic solver. The validation of the model was performed by comparing the dose delivered by the square MLC fields as well as complex field measurements.Main results.An electron phase space was generated from linac simulations and achieved improved MC results. The output factors for square fields were within 1% and the largest differences of 5% were found in the build-up region of PDDs and the penumbra region of profiles. With the more complicated MLC-shaped field (Fishbone), the largest differences of up to 8% were found in the MLC leaf tip region due to the uncertainty of the MLC positioning and the mechanical leaf gap value. The impact of the collimator rotation on the PHSP solution has been assessed with both small and large fields, confirming negligible effects on in-field and out-of-field dose distributions.Significance.A computational model of the Halcyon and Ethos radiotherapy beam with a high accuracy implementation of the MLC was shown to be able to reproduce the radiation beam characteristics with square fields and more complex MLC-shaped fields. The field-independent PHSP files that were produced can be used as an accurate treatment head model above the MLC, and reduce the time to simulate particle transport through treatment head components.

A body mass index-based method for "MR-only" abdominal MR-guided adaptive radiotherapy

PURPOSE

Dose calculation for MR-guided radiotherapy (MRgRT) at the 0.35 T MR-Linac is currently based on deformation of planning CTs (defCT) acquired for each patient. We present a simple and robust bulk density overwrite synthetic CT (sCT) method for abdominal treatments in order to streamline clinical workflows.

METHOD

Fifty-six abdominal patient treatment plans were retrospectively evaluated. All patients had been treated at the MR-Linac using MR datasets for treatment planning and plan adaption and defCT for dose calculation. Bulk density CTs (4M-sCT) were generated from MR images with four material compartments (bone, lung, air, soft tissue). The relative electron densities (RED) for bone and lung were extracted from contoured CT structure average REDs. For soft tissue, a correlation between BMI and RED was evaluated. Dose was recalculated on 4M-sCT and compared to dose distributions on defCTs assessing dose differences in the PTV and organs at risk (OAR).

RESULTS

Mean RED of bone was 1.17 ± 0.02, mean RED of lung 0.17 ± 0.05. The correlation between BMI and RED for soft tissue was statistically significant (p < 0.01). PTV dose differences between 4M-sCT and defCT were Dmean: -0.4 ± 1.0%, D1%: -0.3 ± 1.1% and D95%: -0.5 ± 1.0%. OARs showed D2%: -0.3 ± 1.9% and Dmean: -0.1 ± 1.4% differences. Local 3D gamma index pass rates (2%/2mm) between dose calculated using 4M-sCT and defCT were 96.8 ± 2.6% (range 89.9-99.6%).

CONCLUSION

The presented method for sCT generation enables precise dose calculation for MR-only abdominal MRgRT.

A cloud-based consultation and collaboration system for radiotherapy: Remote decision support services for community radiotherapy centers

PURPOSE

This study aimed to establish a cloud-based radiotherapy consultation and collaboration system, then investigated the practicability of remote decision support for community radiotherapy centers using the system.

METHODS AND MATERIALS

A cloud-based consultation and collaboration system for radiotherapy, OncoEvidance®, was developed to provide remote services of LINAC modeling, simulation CT data import/export, target volume and organ-at-risk delineation, prescription, and treatment planning. The system was deployed on a hybrid cloud. A federate of public nodes, each corresponding to a medical institution, are managed by a central node where a group of consultants have registered. Users can access the system through network using computing devices. The system has been tested at three community radiotherapy centers. One accelerator was modeled. 12 consultants participated the remote radiotherapy decision support and 77 radiation treatment plans had been evaluated remotely.

RESULTS

All the passing rates of per-beam dose verification are > 94% and all the passing rates of composite beam dose verification are > 99%. The average downloading time for one set of simulation CT data for one patient from Internet was within 1 min under the cloud download bandwidth of 8 Mbps and local network bandwidth of 100 Mbps. The average response time for one consultant to contour target volumes and make prescription was about 24 h. And that for one consultant to design and optimize a IMRT treatment plan was about 36 h. 100% of the remote plans passed the dosimetric criteria and could be imported into the local TPS for further verification.

CONCLUSION

The cloud-based consultation and collaboration system saved the travel time for consultants and provided high quality radiotherapy to patients in community centers. The under-staffed community radiotherapy centers could benefit from the remote system with lower cost and better treatment quality control.

Feasibility of a multigroup Boltzmann-Fokker-Planck solution for electron beam dose calculations

Legacy nuclear-reactor Boltzmann solvers start clinical deployment as an alternative to Monte Carlo (MC) codes and Fermi-Eyges semiemprical models in radiation oncology treatment planning. Today's certified clinical solvers are limited to photon beams. In this paper, ELECTR, a state-of-the-art multigroup electron cross sections generation module in NJOY is presented and validated against Lockwood's calorimetric measurements, EGS-nrc and GEANT-4 for 1-20 MeV unidirectional electron beams. The nuclear-reactor DRAGON-5 solver is upgraded to access the library and solve the Boltzmann-Fokker-Planck (BFP) equation. A variety of heterogeneous radiotherapy and radiosurgery phantom configurations were used for validation purpose. Case studies include a thorax benchmark, that of a typical breast Intra-Operative Radiotherapy and a high-heterogeneity patient-like benchmark. For all beams, [Formula: see text] of the water voxels satisfied the American Association of Physicists in Medicine accuracy criterion for a BFP-MC dose error below [Formula: see text]. At least, [Formula: see text] of adipose, muscle, bone, lung, tumor and breast voxels satisfied the [Formula: see text] criterion. The average BFP-MC relative error was about [Formula: see text] for all voxels, beams and materials combined. By irradiating homogeneous slabs from [Formula: see text] (hydrogen) to [Formula: see text] (einsteinium), we reported performance and defects of the CEPXS mode [US. Sandia National Lab., SAND-89-1685] in ELECTR for the entire periodic table. For all Lockwood's benchmarks, NJOY-DRAGON dose predictions are within the experimental data precision for [Formula: see text] of voxels.

Monte Carlo-based independent dose verification of radiosurgery HyperArc plans

PURPOSE

To investigate the feasibility of using the free PRIMO Monte Carlo software for independent dose check of cranial SRS plans designed with the Varian HyperArc (HA) technique.

MATERIALS AND METHODS

In this study, the PRIMO Monte Carlo software v. 0.3.64.1800 was used with the phase-space files (v. 2, Feb. 27, 2013) provided by Varian for 6 MV flattening-filter-free (FFF) photon beams from a Varian TrueBeam linear accelerator (linac), equipped with a Millennium 120 multileaf collimator (MLC). This configuration was validated by comparing the percentage depth doses (PDDs), lateral profiles and relative output factors (OFs) simulated in a water phantom against measurements for field sizes from 1 × 1 to 40 × 40 cm². The agreement between simulated and experimental relative dose curves was evaluated using a global (G) gamma index analysis. In addition, the accuracy of PRIMO to model the MLC was investigated (dosimetric leaf gap, tongue and groove, leaf transmission and interleaf leakage). Thirty-five HA SRS plans computed in the Eclipse treatment planning system (TPS) were simulated in PRIMO. The Acuros XB algorithm v. 16.10 (dose to medium) was used in Eclipse. Sixty targets with diameters ranging from 6 to 33 mm were included. Agreement between the dose distributions given by Eclipse and PRIMO was evaluated in terms of 3D global gamma passing rates (GPRs) for the 2 %/2 mm criteria.

