3D Gamma Research Articles

Machine log file-based dose verification using novel iterative CBCT reconstruction algorithm in commercial software during volumetric modulated arc therapy for prostate cancer patients.

To evaluate the utility of the use of iterative cone-beam computed tomography (CBCT) for machine log file-based dose verification during volumetric modulated arc therapy (VMAT) for prostate cancer patients. All CBCT acquisition data were used to reconstruct images with the Feldkamp-Davis-Kress algorithm (FDK-CBCT) and the novel iterative algorithm (iCBCT). The Hounsfield unit (HU)-electron density curves for CBCT images were created using the Advanced Electron Density Phantom. The I'mRT and anthropomorphic phantoms were irradiated with VMAT after CBCT registration. Subsequently, fourteen prostate cancer patients received VMAT after CBCT registration. Machine log files and both CBCT images were exported to the PerFRACTION software, and a 3D patient dose was reconstructed. Mean dose for planning target volume (PTV), the bladder, and rectum and the 3D gamma analysis were evaluated. For the phantom studies, the variation of HU values was observed at the central position surrounding the bones in FDK-CBCT. There were almost no changes in the difference of doses at the isocenter between measurement and reconstructed dose for planning CT (pCT), FDK-CBCT, and iCBCT. Mean dose differences of PTV, rectum, and bladder between iCBCT and pCT were approximately 2% lower than those between FDK-CBCT and pCT. For the clinical study, average gamma analysis for 2%/2 mm was 98.22% ± 1.07 and 98.81% ± 1.25% in FDK-CBCT and iCBCT, respectively. A similar machine log file-based dose verification accuracy is obtained for FDK-CBCT and iCBCT during VMAT for prostate cancer patients.

Predicting Three-Dimensional Dose Distribution of Prostate Volumetric Modulated Arc Therapy Using Deep Learning.

Background: Volumetric modulated arc therapy (VMAT) planning is a time-consuming process of radiation therapy. With a deep learning approach, 3D dose distribution can be predicted without the need for an actual dose calculation. This approach can accelerate the process by guiding and confirming the achievable dose distribution in order to reduce the replanning iterations while maintaining the plan quality. Methods: In this study, three dose distribution predictive models of VMAT for prostate cancer were developed, evaluated, and compared. Each model was designed with a different input data structure to train and test the model: (1) patient CT alone (PCT alone), (2) patient CT and generalized organ structure (PCTGOS), and (3) patient CT and specific organ structure (PCTSOS). The generative adversarial network (GAN) model was used as a core learning algorithm. The models were trained slice-by-slice using 46 VMAT plans for prostate cancer, and then used to predict and evaluate the dose distribution from 8 independent plans. Results: VMAT dose distribution was generated with a mean prediction time of approximately 3.5 s per patient, whereas the PCTSOS model was excluded due to a mean prediction time of approximately 17.5 s per patient. The highest average 3D gamma passing rate was 80.51 ± 5.94, while the lowest overall percentage difference of dose-volume histogram (DVH) parameters was 6.01 ± 5.44% for the prescription dose from the PCTGOS model. However, the PCTSOS model was the most reliable for the evaluation of multiple parameters. Conclusions: This dose prediction model could accelerate the iterative optimization process for the planning of VMAT treatment by guiding the planner with the desired dose distribution.

A Validation Method for EPID In Vivo Dosimetry Algorithms

The aim of this study was to develop and apply an evaluation method for assessing the accuracy of a novel 3D EPID back-projection algorithm for in vivo dosimetry. The novel algorithm of Dosimetry Check (DC) 5.8 was evaluated. A slab phantom homogeneously filled, or with air and bone inserts, was used for fluence reconstruction of different squared fields. VMAT plans in different anatomical sites were delivered on an anthropomorphic phantom. Dose distributions were measured with radiochromic films. The 2D Gamma Agreement Index (GAI) between the DC and the film dose distributions (3%, 3 mm) was computed for assessing the accuracy of the algorithm. GAIs between films and TPS and between DC and TPS were also computed. The fluence reconstruction accuracy was within 2% for all squared fields in the three slabs’ configurations. The GAI between the DC and the film was 92.7% in the prostate, 92.9% in the lung, 96.6% in the head and the neck, and 94.6% in the brain. An evaluation method for assessing the accuracy of a novel EPID algorithm was developed. The DC algorithm was shown to be able to accurately reconstruct doses in all anatomic sites, including the lung. The methodology described in the present study can be applied to any EPID back-projection in vivo algorithm.

Radiological characterization of a German pressurized water reactor based on a highly resolved method for activity analysis and dose rate calculation

Abstract. The amendment to the atomic act in 2011 results to phase out nuclear energy in Germany until the end of 2022. Subsequently, the licensee of the nuclear power plant is responsible for decommissioning and dismantling. During operation, activation of structures near the core of the reactor occur which govern the amount of radioactive waste, the dose rate distribution and dismantling strategies. Thus, a detailed radiological characterization of in-core and out-core structures is required to optimize decommissioning processes regarding the quantification and minimization of radioactive waste, radiation protection and reducing radiation exposure. These objectives are achieved using an innovative and efficient method developed and applied at the Chair of Repository Safety (Lehrstuhl für Endlagersicherheit, ELS) RWTH Aachen University. Within the framework of the joint project „Development of a methodology for activity analysis and dose rate estimation“, funded by the Federal ministry of Education and Research, approaches the objective to develop a standardized and highly resolved method to calculate time-dependent activity of components and structures near the reactor core based on operating history of the nuclear power plant and neutron fluence distribution. The approach requires the development of a detailed model for Monte-Carlo simulations which provides the basis to neutron fluence, neutron spectra and radiation transport simulations. To calculate the nuclide specific 3-Dimensional (3D) activity distribution of the entire facility, a facility-dependent activation cross section library is produced which focuses on recent nuclear databases (ENDF/B-VIII.0). A highly resolved and space-dependent 3D activity distribution of the entire facility is obtained using a modular program package, developed at ELS, including the activation code ORIGEN2. The results are produced in the form of detailed 3D activity maps. The source terms are generated on the basis of the space-dependent 3D activity distribution using an additional module of the program package. The combination of recent nuclear databases focusing on ENDF/B-VII.1 and complemented by JEFF-3.3 ensures a comprehensive characterisation of source terms. Subsequently, source terms are prepared for 3D radiation transport simulation using the Monte-Carlo method and the computer code MCNP. The simulations are conducted separately for each individual component obtaining the partial contribution of all in-core and out-core structures as well as the dose rate distribution of the entire facility. Similar to the activity calculation, the simulation results are used to generate 3D gamma flux and dose rate maps using the graphic module of the whole program system. On the basis of the radiological characterisation and in view of a high-level radiation protection these maps allow the optimum planning and realisation of the decommissioning and dismantling process of the nuclear power plant.

Comparative performance analysis of 2D and 3D gamma metrics for patient specific QA in VMAT using Octavius 4D with 2D-Array 1500.

Gamma pass percentage (GPP) is the predominant metric used for Patient Specific Quality Assurance (PSQA) in radiation therapy. The dimensionality of the measurement geometry in PSQA has evolved from 2D planar to 3D planar, and presently to state-of-the-art 3D volumetric geometry. We aim to critically examine the performance of the three-dimensional gammas vis-à-vis the older gamma metrics of lower dimensionality to determine their mutual fungibility in PSQA, using clinically approved Volumetric Arc Therapy (VMAT) plans. Gamma pass percentages derived from PSQA for VMAT plans using Octavius 4D phantom with 2D-Array 1500 and its proprietary software were recorded. 2D planar, 3D planar, and 3D volumetric gamma pass percentages were retrospectively extracted for multiple treatment plans at three sites, using three acceptance limits, and for two modes of normalization. The differences in mean pass percentages, and the pairwise correlation between geometries were calculated within limits of statistical significance. A significant increase in mean pass rates was observed from 2D planar to 3D planar geometries. The difference was less pronounced from 3D planar to 3D volumetric. 2D planar v/s 3D planar showed a significant degree of correlation among themselves, which was not seen against most of the 3D volumetric pass rates. The mean gamma pass rates show conclusive evidence of the benefits of shifting from 2D planar to higher dimensions measurement geometries, but the benefits of using 3D volumetric compared to 3D planar is not always unequivocal. The correlations show mixed results regarding the interdependence of pass percentages at different geometries.