RESULTS

Average GPR greater than 95 % with the 2 %(G)/1 mm criteria were obtained over the PDD and profiles of each field size. Differences between PRIMO calculated and measured OFs were within 0.5 % in all fields, except for the 1 × 1 cm² with a discrepancy of 1.5 %. Regarding the MLC modeling in PRIMO, an agreement within 3 % was achieved between calculated and experimental doses. Excellent agreement between PRIMO and Eclipse was found for the 35 HA plans. The 3D global GPRs (2 %/2 mm) for the targets and external patient contour were 99.6 % ± 1.1 % and 99.8 % ± 0.5 %, respectively.

CONCLUSIONS

According to the results described in this study, the PRIMO Monte Carlo software, in conjunction with the 6X FFF Varian phase-space files, can be used as secondary dose calculation software to check stereotactic radiosurgery plans from Eclipse using the HyperArc technique.

Maintaining dosimetric quality when switching to a Monte Carlo dose engine for head and neck volumetric-modulated arc therapy planning

Head and neck cancers present challenges in radiation treatment planning due to the large number of critical structures near the target(s) and highly heterogeneous tissue composition. While Monte Carlo (MC) dose calculations currently offer the most accurate approximation of dose deposition in tissue, the switch to MC presents challenges in preserving the parameters of care. The differences in dose‐to‐tissue were widely discussed in the literature, but mostly in the context of recalculating the existing plans rather than reoptimizing with the MC dose engine. Also, the target dose homogeneity received less attention. We adhere to strict dose homogeneity objectives in clinical practice. In this study, we started with 21 clinical volumetric‐modulated arc therapy (VMAT) plans previously developed in Pinnacle treatment planning system. Those plans were recalculated “as is” with RayStation (RS) MC algorithm and then reoptimized in RS with both collapsed cone (CC) and MC algorithms. MC statistical uncertainty (0.3%) was selected carefully to balance the dose computation time (1–2 min) with the planning target volume (PTV) dose‐volume histogram (DVH) shape approaching that of a “noise‐free” calculation. When the hot spot in head and neck MC‐based treatment planning is defined as dose to 0.03 cc, it is exceedingly difficult to limit it to 105% of the prescription dose, as we were used to with the CC algorithm. The average hot spot after optimization and calculation with RS MC was statistically significantly higher compared to Pinnacle and RS CC algorithms by 1.2 and 1.0 %, respectively. The 95% confidence interval (CI) observed in this study suggests that in most cases a hot spot of ≤107% is achievable. Compared to the 95% CI for the previous clinical plans recalculated with RS MC “as is” (upper limit 108%), in real terms this result is at least as good or better than the historic plans.

Heart atlas for retrospective cardiac dosimetry: a multi-institutional study on interobserver contouring variations and their dosimetric impact

PURPOSE

Cardiac effects after breast cancer radiation therapy potentially affect more patients as survival improves. The heart's heterogeneous radiation exposure and composition of functional structures call for establishing individual relationships between structure dose and specific late effects. However, valid dosimetry requires reliable contouring which is challenging for small volumes based on older, lower-quality computed tomography imaging. We developed a heart atlas for robust heart contouring in retrospective epidemiologic studies.

METHODS AND MATERIALS

The atlas defined the complete heart and geometric surrogate volumes for six cardiac structures: aortic valve, pulmonary valve, all deeper structures combined, myocardium, left anterior myocardium, and right anterior myocardium. We collected treatment planning records from 16 patients from 4 hospitals including dose calculations for 3D conformal tangential field radiation therapy for left-sided breast cancer. Six observers each contoured all patients. We assessed spatial contouring agreement and corresponding dosimetric variability.

RESULTS

Contouring agreement for the complete heart was high with a mean Jaccard similarity coefficient (JSC) of 89%, a volume coefficient of variation (CV) of 5.2%, and a mean dose CV of 4.2%. The left (right) anterior myocardium had acceptable agreement with 63% (58%) JSC, 9.8% (11.5%) volume CV, and 11.9% (8.0%) mean dose CV. Dosimetric agreement for the deep structures and aortic valve was good despite higher spatial variation. Low spatial agreement for the pulmonary valve translated to poor dosimetric agreement.

CONCLUSIONS

For the purpose of retrospective dosimetry based on older imaging, geometric surrogate volumes for cardiac organs at risk can yield better contouring agreement than anatomical definitions, but retain limitations for small structures like the pulmonary valve.

Determining tolerance levels for quality assurance of 3D printed bolus for modulated arc radiotherapy of the nose

Given the existing literature on the subject, there is obviously a need for specific advice on quality assurance (QA) tolerances for departments using or implementing 3D printed bolus for radiotherapy treatments. With a view to providing initial suggested QA tolerances for 3D printed bolus, this study evaluated the dosimetric effects of changes in bolus geometry and density, for a particularly common and challenging clinical situation: specifically, volumetric modulated arc therapy (VMAT) treatment of the nose. Film-based dose verification measurements demonstrated that both the AAA and the AXB algorithms used by the Varian Eclipse treatment planning system (Varian Medical Systems, Palo Alto, USA) were capable of providing sufficiently accurate dose calculations to allow this planning system to be used to evaluate the effects of bolus errors on dose distributions from VMAT treatments of the nose. Thereafter, the AAA and AXB algorithms were used to calculate the dosimetric effects of applying a range of simulated errors to the design of a virtual bolus, to identify QA tolerances that could be used to avoid clinically significant effects from common printing errors. Results were generally consistent, whether the treatment target was superficial and treated with counter-rotating coplanar arcs or more-penetrating and treated with noncoplanar arcs, and whether the dose was calculated using the AAA algorithm or the AXB algorithm. The results of this study suggest the following QA tolerances are advisable, when 3D printed bolus is fabricated for use in photon VMAT treatments of the nose: bolus relative electron density variation within [Formula: see text] (although an action level at [Formula: see text] may be permissible); bolus thickness variation within [Formula: see text] mm (or 0.5 mm variation on opposite sides); and air gap between bolus and skin [Formula: see text] mm. These tolerances should be investigated for validity with respect to other treatment modalities and anatomical sites. This study provides a set of baselines for future comparisons and a useful method for identifying additional or alternative 3D printed bolus QA tolerances.

Technical Note: validation of a material assignment method for a retrospective study of carbon-ion radiotherapy using Monte Carlo simulation

We propose a two-step method to converse human tissue materials from patient computed tomography (CT) images, which is required in dose reconstructions for a retrospective study of carbon-ion radiotherapy (CIRT) using Monte Carlo (MC) simulation. The first step was to assign the standard tissues of the International Commission on Radiological Protection reference phantoms according to the CT-number. The second step was to determine the mass density of each material based on the relationship between CT-number and stopping power ratio (Hounsfield unit [HU]-SPR) registered in treatment planning system (TPS). Direct implementation of the well-calibrated HU-SPR curve allows the reproduction of previous clinical treatments recorded in TPS without uncertainty due to a mismatch of the CT scanner or scanning conditions, whereas MC simulation with realistic human tissue materials can fulfill the out-of-field dose, which was missing in the record. To validate our proposed method, depth-dose distributions in the homogenous and heterogeneous phantoms irradiated by a 400 MeV/u carbon beam with an 8 cm spread-out Bragg peak (SOBP) were computed by the MC simulation in combination with the proposed methods and compared with those of TPS. Good agreement of the depth-dose distributions between the TPS and MC simulation (within a 1% discrepancy in range) was obtained for different materials. In contrast, fluence distributions of secondary particles revealed the necessity of MC simulation using realistic human tissue. The proposed material assignment method will be used for a retrospective study using previous clinical data of CIRT at the National Institute of Radiological Sciences (NIRS).