Cycle-Generative Adversarial Network-Based Image Correction of 4-Dimensional Cone-Beam Computed Tomography for Lung Cancer Adaptive Radiation Therapy

To achieve high-precision stereotactic body radiotherapy, visualization of moving tumor/target estimation is performed using 4-dimensional cone-beam computed tomography (4D-CBCT) before each treatment fraction. Currently, respiration-correlated 4D-CBCT is achieved with subgroups of cone-beam projections into different respiratory phases. However, insufficient projections inside each phase bin cause severe imaging artifacts. Convolutional neural networks are widely used to correct CBCT specific artifacts. Previous research has shown that mean absolute error in Hounsfield units improved up to 93% over the original CBCT image. However, slight voxel-wise misalignment between the training images may lead to blurred synthesis, and hence, due to respiratory motion, it is impossible to perform 4D-CBCT imaging without organ misalignment with the 4D-computed tomography (4D-CT). Cycle-generative adversarial network (cycle-GAN) framework enforces an inverse transformation and achieves high accurate consistency even in mapping nonlinear domains. The aim of this study was to improve 4D-CBCT image quality using cycle-GAN and evaluate its use in adaptive radiation therapy (ART) planning for lung cancer.Unpaired 4D-CT and 4D-CBCT taken from publicly available datasets in 5 patients who underwent lung cancer treatment were used for training using cycle-GAN model. Then, Synthesis of 4D-CBCT (Syn-CBCT) images with improved quality conferred by the cycle-GAN model was tested in another 5 cases in the lung region. To assess pixel value deviation and image similarity, mean absolute errors (MAE) and structural similarity index measure (SSIM) were calculated for Syn-CBCT and paired 4D-CT images and compared with those of original 4D-CBCT images. Moreover, volumetric modulation arc therapy plans with a dose of 48 Gy in 4 fractions were recalculated based on Syn-CBCT images, and 3-dimensional (3D)-gamma (3%/3 mm) analysis was conducted to compare the former with ideal dose distributions observed in 4D-CT images.4D-CBCT imaging quality was improved and lung tumor regions were clearly observed after noise and artifact removal by applying the trained cycle-GAN model. MAEs were improved by 21% and SSIMs by 46% for whole images. Hence, pixel value and structural visualization in Syn-CBCT images could achieve near-ideal quality of 4D-CT images. Large structures were generally consistent with those in paired 4D-CT images, whereas fine structures (pulmonary vessels and fine bones) were not completely duplicated. However, a mean 3D gamma pass rate of > 95% was achieved in all cases.The proposed cycle-GAN method enhances 4D-CBCT image quality, making it comparable to that of 4D-CT images. Thus, clinical implementation of Syn-CBCT-based ART is feasible.

Generalizability Study of a Fluence Map Prediction Network

<h3>Purpose/Objective(s)</h3> A deep learning-based fluence map prediction network (FMPN) was developed for predicting fluence maps for given desired dose distributions. The FMPN was trained with head and neck (HN) VMAT plans only. Theoretically, the FMPN learned an inverse planning optimization procedure, so it is a general method working for other types of plans. This work is to investigate the FMPN's generalizability in various clinical scenarios apart from the training data. <h3>Materials/Methods</h3> The FMPN which maps projections of 3D dose distribution to fluence maps was trained only with clinical HN full-arc VMAT plans. The baseline performance of the FMPN was evaluated on 102 HN full-arc VMAT plans independent of training dataset. To evaluate how well it generalizes to other clinical scenarios, we designed three tests, each of which has a feature different from training scenario. In Test A, we test the FMPN on a different treatment site, prostate. In Test B, we test the network on different delivery modality with three partial-arc VMAT plans and one IMRT plan. The first partial-arc plan is for a simple pelvis case with small PTV, while the other two are derived by blocking some angles of a plan for a complicated HN case with large concave PTV. The arc coverage for three plans are 120-360, 0-180, and 0-90/180-270 degrees. The IMRT plan is re-optimized from the HN case using eight equi-spaced angles. In Test C, we test the network on different degrees of modulation (DOM), in which four plans are generated by re-optimizing the HN case using four levels of penalty on DOM. Since there's a trade-off between plan DOM and plan quality, these four plans also distribute in a wide range of plan quality. The FMPN's baseline performance and generalizability were quantified by comparing the dose of FMPN-predicted fluence maps to the ground truth dose (the desired dose, input of FMPN) using 3D Gamma passing rate. <h3>Results</h3> All results are listed in Table 1. As shown in Test A, FMPN achieved a prediction accuracy in prostate site as high as in training site (HN). In test B, performance is excellent on partial-arc VMAT and degrades on IMRT, which is reasonable because IMRT is too far away from training data (full-arc VMAT) from data distribution point of view. In Test C, FMPN can predict fluence map for plans with various DOM/quality and accuracy doesn't vary with DOM. <h3>Conclusion</h3> The FMPN trained on HN full-arc VMAT plans achieves high accuracy on various clinical scenarios without retraining the model. Our results demonstrate that the FMPN is well generalizable to other treatment site, delivery modality and various DOM, showing great potential for clinical use.

Investigate the Feasibility of Using CBCT to Assess the Dose Validation for Spot-Scanning Proton Arc (SPArc) Therapy for Advanced Staged Lung Cancer Treatment

<h3>Purpose/Objective(s)</h3> Spot-scanning proton arc therapy has been developed to improve the dose conformity and plan quality in varieties of disease indications, including advanced staged lung cancer treatment. Recently, the artificial Intelligent based synthetic CT method has been introduced in the proton clinic to assess the plan's robustness daily using CBCT. This study investigated the dose validation accuracy of using AI based synthetic-CT for SPArc in comparison with conventional IMPT delivery. <h3>Materials/Methods</h3> Twenty-seven advanced stage lung cancer patients' CT/CBCT data sets were used for training. A Generative-Adversarial-Network (GAN) model, including one generator with Residual-Unet (Res-GAN) architecture and one discriminator using a multi-layer convolutional neural net (CNN), were trained from scratch to predict synthetic CT from CBCT, with random 3D B-spline deformation as data augmentation. Another six patients with the same day re-plan CT (reCT) and CBCT were retrospectively selected for the dose validation comparison. Synthetic CT (synCT) was predicted from CBCT and compared to reCT in terms of Mean Absolute Error (MAE). Both IMPT 2-3 field and SPArc plans were generated on the reCT using the same robust optimization parameters (± 3mm setup and ± 3.5% range uncertainty) as well as planning objectives. Then, both plans were recalculated on the synCT from the same day CBCT. 3D Gamma Index was used to quantitatively assess the dose distribution discrepancy between the synCT and reCT for both IMPT and SPArc techniques. <h3>Results</h3> The result show that the image similarity between the synCT and reCT is very close with average MAE of 32.6 ± 7.5 HU. Overall, SPArc plans have superior 3D gamma index passing rate compared the multi-field IMPT plan in terms of 1%1mm; 2%2mm, 3%/3mm (<i>P</i> < 0.05) as shown in the table. In other words, SPArc effectively mitigated the dosimetric impact from the proton range differences or CT HU error in the synthetic CT. <h3>Conclusion</h3> Deep-learning CBCT-based synthetic CT could be used in dose validation not only for IMPT but also for SPArc even though SPArc plan has better dose conformity to the target. This finding paves the road map to the CBCT-based adaptation platform for SPArc adaptive therapy of lung cancer patient.

Impact of Dose Calculation Algorithm on Skin Dose for Breast Cancer Treated With Intensity Modulated Proton Therapy

<h3>Purpose/Objective(s)</h3> Historically, pencil beam algorithms (PBA), have been used for intensity-modulated proton therapy (IMPT) dose calculation. Recently, improvements in calculation speeds and a desire to generate more accurate plans have led to wider adoption of Monte Carlo (MC) algorithms for IMPT. Our goal was to ascertain the impact of both PBA and MC calculation algorithms on the skin dose in breast cancer treated with IMPT by means of measurements. <h3>Materials/Methods</h3> Twenty-one comprehensive chest wall patients were included in this study with treatment planning performed using a treatment planning system. All IMPT plans were generated with 1 or 2 en-face fields. Single- and multiple-field-optimization were used in 19 and 2 patients, respectively. The prescription doses were 50.4 Gy for 15 and 45 Gy for 6 patients, all at 1.8 Gy/fraction. The skin OAR was defined as a 3 mm thick rind anterior to the chest wall target volume. For 12 patients, the skin dose was optimized with the objectives of V95% < = 50%, V100% < = 5%, and D0.03cc < = 50.9 Gy. For the remaining 9 patients, the skin dose was optimized to control D0.03cc alone. The physical skin dose was measured with a parallel plate ionization chamber and a 2D ionization chamber array device at various depths in solid water ranging from 0.2 to 1.3 cm. The agreement of calculated to measured dose was evaluated by 2D gamma analysis (3%/3mm) and point dose comparison. <h3>Results</h3> The average gamma passing rates for PBA and MC plans were 94% and 97%, respectively. The point dose comparison showed that the measured skin doses were systematically lower than the calculated dose in PBA plans by an average of 3.1% [range: -6.76% to 0.03%], whereas the measured doses were systematically higher than the calculated dose in MC plans by an average of 1.3% [range: -0.58% to 3.51%]. For the patients who received 50.4 Gy, the skin mean dose and D1% were 48.1 [range: 45.8 to 49.6] and 50.8 [range: 49.0 to 52.6] Gy. For the patients who received 45 Gy, the skin mean dose and D1% were 45.0 [range: 44.2 to 46.2] and 46.8 [range: 46.1 to 48.0] Gy. <h3>Conclusion</h3> This study demonstrated that comparing with the PBA algorithm generated plans, the MC generated plans yield a higher gamma passing rate (97% for MC vs. 94% for PBA), and a narrower 95% of confidence interval of skin dose (4.1 for MC vs. 6.8 for PBA). Measurements showed that MC computed plans systematically under-estimate the skin dose by an average of 1.3%, in contrast to PBA computed plans which systematically over-estimate the skin dose by an average of 3.1%. The clinical implications are that for individual patient, PBA could overestimate skin dose considerably more than MC. Therefore, cautions for skin dose should to be taken when PBA is replaced by MC in clinical practice, and it is recommended that appropriate optimization objectives be used during treatment planning to control skin dose.