Accuracy Evaluation of EPL and ETAR Algorithms in the Treatment Planning Systems using CIRS Thorax Phantom

Background:

It is recommended for each set of radiation data and algorithm that subtle deliberation is done regarding dose calculation accuracy. Knowing the errors in dose calculation for each treatment plan will result in an accurate estimate of the actual dose achieved by the tumor.

Objective:

This study aims to evaluate the equivalent path length (EPL) and equivalent tissue air ratio (ETAR) algorithms in radiation dose calculation.

Material and Methods:

In this experimental study, the TEC-DOC 1583 guideline was used. Measurements and calculations were obtained for each algorithm at specific points in thorax CIRS phantom for 6 and 18 MVs and results were compared.

Results:

In the EPL, calculations were in agreement with measurements for 27 points and differences between them ranged from 0.1% to 10.4% at 6 MV. The calculations were in agreement with measurements for 21 points and differences between them ranged from 0.4% to 13% at 18 MV. In ETAR, calculations were also in consistent with measurements for 21 points, and differences between them ranged from 0.1% to 9% at 6 MV. Moreover, for 18 MV, the calculations were in agreement with measurements for 17 points and differences between them ranged from 0% to 11%.

Conclusion:

For the EPL algorithm, more dose points were in consistent with acceptance criteria. The errors in the ETAR were 1% to 2% less than the EPL. The greatest calculation error occurs in low-density lung tissue with inhomogeneities or in high-density bone. Errors were larger in shallow depths. The error in higher energy was more than low energy beam.

Dose calculation validation of a convolution algorithm in a solid water phantom

PURPOSE

The dose calculated using a convolution algorithm should be validated in a simple homogeneous water-equivalent phantom before clinical use. The dose calculation accuracy within a solid water phantom was investigated.

METHODS

The specific Gamma knife design requires a dose rate calibration within a spherical solid water phantom. The TMR10 algorithm, which approximates the phantom material as liquid water, correctly computes the absolute dose in water. The convolution algorithm, which considers electron density miscalculates the dose in water as the phantom Hounsfield units were converted into higher electron density when the original CT calibration curve was used. To address this issue, the electron density of liquid water was affected by modifying the CT calibration curve. The absolute dose calculated using the convolution algorithm was compared with that computed by the TMR10. The measured depth dose profiles were also compared to those computed by the convolution and TMR10 algorithms. A patient treatment was recalculated in the solid-water phantom and the delivery quality assurance was checked.

RESULTS

The convolution algorithm and the TMR10 calculate an absolute dose within 1% when using the modified CT calibration curve. The dose depth profile calculated using the convolution algorithms was superimposed on the TMR10 and measured dose profiles when the modified CT calibration curve was applied. The Gamma index was better than 93%.

CONCLUSIONS

Dose calculation algorithms, which consider electron density, require a CT calibration curve adapted to the phantom material to correctly compute the dose in water.

Implementation and experimental evaluation of Mega-voltage fan-beam CT using a linear accelerator

BACKGROUND

Mega-voltage fan-beam Computed Tomography (MV-FBCT) holds potential in accurate determination of relative electron density (RED) and proton stopping power ratio (SPR) but is not widely available.

OBJECTIVE

To demonstrate the feasibility of MV-FBCT using a medical linear accelerator (LINAC) with a 2.5 MV imaging beam, an electronic portal imaging device (EPID) and multileaf collimators (MLCs).

METHODS

MLCs were used to collimate MV beam along z direction to enable a 1 cm width fan-beam. Projection data were acquired within one gantry rotation and preprocessed with in-house developed artifact correction algorithms before the reconstruction. MV-FBCT data were acquired at two dose levels: 30 and 60 monitor units (MUs). A Catphan 604 phantom was used to evaluate basic image quality. A head-sized CIRS phantom with three configurations of tissue-mimicking inserts was scanned and MV-FBCT Hounsfield unit (HU) to RED calibration was established for each insert configuration using linear regression. The determination coefficient ([Formula: see text]) was used to gauge the accuracy of HU-RED calibration. Results were compared with baseline single-energy kilo-voltage treatment planning CT (TP-CT) HU-RED calibration which represented the current standard clinical practice.

RESULTS

The in-house artifact correction algorithms effectively suppressed ring artifact, cupping artifact, and CT number bias in MV-FBCT. Compared to TP-CT, MV-FBCT was able to improve the prediction accuracy of the HU-RED calibration curve for all three configurations of insert materials, with [Formula: see text] > 0.9994 and [Formula: see text] < 0.9990 for MV-FBCT and TP-CT HU-RED calibration curves of soft-tissue inserts, respectively. The measured mean CT numbers of blood-iodine mixture inserts in TP-CT drastically deviated from the fitted values but not in MV-FBCT. Reducing the radiation level from 60 to 30 MU did not decrease the prediction accuracy of the MV-FBCT HU-RED calibration curve.

CONCLUSION

We demonstrated the feasibility of MV-FBCT and its potential in providing more accurate RED estimation.

Evaluation of IGRT-Induced Imaging Doses and Secondary Cancer Risk for SBRT Early Lung Cancer Patients In Silico Study

Objectives:

This study performed dosimetry studies and secondary cancer risk assessments on using electronic portal imaging device (EPID) and cone beam computed tomography (CBCT) as image guided tools for the early lung cancer patients treated with SBRT.

Methods:

The imaging doses from MV-EPID and kV-CBCT of the Edge accelerator were retrospectively added to sixty-one SBRT treatment plans of early lung cancer patients. The MV-EPID imaging dose (6MV Photon beam) was calculated in Pinnacle TPS, and the kV-CBCT imaging dose was simulated and calculated by modeling of the kV energy beam in TPS using Pinnacle automatic modeling program. Three types of plans, namely Plan_EPID, Plan_CBCT and Plan_origin, were generated with incorporating doses of EPID, CBCT and no imaging, respectively, for analysis. The effects of imaging doses on dose-volume-histogram (DVH) and plan quality were analyzed, and the excess absolute risk (EAR) of secondary cancer for ipsilateral lung was evaluated.

Results:

The regions that received less than 50 cGy were significantly impacted by the imaging doses, while the isodose lines greater than 1000 cGy were barely changed. The DVH values of ipsilateral lung increased the most in Plan_EPID, followed by Plan_CBCT. Compared to Plan_origin on the average, the estimated EAR of ipsilateral lung in Plan_EPID increased by 3.43%, while the corresponding EAR increase in Plan_CBCT was much smaller (about 0.4%). Considering only the contribution of the imaging dose, the EAR values for the ipsilateral lung due to the MV-EPID dose in 5 years,10 years and 15 years were 1.49 cases, 2.09 cases and 2.88 cases per 10⁴PY respectively, and those due to the kV-CBCT dose were about 9 times lower, correspondingly.

Conclusions:

The imaging doses produced by MV-EPID and kV-CBCT had little effects on the target dose coverage. The secondary cancer risk caused by MV-EPID dose is more than 8.5 times that of kV-CBCT.

Report of AAPM Task Group 155: Megavoltage photon beam dosimetry in small fields and non-equilibrium conditions

Small-field dosimetry used in advance treatment technologies poses challenges due to loss of lateral charged particle equilibrium (LCPE), occlusion of the primary photon source, and the limited choice of suitable radiation detectors. These challenges greatly influence dosimetric accuracy. Many high-profile radiation incidents have demonstrated a poor understanding of appropriate methodology for small-field dosimetry. These incidents are a cause for concern because the use of small fields in various specialized radiation treatment techniques continues to grow rapidly. Reference and relative dosimetry in small and composite fields are the subject of the International Atomic Energy Agency (IAEA) dosimetry code of practice that has been published as TRS-483 and an AAPM summary publication (IAEA TRS 483; Dosimetry of small static fields used in external beam radiotherapy: An IAEA/AAPM International Code of Practice for reference and relative dose determination, Technical Report Series No. 483; Palmans et al., Med Phys 45(11):e1123, 2018). The charge of AAPM task group 155 (TG-155) is to summarize current knowledge on small-field dosimetry and to provide recommendations of best practices for relative dose determination in small megavoltage photon beams. An overview of the issue of LCPE and the changes in photon beam perturbations with decreasing field size is provided. Recommendations are included on appropriate detector systems and measurement methodologies. Existing published data on dosimetric parameters in small photon fields (e.g., percentage depth dose, tissue phantom ratio/tissue maximum ratio, off-axis ratios, and field output factors) together with the necessary perturbation corrections for various detectors are reviewed. A discussion on errors and an uncertainty analysis in measurements is provided. The design of beam models in treatment planning systems to simulate small fields necessitates special attention on the influence of the primary beam source and collimating devices in the computation of energy fluence and dose. The general requirements for fluence and dose calculation engines suitable for modeling dose in small fields are reviewed. Implementations in commercial treatment planning systems vary widely, and the aims of this report are to provide insight for the medical physicist and guidance to developers of beams models for radiotherapy treatment planning systems.

Validation of virtual water phantom software for pre-treatment verification of single-isocenter multiple-target stereotactic radiosurgery

The aim of this study was to benchmark the accuracy of the VIrtual Phantom Epid dose Reconstruction (VIPER) software for pre-treatment dosimetric verification of multiple-target stereotactic radiosurgery (SRS). VIPER is an EPID-based method to reconstruct a 3D dose distribution in a virtual phantom from in-air portal images. Validation of the VIPER dose calculation was assessed using several MLC-defined fields for a 6 MV photon beam. Central axis percent depth doses (PDDs) and output factors were measured with an ionization chamber in a water tank, while dose planes at a depth of 10 cm in a solid flat phantom were acquired with radiochromic films. The accuracy of VIPER for multiple-target SRS plan verification was benchmarked against Monte Carlo simulations. Eighteen multiple-target SRS plans designed with the Eclipse treatment planning system were mapped to a cylindrical water phantom. For each plan, the 3D dose distribution reconstructed by VIPER within the phantom was compared with the Monte Carlo simulation, using a 3D gamma analysis. Dose differences (VIPER vs. measurements) generally within 2% were found for the MLC-defined fields, while film dosimetry revealed gamma passing rates (GPRs) ≥95% for a 3%/1 mm criteria. For the 18 multiple-target SRS plans, average 3D GPRs greater than 93% and 98% for the 3%/2 mm and 5%/2 mm criteria, respectively. Our results validate the use of VIPER as a dosimetric verification tool for pre-treatment QA of single-isocenter multiple-target SRS plans. The method requires no setup time on the linac and results in an accurate 3D characterization of the delivered dose.

An automated optimization strategy to design collimator geometry for small field radiation therapy systems

PURPOSE

To develop an automated optimization strategy to facilitate collimator design for small-field radiotherapy systems.

METHODS

We developed an objective function that links the dose profile characteristics (FWHM, penumbra, and central dose rate) and the treatment head geometric parameters (collimator thickness/radii, source-to-distal-collimator distance[SDC]) for small-field radiotherapy systems. We performed optimization using a downhill simplex algorithm. We applied this optimization strategy to a linac-based radiosurgery system to determine the optimal geometry of four pencil-beam collimators to produce 5, 10, 15, and 20mm diameter photon beams (from a 6.7MeV, 2.1mmFWHM electron beam). Two different optimizations were performed to prioritize minimum penumbra or maximum central dose rate for each beam size. We compared the optimized geometric parameters and dose distributions to an existing clinical system (CyberKnife).

RESULTS

When minimum penumbra was prioritized, using the same collimator thickness and SDC (40cm) as a CyberKnife system, the optimized collimator upstream and downstream radii agreed with the CyberKnife system within 3-14%, the optimized output factors agreed within 0-8%, and the optimized transverse and percentage depth dose profiles matched those of the CyberKnife with the penumbras agreeing within 2%. However, when maximum dose rate was prioritized, allowing both the collimator thickness and SDC to change, the central dose rate for larger collimator sizes (10, 15, 20mm) could be increased by about 1.5-2 times at the cost of 1.5-2 times larger penumbras. No further improvement in central dose rate for the 5mm beam size could be achieved.

CONCLUSIONS

We developed an automated optimization strategy to design the collimator geometry for small-field radiation therapy systems. Using this strategy, the penumbra-prioritized dose distribution and geometric parameters agree well with the CyberKnife system as an example, suggesting that this system was designed to prioritize sharp penumbra. This represents proof-of-principle that an automated optimization strategy may apply to more complex collimator designs with multiple optimization parameters.

A preliminary study of a photon dose calculation algorithm using a convolutional neural network

The aim of dose calculation algorithm research is to improve the calculation accuracy while maximizing the calculation efficiency. In this study, the three-dimensional distribution of total energy release per unit mass (TERMA) and the electron density (ED) distribution are considered inputs in a method for calculating the three-dimensional dose distribution based on a convolutional neural network (CNN). Attempts are made to improve the efficiency of the collapsed cone convolution/superposition (CCCS) algorithm while providing an approach to improve the efficiency of other traditional dose calculation algorithms. Twelve sets of computed tomography (CT) images were employed for training. Data sets were generated by the CCCS algorithm with a random beam configuration. For each monoenergetic photon model, 7500 samples were generated for the training set, and 1500 samples were generated for the validation set. Training occurred for 0.5 MeV, 1 MeV, 2 MeV, 3 MeV, 4 MeV, 5 MeV, and 6 MeV monoenergetic photon models. To evaluate the usability under linac conditions, a comparison between CCCS and CNN-Dose was performed for the Mohan 6-MV spectrum for 12 additional new sets of CT images with different anatomies. A total of 1512 test samples were generated. For all anatomies, the mean value, 95% lower confidence limit (LCL) and 95% upper confidence limit (UCL) were 99.56%, 99.51% and 99.61%, respectively, at the 3%/2 mm criteria. The mean value, 95% LCL and 95% UCL were 98.57%, 98.46% and 98.67%, respectively, at the 2%/2 mm criteria. The results meet the relevant clinical requirements. In the proposed methods, the dose distribution of clinical energy can be obtained by TERMA, and the electronic density can be obtained with a CNN. This method can also be used for other traditional dose algorithms and displays potential in treatment planning, adaptive radiation therapy, and in vivo verification.

Monte Carlo calculations of radiotherapy dose in "homogeneous" anatomy

Given the substantial literature on the use of Monte Carlo (MC) simulations to verify treatment planning system (TPS) calculations of radiotherapy dose in heterogeneous regions, such as head and neck and lung, this study investigated the potential value of running MC simulations of radiotherapy treatments of nominally homogeneous pelvic anatomy. A pre-existing in-house MC job submission and analysis system, built around BEAMnrc and DOSXYZnrc, was used to evaluate the dosimetric accuracy of a sample of 12 pelvic volumetric arc therapy (VMAT) treatments, planned using the Varian Eclipse TPS, where dose was calculated with both the Analytical Anisotropic Algorithm (AAA) and the Acuros (AXB) algorithm. In-house TADA (Treatment And Dose Assessor) software was used to evaluate treatment plan complexity, in terms of the small aperture score (SAS), modulation index (MI) and a novel exposed leaf score (ELS/ELA). Results showed that the TPS generally achieved closer agreement with the MC dose distribution when treatments were planned for smaller (single-organ) targets rather than larger targets that included nodes or metastases. Analysis of these MC results with reference to the complexity metrics indicated that while AXB was useful for reducing dosimetric uncertainties associated with density heterogeneity, the residual TPS dose calculation uncertainties resulted from treatment plan complexity and TPS model simplicity. The results of this study demonstrate the value of using MC methods to recalculate and check the dose calculations provided by commercial radiotherapy TPSs, even when the treated anatomy is assumed to be comparatively homogeneous, such as in the pelvic region.