Corrections of photon beam profiles of small fields measured with ionization chambers using a three-layer neural network.

The purpose of this work is to study the feasibility of photon beam profile deconvolution using a feedforward neural network (NN) in very small fields (down to 0.56 × 0.56 cm2). The method's independence of the delivery and scanning system is also investigated. Lateral beam profiles of photon fields between 0.56 × 0.56 cm2 and 4.03 × 4.03 cm2 were collected on a Siemens Artiste linear accelerator. Three scanning ionization chambers (SNC 125c, PTW 31021, and PTW 31022) of sensitive volumes ranging from 0.016 cm3 to 0.108 cm3 were used with a PTW MP3 water phantom. A reference dataset was also collected with a PTW 60019 microDiamond detector to train and test individual NNs for each ionization chamber. Further testing of the trained NNs was performed with additional test data collected on an Elekta Synergy linear accelerator using a Sun Nuclear 3D Scanner. The results were evaluated with a 1D gamma analysis (0.5 mm/0.5%). After the deconvolution, the gamma passing rates increased from 54.79% to 99.58% for the SNC 125c, from 57.09% to 99.83% for the PTW 31021, and from 91.03% to 96.36% for the PTW 31022. The delivery system, the scanning system, the scanning mode (continuous vs. step‐by‐step), and the electrometer had no significant influence on the results. This study successfully demonstrated the feasibility of using NN to correct the beam profiles of very small photon fields collected with ionization chambers of various sizes. Its independence of the delivery and scanning system was also shown.

Optimisation and validation of an integrated magnetic resonance imaging-only radiotherapy planning solution.

Background and purposeMagnetic resonance imaging (MRI)-only treatment planning is gaining in popularity in radiation oncology, with various methods available to generate a synthetic computed tomography (sCT) for this purpose. The aim of this study was to validate a sCT generation software for MRI-only radiotherapy planning of male and female pelvic cancers. The secondary aim of this study was to improve dose agreement by applying a derived relative electron and mass density (RED) curve to the sCT.Method and materialsComputed tomography (CT) and MRI scans of forty patients with pelvic neoplasms were used in the study. Treatment plans were copied from the CT scan to the sCT scan for dose comparison. Dose difference at reference point, 3D gamma comparison and dose volume histogram analysis was used to validate the dose impact of the sCT. The RED values were optimised to improve dose agreement by using a linear plot.ResultsThe average percentage dose difference at isocentre was 1.2% and the mean 3D gamma comparison with a criteria of 1%/1 mm was 84.0% ± 9.7%. The results indicate an inherent systematic difference in the dosimetry of the sCT plans, deriving from the tissue densities. With the adapted REDmod table, the average percentage dose difference was reduced to −0.1% and the mean 3D gamma analysis improved to 92.9% ± 5.7% at 1%/1 mm.ConclusionsCT generation software is a viable solution for MRI-only radiotherapy planning. The option makes it relatively easy for departments to implement a MRI-only planning workflow for cancers of male and female pelvic anatomy.

Deep learning-enabled EPID-based 3D dosimetry for dose verification of step-and-shoot radiotherapy.

The study aims at a novel dosimetry methodology to reconstruct a 3D dose distribution as imparted to a virtual cylindrical phantom using an electronic portal imaging device (EPID). A deep learning-based signal processing strategy, referred to as 3DosiNet, is utilized to learn a mapping from an EPID image to planar dose distributions at given depths. The network was trained with the volumetric dose exported from the clinical treatment planning system (TPS). Given the latent inconsistency between measurements and corresponding TPS calculations, unsupervised learning is formulated in 3DosiNet to capture abstractive image features that are less sensitive to the potential variations. Validation experiments were performed using five regular fields and three clinical intensity-modulated radiation therapy (IMRT) cases. The measured dose profiles and percentage depth dose (PDD) curves were compared with those measured using standard tools in terms of the 1D gamma index. The mean gamma pass rates (2%/2mm) over the regular fields are 100% and 97.3% for the dose profile and PDD measurements, respectively. The measured volumetric dose was compared to the corresponding TPS calculation in terms of the 3D gamma index. The mean 2%/2mm gamma pass rates are 97.9% for square fields and 94.9% for the IMRT fields. The system promises to be a practical 3D dosimetric tool for pre-treatment patient-specific quality assurance and further developed for in-treatment patient dose monitoring.