Monte Carlo commissioning of radiotherapy LINAC-Introducing an improved methodology

Purpose

Monte Carlo (MC) commissioning of medical linear accelerator (LINAC) is a time-consuming process involving a comparison between measured and simulated cross beam/lateral profiles and percentage depth doses (PDDs) for various field sizes. An agreement between these two data sets is sought by trial and error method while varying the incident electron beam parameters, such as electron beam energy or width, etc. This study aims to improve the efficiency of MC commissioning of a LINAC by assessing the feasibility of using a limited number of simulated PDDs.

Materials and methods

Using EGSnrc codes, a Varian Clinac 2100 unit has been commissioned for 6 MV photon beam, and a methodology has been proposed to identify the incident electron beam parameters in a speedier fashion. Impact of voxel size in 3-dimensions and cost functions used for comparison of the measured and simulated data have been investigated along with the role of interpolation.

Results

A voxel size of 1 × 1×0.5 cm³ has been identified as suitable for accurate and fast commissioning of the LIANC. The optimum number of simulated PDDs (required for further interpolation) has been found to be five.

Conclusion

The present study suggests that PDDs alone at times can be insufficient for an unambiguous commissioning process and should be supported by including the lateral beam profiles in the process.

Validation of PRIMO Monte Carlo Model of Clinac®iX 6MV Photon Beam

Purpose

This study aims to model 6MV photon of Clinac^®iX linear accelerator using PRIMO Monte Carlo (MC) code and to assess PRIMO as an independent MC-based dose verification and quality assurance tool.

Materials and Methods

The modeling of Clinac^®iX linear accelerator has been carried out by using PRIMO simulation software (Version 0.3.1.1681). The simulated beam parameters were compared against the measured beam data of the Clinac^®iX machine. The PRIMO simulation model of Clinac^®iX was also validated against Eclipse^® Acuros XB dose calculations in the case of both homogenous and inhomogeneous mediums. The gamma analysis method with the acceptance criteria of 2%, 2 mm was used for the comparison of dose distributions.

Results

Gamma analysis shows a minimum pass percentage of 99% for depth dose curves and 95.4% for beam profiles. The beam quality index and output factors and absolute point dose show good agreement with measurements. The validation of PRIMO dose calculations, in both homogeneous and inhomogeneous medium, against Acuros^® XB shows a minimum gamma analysis pass rate of 99%.

Conclusions

This study shows that the research software PRIMO can be used as a treatment planning system-independent quality assurance and dose verification tool in daily clinical practice. Further validation will be performed with different energies, complex multileaf collimators fields, and with dynamic treatment fields.

6X acuros algorithm validation in the presence of inhomogeneities for VMAT treatment planning

Aim

To validate the Acuros®XB (AXB) dose calculation algorithm for a 6 MV beam from the Varian TrueBeam treatment units.

Background

Currently Anisotropic Analytic Algorithm (AAA) is clinically used on authors' department but AXB could replace it for VMAT treatments in regions where inhomogeneities and free air are present.

Materials and methods

Two steps are followed in the validation process of a new dose calculation algorithm. The first is to check the accuracy of algorithm for a homogenous phantom and regular fields. Multiple fields of increasing complexity have been acquired using a Mapcheck diode array. The accuracy of the algorithm was evaluated using the gamma analysis method. The second is to validate the algorithm in the presence of heterogeneous media. Planar absolute dose was measured with GafChromic®EBT2 film and was compared with the dose calculated by AXB. Gamma analysis was performed between Mapcheck measurements and AXB dose calculations, at a range of clinical source-surface distance.

Results

For SSDs ranging from 80 to 100 cm, the results show a minimum pass rate of 95% between AXB and Mapcheck acquisition. For open 6 MV photon beam interacting with a phantom with an air gap, the agreement after the air gap between AXB and GafChromic®EBT2 is less than 1% in the 3 × 3cm² field and less than 2% in the 10 × 10 cm² field.

Conclusions

AXB has advanced modelling of lateral electron transport that enables a more accurate dose calculation in heterogeneous regions and, compared with AAA, improves accuracy between different density interfaces. This will be of particular benefit for head/neck treatments.

Scanning Beam Proton Therapy versus Photon IMRT for Stage III Lung Cancer: Comparison of Dosimetry, Toxicity, and Outcomes

Purpose

There are limited clinical data on scanning-beam proton therapy (SPT) in treating locally advanced lung cancer, as most published studies have used passive-scatter technology. There is increasing interest in whether the dosimetric advantages of SPT compared with photon therapy can translate into superior clinical outcomes. We present our experience of SPT and photon intensity modulated radiation therapy (IMRT) with clinical dosimetry and outcomes in patients with stage III lung cancer.

Methods and Materials

Patients with stage III lung cancer treated at our center between 2013 and May 2018 were identified in compliance with our institutional review board (64 patients = 34 SPT + 30 IMRT). Most proton patients were treated with pencil beam scanning (28 of 34), and 6 of 34 were treated with uniform scanning. Fisher exact test, χ² test, and Mann-Whitney test were used to compare groups. All tests were 2-sided.

Results

Patient characteristics were similar between the IMRT and SPT patients, except for worse lung function in the IMRT group. Mean dose to lung, heart, and esophagus was lower in the SPT group, with most benefit in the low-dose region (lungs, 9.7 Gy vs 15.7 Gy for SPT vs IMRT, respectively [P = .004]; heart, 7 Gy vs 14 Gy [P = .001]; esophagus, 28.2 Gy vs 30.9 Gy [P = .023]). Esophagitis and dermatitis grades were not different between the 2 groups. Grade 2+ pneumonitis was 21% in the SPT group and 40% in the IMRT group (P = .107). Changes in blood counts were not different between the 2 groups. Overall survival and progression-free survival were not different between SPT and IMRT (median overall survival, 41.6 vs 30.7 months, respectively [P = .52]; median progression-free survival, 19.5 vs 14.6 months [P = .50]).

Conclusions

We report our experience with SPT and IMRT in stage III lung cancer. Our cohort of patients treated with SPT had lower doses to normal organs (lungs, heart, esophagus) than our IMRT cohort. There was no statistically significant difference in toxicity rates or survival, although there may have been a trend toward lower rates of pneumonitis.

Technical Note: A feasibility study on deep learning-based radiotherapy dose calculation

PURPOSE

Various dose calculation algorithms are available for radiation therapy for cancer patients. However, these algorithms are faced with the tradeoff between efficiency and accuracy. The fast algorithms are generally less accurate, while the accurate dose engines are often time consuming. In this work, we try to resolve this dilemma by exploring deep learning (DL) for dose calculation.

METHODS

We developed a new radiotherapy dose calculation engine based on a modified Hierarchically Densely Connected U-net (HD U-net) model and tested its feasibility with prostate intensity-modulated radiation therapy (IMRT) cases. Mapping from an IMRT fluence map domain to a three-dimensional (3D) dose domain requires a deep neural network of complicated architecture and a huge training dataset. To solve this problem, we first project the fluence maps to the dose domain using a broad beam ray-tracing (RT) algorithm, and then we use the HD U-net to map the RT dose distribution into an accurate dose distribution calculated using a collapsed cone convolution/superposition (CS) algorithm. The model is trained on 70 patients with fivefold cross validation, and tested on a separate 8 patients.

RESULTS

It takes about 1 s to compute a 3D dose distribution for a typical 7-field prostate IMRT plan, which can be further reduced to achieve real-time dose calculation by optimizing the network. The average Gamma passing rate between DL and CS dose distributions for the 8 test patients are 98.5% (±1.6%) at 1 mm/1% and 99.9% (±0.1%) at 2 mm/2%. For comparison of various clinical evaluation criteria (dose-volume points) for IMRT plans between two dose distributions, the average difference for dose criteria is less than 0.25 Gy while for volume criteria is <0.16%, showing that the DL dose distributions are clinically identical to the CS dose distributions.

CONCLUSIONS

We have shown the feasibility of using DL for calculating radiotherapy dose distribution with high accuracy and efficiency.

Parallel beamlet dose calculation via beamlet contexts in a distributed multi-GPU framework

PURPOSE

Dose calculation is one of the most computationally intensive, yet essential tasks in the treatment planning process. With the recent interest in automatic beam orientation and arc trajectory optimization techniques, there is a great need for more efficient model-based dose calculation algorithms that can accommodate hundreds to thousands of beam candidates at once. Foundational work has shown the translation of dose calculation algorithms to graphical processing units (GPUs), lending to remarkable gains in processing efficiency. But these methods provide parallelization of dose for only a single beamlet, serializing the calculation of multiple beamlets and under-utilizing the potential of modern GPUs. In this paper, the authors propose a framework enabling parallel computation of many beamlet doses using a novel beamlet context transformation and further embed this approach in a scalable network of multi-GPU computational nodes.

METHODS

The proposed context-based transformation separates beamlet-local density and TERMA into distinct beamlet contexts that independently provide sufficient data for beamlet dose calculation. Beamlet contexts are arranged in a composite context array with dosimetric isolation, and the context array is subjected to a GPU collapsed-cone convolution superposition procedure, producing the set of beamlet-specific dose distributions in a single pass. Dose from each context is converted to a sparse representation for efficient storage and retrieval during treatment plan optimization. The context radius is a new parameter permitting flexibility between the speed and fidelity of the dose calculation process. A distributed manager-worker architecture is constructed around the context-based GPU dose calculation approach supporting an arbitrary number of worker nodes and resident GPUs. Phantom experiments were executed to verify the accuracy of the context-based approach compared to Monte Carlo and a reference CPU-CCCS implementation for single beamlets and broad beams composed by addition of beamlets. Dose for representative 4π beam sets was calculated in lung and prostate cases to compare its efficiency with that of an existing beamlet-sequential GPU-CCCS implementation. Code profiling was also performed to evaluate the scalability of the framework across many networked GPUs.

RESULTS

The dosimetric accuracy of the context-based method displays <1.35% and 2.35% average error from the existing serialized CPU-CCCS algorithm and Monte Carlo simulation for beamlet-specific PDDs in water and slab phantoms, respectively. The context-based method demonstrates substantial speedup of up to two orders of magnitude over the beamlet-sequential GPU-CCCS method in the tested configurations. The context-based framework demonstrates near linear scaling in the number of distributed compute nodes and GPUs employed, indicating that it is flexible enough to meet the performance requirements of most users by simply increasing the hardware utilization.

CONCLUSIONS

The context-based approach demonstrates a new expectation of performance for beamlet-based dose calculation methods. This approach has been successful in accelerating the dose calculation process for very large-scale treatment planning problems - such as automatic 4π IMRT beam orientation and VMAT arc trajectory selection, with hundreds of thousands of beamlets - in clinically feasible timeframes. The flexibility of this framework makes it as a strong candidate for use in a variety of other very large-scale treatment planning tasks and clinical workflows.

Distributive quality assurance and delivery of stereotactic ablative radiotherapy treatments amongst beam matched linear accelerators: A feasibility study

PURPOSE

Beam matching occurs on all linacs to some degree and when two are more are matched to each other, patients are able to be transferred between machines. Quality assurance of plans can also be performed "distributively" on any of the matched linacs. The degree to which machines are matched and how this translates to like delivery of plans has been the focus of a number of studies. This concept has not yet been explored for stereotactic techniques which require a higher degree of accuracy. This study proposes beam matching criteria which allows for the distributive delivery and quality assurance of stereotactic body radiotherapy (SBRT) plans.

METHOD

Two clinically relevant and complex volumetric modulated arc therapy (VMAT) SBRT spine and lung plans were chosen as benchmarking cases. These were delivered on nine previously beam matched linacs with quality assurance performed through ArcCheck and film exposure in the sagittal plane. Measured doses were compared to their treatment planning system predictions through gamma analysis at a range of criteria.

RESULTS

Despite differences in beam match parameters and variations in small fields, all nine linacs produced accurate deliveries with a tight deviation in the population sample. Pass rates were well above suggested tolerances at the recommended gamma criterion. Film was able to detect dose errors to a greater degree than ArcCheck.

CONCLUSION

Distributive quality assurance and delivery of stereotactic ablative radiotherapy treatments amongst beam matched linacs is certainly feasible provided the linacs are matched to a strict protocol like that suggested in this study and regular quality assurance is performed on the matched fleet. Distributive quality assurance and delivery of SBRT provides the possibility of efficiency gains for physicists as well as treatment staff.

Validation of post-treatment PET-based dosimetry software for hepatic radioembolization of Yttrium-90 microspheres

PURPOSE

Yttrium-90 (⁹⁰ Y) microsphere radioembolization enables selective internal radiotherapy for hepatic malignancies. Currently, there is no standard postdelivery imaging and dosimetry of the microsphere distribution to verify treatment. Recent studies have reported utilizing the small positron yield of ⁹⁰ Y (32 ppm) with positron emission tomography (PET) to perform treatment verification and dosimetry analysis. In this study, we validated a commercial dosimetry software, MIM SurePlan™ LiverY90 (MIM Software Inc., Cleveland, OH), for clinical use.

METHODS

A MATLAB-based algorithm for ⁹⁰ Y PET-based dosimetry was developed in-house and validated for the purpose of commissioning the commercial software. The algorithm is based on voxel S values and dosimetry formalism reported in MIRD Pamphlet 17. We validated the in-house algorithm to establish it as the ground truth by comparing results from a digital point phantom and a digital uniform cylinder to manual calculations. Once we validated our in-house MATLAB-based algorithm, we used it to perform acceptance testing and commissioning of the commercial dosimetry software, MIM SurePlan, which uses the same dosimetry formalism. A 0.4 cm/5% gamma test was performed on PET-derived dose maps from each algorithm of uniform digital and nonuniform physical phantoms filled with ⁹⁰ Y chloride solution. Average dose (D_avg ) and minimum dose to 70% (D₇₀ ) of a given volume of interest (VOI) were compared for the digital phantom, the physical phantom, and five patient cases (27 tumor VOIs), representing different clinical scenarios.

RESULTS

The gamma-pass rates were 97.26% and 97.66% for the digital and physical phantoms, respectively. The differences between D_avg and D₇₀ were 0.076% and 0.10% for the digital phantom, respectively, and <5.2% for various VOIs in the physical phantom. In the clinical cases, 96.3% of the VOIs had a difference <5% for D_avg , and 88.9% of the VOIs had a difference <5% for D₇₀ .