The Effect of Contrast Agents on Dose Calculations of Volumetric Modulated Arc Radiotherapy Plans for Critical Structures

Radiotherapy dose calculation requires accurate Computed Tomography (CT) imaging while tissue delineation may necessitate the use of contrast agents (CA). Acquiring these two sets is a common practice in radiotherapy. This study aims to evaluate the effect of CA on the dose calculations. Two hundred and twenty-six volumetric modulated arc therapy (VMAT) patients that had planning CT with contrast (CCT) and non-contrast CT (NCCT) of different cancer sites (e.g., brain, head, and neck (H&amp;N), chest, abdomen, and pelvis) were evaluated. Treatment plans were recalculated using CCT, then compared to NCCT. The variation in Hounsfield units (HU) and dose distributions for critical structures and target volumes were analyzed using mean HU, mean and maximum relative dose values, D2%, D98%, and 3D gamma analysis. HU variations were statistically significant for most structures. However, this was not clinically significant as the difference in mean HU values was within 30 HU for soft tissue and 50 HU for lungs. Variation in target volumes’ D2% and D98% were insignificant for all sites except brain and nasopharynx. Dose maximum differences were within 2% for the majority of critical structures and target volumes. 3D gamma analysis results revealed that majority of plans satisfied the 2% and 2 mm criteria. CCT may be acquired for VMAT radiotherapy planning purposes instead of NCCT, since there is no clinically significant difference in dose calculations based on either image set.

Plastic scintillation dosimeter with a conical mirror for measuring 3D dose distribution.

To test the measurement technique of the three-dimensional (3D) dose distribution measured image by capturing the scintillation light generated using a plastic scintillator and a scintillating screen. Our imaging system constituted a column shaped plastic scintillator covered by a Gd2 O2 S:Tb scintillating screen, a conical mirror and a cooled CCD camera. The scintillator was irradiated with 6 MV photon beams. Meanwhile, the irradiated plan was prepared for the static field plans, two-field plan (2F plan) and the conformal arc plan (CA plan). The 2F plan contained 16 mm2 and 10 mm2 fields irradiated from gantry angles of 0° and 25°, respectively. The gantry was rotated counterclockwise from 45° to 315° for the CA plan. The field size was then obtained as 10 mm2 . A Monte Carlo simulation was performed in the experimental geometry to obtain the calculated 3D dose distribution as the reference data. Dose response was acquired by comparing between the reference and the measurement. The dose rate dependence was verified by irradiating the same MU value at different dose rates ranging from 100 to 600 MU/min. Deconvolution processing was applied to the measured images for the correction of light blurring. The measured 3D dose distribution was reconstructed from each measured image. Gamma analysis was performed to these 3D dose distributions. The gamma criteria were 3% for the dose difference, 2mm for the distance-to-agreement and 10% for the threshold. Dose response for the scintillation light was linear. The variation in the light intensity for the dose rate ranging from 100 to 600 MU/min was less than 0.5%, while our system presents dose rate independence. For the 3D dose measurement, blurring of light through deconvolution processing worked well. The 3D gamma passing rate (3D GPR) for the 10 × 10 mm2 , 16 × 16 mm2 , and 20 × 20 mm2 fields were observed to be 99.3%, 98.8%, and 97.8%, respectively. Reproducibility of measurement was verified. The 3D GPR results for the 2F plan and the CA plan were 99.7% and 100%, respectively. We developed a plastic scintillation dosimeter and demonstrated that our system concept can act as a suitable technique for measuring the 3D dose distribution from the gamma results. In the future, we will attempt to measure the 4D dose distribution for clinical volumetric modulated arc radiation therapy (VMAT)-SBRTplans.

The root cause analysis on failed patient-specific measurements of pencil beam scanning protons using a 2D detection array with finite size ionization chambers.