CONCLUSIONS

Dose calculation results from MIM SurePlan were found to be in good agreement with our in-house algorithm. This indicates that MIM SurePlan performs as it should and, hence, can be deemed accepted and commissioned for clinical use for post-implant PET-based dosimetry of ⁹⁰ Y radioembolization.

Toward automated and personalized organ dose determination in CT examinations - A comparison of two tissue characterization models for Monte Carlo organ dose calculation with a Therapy Planning System

PURPOSE

Computed tomography (CT) is a versatile tool in diagnostic radiology with rapidly increasing number of examinations per year globally. Routine adaption of the exposure level for patient anatomy and examination protocol cause the patients' exposures to become diversified and harder to predict by simple methods. To facilitate individualized organ dose estimates, we explore the possibility to automate organ dose calculations using a radiotherapy treatment planning system (TPS). In particular, the mapping of CT number to elemental composition for Monte Carlo (MC) dose calculations is investigated.

METHODS

Organ dose calculations were done for a female thorax examination test case with a TPS (Raystation™, Raysearch Laboratories AB, Stockholm, Sweden) utilizing a MC dose engine with a CT source model presented in a previous study. The TPS's inherent tissue characterization model for mapping of CT number to elemental composition of the tissues was calibrated using a phantom with known elemental compositions and validated through comparison of MC calculated dose with dose measured with Thermo Luminescence Dosimeters (TLD) in an anthropomorphic phantom. Given the segmentation tools of the TPS, organ segmentation strategies suitable for automation were analyzed for high contrast organs, utilizing CT number thresholding and model-based segmentation, and for low contrast organs utilizing water replacements in larger tissue volumes. Organ doses calculated with a selection of organ segmentation methods in combination with mapping of CT numbers to elemental composition (RT model), normally used in radiotherapy, were compared to a tissue characterization model with organ segmentation and elemental compositions defined by replacement materials [International Commission on Radiological Protection (ICRP) model], frequently favored in imaging dosimetry.

RESULTS

The results of the validation with the anthropomorphic phantom yielded mean deviations from the dose to water calculated with the RT and ICRP model as measured with TLD of 1.1% and 1.5% with maximum deviations of 6.1% and 8.7% respectively over all locations in the phantom. A strategy for automated organ segmentation was evaluated for two different risk organ groups, that is, low contrast soft organs and high contrast organs. The relative deviation between organ doses calculated with the RT model and with the ICRP model varied between 0% and 20% for the thorax/upper abdomen risk organs.

CONCLUSIONS

After calibration, the RT model in the TPS provides accurate MC dose results as compared to measurements with TLD and the ICRP model. Dosimetric feasible segmentation of the risk organs for a female thorax demonstrates a possibility for automation using the segmentation tool available in a TPS for high contrast organs. Low contrast soft organs can be represented by water volumes, but organ dose to the esophagus and thyroid must be determined using standardized organ shapes. The uncertainties of the organ doses are small compared to the overall uncertainty, at least an order of magnitude larger, in the estimates of lifetime attributable risk (LAR) based on organ doses. Large-scale and automated individual organ dose calculations could provide an improvement in cancer incidence estimates from epidemiological studies.

Towards photon radiotherapy treatment planning with high Z nanoparticle radiosensitisation agents: the Relative Biological Effective Dose (RBED) framework

A novel treatment planning framework, the Relative Biological Effective Dose (RBED), for high Z nanoparticle (NP)-enhanced photon radiotherapy is developed and tested in silico for the medical exemplar of neoadjuvant (preoperative) breast cancer MV photon radiotherapy. Two different treatment scenarios, conventional and high Z NP enhanced, were explored with a custom Geant4 application that was developed to emulate the administration of a single 2 Gy fraction as part of a 50 Gy radiotherapy treatment plan. It was illustrated that there was less than a 1% difference in the dose deposition throughout the standard and high Z NP-doped adult female phantom. Application of the RBED framework found that the extent of possible biological response with high Z NP doping was great than expected via the dose deposition alone. It is anticipated that this framework will assist the scientific community in future high Z NP-enhanced in-silico, pre-clinical and clinical trials.

Integral dose based inverse optimization objective function promises lower toxicity in head-and-neck

PURPOSE

The voxels in a CT data sets contain density information. Besides its use in dose calculation density has no other application in modern radiotherapy treatment planning. This work introduces the use of density information by integral dose minimization in radiotherapy treatment planning for head-and-neck squamous cell carcinoma (HNSCC).

MATERIALS AND METHODS

Eighteen HNSCC cases were studied. For each case two intensity modulated radiotherapy (IMRT) plans were created: one based on dose-volume (DV) optimization, and one based on integral dose minimization (Energy hereafter) inverse optimization. The target objective functions in both optimization schemes were specified in terms of minimum, maximum, and uniform doses, while the organs at risk (OAR) objectives were specified in terms of DV- and Energy-objectives respectively. Commonly used dosimetric measures were applied to assess the performance of Energy-based optimization. In addition, generalized equivalent uniform doses (gEUDs) were evaluated. Statistical analyses were performed to estimate the performance of this novel inverse optimization paradigm.

RESULTS

Energy-based inverse optimization resulted in lower OAR doses for equivalent target doses and isodose coverage. The statistical tests showed dose reduction to the OARs with Energy-based optimization ranging from ∼2% to ∼15%.

CONCLUSIONS

Integral dose minimization based inverse optimization for HNSCC promises lower doses to nearby OARs. For comparable therapeutic effect the incorporation of density information into the optimization cost function allows reduction in the normal tissue doses and possibly in the risk and the severity of treatment related toxicities.

Comparative study between Acuros XB algorithm and Anisotropic Analytical Algorithm in the case of heterogeneity for the treatment of lung cancer

The aim of this study was to investigate the impact of heterogeneity on the dose calculation for two algorithms implemented in the TPS “Analytical Anisotropic Algorithm (AAA) and Acuros XB” and validated the use of Acuros XB algorithm in clinical routine. First, we compare the dose calculated by these algorithms and the dose measured at the given point P, which is found after heterogeneity insert. Second, we extend our work on clinical cases that present a complex heterogeneity. By evaluating the impact of the choice of the algorithm on the dose coverage of the tumor, and the dose received by the organs at risk for 20 patients affected by lung cancer.

The result of our phantom study showed a good agreement with several studies that showed the superiority of the Acuros XB over the AAA in predicting dose when it concerns heterogeneous media. The treatment plans for 20 lung cancers were calculated by two algorithms AAA and Acuros XB, the results show a statistical significant difference between algorithms for Homogeneity Index and the maximum dose of planning target volume (HI: 0.11±0.01 vs 0.05±0.01 p = 0.04; Dmax: 69.30±3.12 vs 68.51±2.64 p = 0.02). Instead, no statistically significant difference was observed for conformity index CI and mean dose (CI: 0.98±0.18 vs 0.99±0.14 p = 0.33; Dmean: 66.3±0.65 vs 66.10 ±0.61 p = 0.54). For organs at risk, the maximum dose for spinal cord, mean dose and D37 % of lung minus GTV (dose receiving 37% of lung volume) were found to be lower for AAA plans than Acuros XB and the differences were statistically significant (p<0.05). For the heart D33% and D67% were found to be higher for AAA plans than Acuros XB and the differences were statistically significant (p<0.05), but No difference was observed for D100% of the heart.

The use of the AXB algorithm is suitable in the case of presence of heterogeneity, because it allows to have a better accuracy close to the Monte Carlo calculation.

Validation of the Acuros XB dose calculation algorithm versus Monte Carlo for clinical treatment plans

PURPOSE

The two distinct dose computation paradigms of Boltzmann equation solvers and Monte Carlo simulation both promise in principle maximum accuracy. In practice, clinically acceptable calculation times demand approximations and numerical short-cuts on one hand, and modeling the beam characteristics of a real linear accelerator to the required accuracy on the other. A thorough benchmark of both algorithm types therefore needs to start with beam modeling, and needs to include a number of clinically challenging treatment plans.

METHODS

The Acuros XB (v 13.7, Varian Medical Systems) and SciMoCa (v 1.0, Scientific RT) algorithms were commissioned for the same Varian Clinac accelerator for beam qualities 6 and 15 MV. Beam models were established with water phantom measurements and MLC calibration protocols. In total, 25 patients of five case classes (lung/three-dimensional (3D) conformal, lung/IMRT, head and neck/VMAT, cervix/IMRT, and rectum/VMAT) were randomly selected from the clinical database and computed with both algorithms. Statistics of 3D gamma analysis for various dose/distance-to-agreement (DTA) criteria and differences in selected DVH parameters were analyzed.

RESULTS

The percentage of points fulfilling a gamma evaluation was scored as the gamma agreement index (GAI), denoted as G(ΔD, DTA). G(3,3), G(2,2), and G(1,1) were evaluated for the full body, PTV, and selected organs at risk (OARs). For all patients, G(3,3) ≥ 99.9% and G(2,2) > 97% for the body. G(1,1) varied among the patients. However, for all patients, G(1,1) > 70% and G(1,1) > 80% for 68% of the patients. For each patient, the mean dose deviation was ΔD < 1% for the body, PTV, and all evaluated OARs, respectively. In dense bone and at off-axis distance > 10 cm, the Acuros algorithm yielded slightly higher doses. In the first layer of voxels of the patient surface, the calculated doses deviated between the algorithms. However, at the second voxel, good agreement was observed. The differences in D(98%PTV) were <1.9% between the two algorithms and for 76% of the patients, deviations were below 1%.

CONCLUSIONS

Overall, an outstanding agreement was found between the Boltzmann equation solver and Monte Carlo. High-accuracy dose computation algorithms have matured to a level that their differences are below common experimental detection thresholds for clinical treatment plans. Aside from residual differences which could be traced back to implementation details and fundamental cross-section data, both algorithms arrive at identical dose distributions.

Spatial frequency performance limitations of radiation dose optimization and beam positioning

The flexibility and sophistication of modern radiotherapy treatment planning and delivery methods have advanced techniques to improve the therapeutic ratio. Contemporary dose optimization and calculation algorithms facilitate radiotherapy plans which closely conform the three-dimensional dose distribution to the target, with beam shaping devices and image guided field targeting ensuring the fidelity and accuracy of treatment delivery. Ultimately, dose distribution conformity is limited by the maximum deliverable dose gradient; shallow dose gradients challenge techniques to deliver a tumoricidal radiation dose while minimizing dose to surrounding tissue. In this work, this 'dose delivery resolution' observation is rigorously formalized for a general dose delivery model based on the superposition of dose kernel primitives. It is proven that the spatial resolution of a delivered dose is bounded by the spatial frequency content of the underlying dose kernel, which in turn defines a lower bound in the minimization of a dose optimization objective function. In addition, it is shown that this optimization is penalized by a dose deposition strategy which enforces a constant relative phase (or constant spacing) between individual radiation beams. These results are further refined to provide a direct, analytic method to estimate the dose distribution arising from the minimization of such an optimization function. The efficacy of the overall framework is demonstrated on an image guided small animal microirradiator for a set of two-dimensional hypoxia guided dose prescriptions.

G4DARI: Geant4/GATE based Monte Carlo simulation interface for dosimetry calculation in radiotherapy

Monte Carlo (MC) simulation is widely recognized as an important technique to study the physics of particle interactions in nuclear medicine and radiation therapy. There are different codes dedicated to dosimetry applications and widely used today in research or in clinical application, such as MCNP, EGSnrc and Geant4. However, such codes made the physics easier but the programming remains a tedious task even for physicists familiar with computer programming. In this paper we report the development of a new interface GEANT4 Dose And Radiation Interactions (G4DARI) based on GEANT4 for absorbed dose calculation and for particle tracking in humans, small animals and complex phantoms. The calculation of the absorbed dose is performed based on 3D CT human or animal images in DICOM format, from images of phantoms or from solid volumes which can be made from any pure or composite material to be specified by its molecular formula. G4DARI offers menus to the user and tabs to be filled with values or chemical formulas. The interface is described and as application, we show results obtained in a lung tumor in a digital mouse irradiated with seven energy beams, and in a patient with glioblastoma irradiated with five photon beams. In conclusion, G4DARI can be easily used by any researcher without the need to be familiar with computer programming, and it will be freely available as an application package.

Image guidance doses delivered during radiotherapy: Quantification, management, and reduction: Report of the AAPM Therapy Physics Committee Task Group 180

BACKGROUND

With radiotherapy having entered the era of image guidance, or image-guided radiation therapy (IGRT), imaging procedures are routinely performed for patient positioning and target localization. The imaging dose delivered may result in excessive dose to sensitive organs and potentially increase the chance of secondary cancers and, therefore, needs to be managed.

AIMS

This task group was charged with: a) providing an overview on imaging dose, including megavoltage electronic portal imaging (MV EPI), kilovoltage digital radiography (kV DR), Tomotherapy MV-CT, megavoltage cone-beam CT (MV-CBCT) and kilovoltage cone-beam CT (kV-CBCT), and b) providing general guidelines for commissioning dose calculation methods and managing imaging dose to patients.

MATERIALS & METHODS

We briefly review the dose to radiotherapy (RT) patients resulting from different image guidance procedures and list typical organ doses resulting from MV and kV image acquisition procedures.

RESULTS

We provide recommendations for managing the imaging dose, including different methods for its calculation, and techniques for reducing it. The recommended threshold beyond which imaging dose should be considered in the treatment planning process is 5% of the therapeutic target dose.

DISCUSSION

Although the imaging dose resulting from current kV acquisition procedures is generally below this threshold, the ALARA principle should always be applied in practice. Medical physicists should make radiation oncologists aware of the imaging doses delivered to patients under their care.

CONCLUSION

Balancing ALARA with the requirement for effective target localization requires that imaging dose be managed based on the consideration of weighing risks and benefits to the patient.

Klein-Nishina electronic cross-section, Compton cross sections, and buildup factor of wax for radiation shielding and protection

Klein-Nishina scattering cross-sections, Compton scattering, mass attenuation and energy transfer cross-sections, linear attenuation coefficient and buildup factor of 99.99% pure paraffin wax (Carbon = 85.14%, Hydrogen = 14.86%). are calculated using 0.662, 0.835, 1.17 and 1.33 MeV γ-rays. The mentioned γ-rays were obtained from Cs-137, Mn-54 and Co-60 radioisotopes. Gamma rays obtained from these radioisotopes were passed through circular shaped wax slices and allowed to fall on a NaI detector. The thickness of wax slices were 0.33-2.9 cm with 6 cm diameter. Lead collimator of 1 cm diameter hole in the middle was used to obtain a collimated beam for narrow beam geometry. Broad beam geometry was used by removing the collimator to investigate buildup factor. Results show that Klein-Nishina electronic cross-section, Compton mass attenuation coefficient and Compton energy transfer coefficient all decrease with increasing photon energy. Linear attenuation coefficients μ = 0.0532 cm^-1 for 1.17 MeV beam and μ = 0.0419 cm^-1 for 1.33 MeV γ-rays were obtained for wax. Variations in buildup factors are observed with increasing thickness of wax for 1.17 and 1.33 MeV beams.