The aim of this report is to present the root cause analysis on failed patient‐specific quality assurance (QA) measurements of pencil beam scanning (PBS) protons; referred to as PBS‐QA measurement. A criterion to fail a PBS‐QA measurement is having a <95% passing rate in a 3.0%‐3.0 mm gamma index analysis. Clinically, we use a two‐dimensional (2D) gamma index analysis to obtain the passing rate. The IBA MatriXX PT 2D detection array with finite size ionization chamber was utilized. A total of 2488 measurements performed in our PBS beamline were cataloged. The percentage of measurements for the sites of head/neck, breast, prostate, and other are 53.3%, 22.7%, 10.5%, and 13.5%, respectively. The measurements with a passing rate of 100 to >94%, 94 to >88%, and <88% were 93.6%, 5.6%, and 0.8%, respectively. The percentage of failed measurements with a <95% passing rate was 10.9%. After removed the user errors of either re‐measurement or re‐analysis, 8.1% became acceptable. We observed a feature of >3% per mm dose gradient with respect to depth on the failed measurements. We utilized a 2D/three‐dimensional (3D) gamma index analysis toolkit to investigate the effect of depth dose gradient. By utilizing this 3D toolkit, 43.1% of the failed measurements were improved. A feature among measurements that remained sub‐optimal after re‐analysis was a sharp >3% per mm lateral dose gradient that may not be well handled using the detector size of 5.0 mm in‐diameter. An analysis of the sampling of finite size detectors using one‐dimensional (1D) error function showed a large dose deviation at locations of low‐dose areas between two high‐dose plateaus. User error, large depth dose gradient, and the effect of detector size are identified as root causes. With the mitigation of the root causes, the goals of patient‐specific QA, specifically detecting actual deviation of beam delivery or identifying limitations of the dose calculation algorithm of the treatment planning system, can be directly related to failure of the PBS‐QA measurements.

Calculation of Jaws-only IMRT (JO-IMRT) dose distributions based on the AAPM TG-119 test cases using Monte Carlo simulation and Prowess Panther treatment planning system

The aim of this study is to calculate the JO-IMRT dose distributions based on the AAPM TG-119 using Monte Carlo (MC) simulation and Prowess Panther treatment planning system (TPS) (Panther, Prowess Inc., Chico, CA). JO-IMRT dose distributions of AAPM TG-119 were calculated by the TPS and were recalculated by MC simulation. The DVHs and 3D gamma index using global methods implemented in the PTW-VeriSoft with 3%/3 mm were used for evaluation. JO-IMRT dose distributions calculated by TPS and MC were matched the TG-119 goals. The gamma index passing rates with 3%/3 mm were 98.7% for multi-target, 96.0% for mock prostate, 95.4% for mock head-and-neck, and 96.6% for C-shape. The dose in the planning target volumes (PTV) for TPS was larger than that for the MC. The relative dose differences in D99 between TPS and MC for multi-target are 1.52%, 0.17% and 1.40%, for the center, superior and inferior, respectively. The differences in D95 are 0.16% for C-shape; and 0.06% for mock prostate. Mock head-and-neck difference is 0.40% in D99. In contrast, the organ curve for TPS tended to be smaller than MC values. JO-IMRT dose distributions for the AAPM TG-119 calculated by the TPS agreed well with the MC.

Cone beam CT based validation of neural network generated synthetic CTs for radiotherapy in the head region.

In the past years, many different neural network-based conversion techniques for synthesizing computed tomographys (sCTs) from MR images have been published. While the model's performance can be checked during the training against the test set, test datasets can never represent the whole population. Conversion errors can still occur for special cases, for example, for unusual anatomical situations. Therefore, the performance of sCT conversion needs to be verified on a patient specific level, especially in the absence of a planning CT (pCT). In this study, the capability of cone-beam CTs (CBCTs) for the validation of sCTs generated by a neural network was investigated. 41 patients with tumors in the head region were selected. 20 of them were used for model training and 10 for validation. Different implementations of CycleGAN (with/without identity and feature loss) were used to generate sCTs. The pixel (MAE, RMSE, PSNR) and geometric error (DICE, Sensitivity, Specificity) values were reported to identify the best model. VMAT plans were created for the remaining 11 patients on the pCTs. These plans were re-calculated on sCTs and CBCTs. An automatic density overriding method ( ) and a population-based dose calculation method ( ) were employed for CBCT-based dose calculation. The dose distributions were analysed using 3D global gamma analysis, applying a threshold of 10% with respect to the prescribed dose. Differences in DVH metrics for the PTV and the organs-at-risk were compared among the dose distributions based on pCTs, sCTs, and CBCTs. The best model was the CycleGAN without identity and feature matching loss. Including the identity loss led to a metric decrease of 10% for DICE and a metric increase of 20-60HU for MAE. Using the 2%/2mm gamma criterion and pCT as reference, the mean gamma pass rates were 99.0 0.4% for sCTs. Mean gamma pass rate values comparing pCT and CBCT were 99.0 0.8% and 99.1 0.8% for the and , respectively. The mean gamma pass rates comparing sCT and CBCT resulted in 98.4 1.6% and 99.2 0.6% for and , respectively. The differences between the gamma-pass-rates of the sCT and two CBCT-based methods were not significant. The majority of deviations of the investigated DVH metrices between sCTs and CBCTs were within 2%. The dosimetric results demonstrate good agreement between sCT, CBCT, and pCT based calculations. A properly applied CBCT conversion method can serve as a tool for quality assurance procedures in an MR only radiotherapy workflow for head patients. Dosimetric deviations of DVH metrics between sCT and CBCTs of larger than 2% should be followed up. A systematic shift of approximately 1% should be taken into account when using the approach in an MR only workflow.

The dosimetric impact of deep learning-based auto-segmentation of organs at risk on nasopharyngeal and rectal cancer

PurposeTo investigate the dosimetric impact of deep learning-based auto-segmentation of organs at risk (OARs) on nasopharyngeal and rectal cancer.Methods and materialsTwenty patients, including ten nasopharyngeal carcinoma (NPC) patients and ten rectal cancer patients, who received radiotherapy in our department were enrolled in this study. Two deep learning-based auto-segmentation systems, including an in-house developed system (FD) and a commercial product (UIH), were used to generate two auto-segmented OARs sets (OAR_FD and OAR_UIH). Treatment plans based on auto-segmented OARs and following our clinical requirements were generated for each patient on each OARs sets (Plan_FD and Plan_UIH). Geometric metrics (Hausdorff distance (HD), mean distance to agreement (MDA), the Dice similarity coefficient (DICE) and the Jaccard index) were calculated for geometric evaluation. The dosimetric impact was evaluated by comparing Plan_FD and Plan_UIH to original clinically approved plans (Plan_Manual) with dose-volume metrics and 3D gamma analysis. Spearman’s correlation analysis was performed to investigate the correlation between dosimetric difference and geometric metrics.ResultsFD and UIH could provide similar geometric performance in parotids, temporal lobes, lens, and eyes (DICE, p > 0.05). OAR_FD had better geometric performance in the optic nerves, oral cavity, larynx, and femoral heads (DICE, p < 0.05). OAR_UIH had better geometric performance in the bladder (DICE, p < 0.05). In dosimetric analysis, both Plan_FD and Plan_UIH had nonsignificant dosimetric differences compared to Plan_Manual for most PTV and OARs dose-volume metrics. The only significant dosimetric difference was the max dose of the left temporal lobe for Plan_FD vs. Plan_Manual (p = 0.05). Only one significant correlation was found between the mean dose of the femoral head and its HD index (R = 0.4, p = 0.01), there is no OARs showed strong correlation between its dosimetric difference and all of four geometric metrics.ConclusionsDeep learning-based OARs auto-segmentation for NPC and rectal cancer has a nonsignificant impact on most PTV and OARs dose-volume metrics. Correlations between the auto-segmentation geometric metric and dosimetric difference were not observed for most OARs.

Impact of spot positional errors in robustly optimized intensity-modulated proton therapy plan of craniospinal irradiation.

We investigated the influence of random spot positioning errors (SPEs) on dosimetric outcomes of robustly optimized intensity-modulated proton therapy (RB-IMPT) plans in craniospinal irradiation (CSI). Six patients with CSI treated using the RB-IMPT technique were selected. An in-house MATLAB code was used to simulate a random SPE of 1mm in positive, negative, and both directions for 25%, 50%, and 75% of the total spot positions in the nominal plan. The percentage dose variation (ΔD%) in the six nominal and 54 error-introduced plans was evaluated using standard dose-volume indices, line dose difference, and 3D gamma analysis method. The introduction of a random SPE of 1mm resulted in a reduction in D99%, D98%, and D95% of both CTVs and PTVs by < 2% compared with the corresponding nominal plans. However, this leads to an increase in D1% of the lens by up to 16.9%. The line dose in the junction region showed ΔD% < 2% for the brain and upper spine and < 4% for the upper and lower spine. The 3D gamma values for 3% at 3mm and 2% at 2mm were above 99% and 95%, respectively, in all 54 error-introduced plans. The worst decrease in gamma values was observed for 1% at 1mm, with values ranging from 64 to 78% for all types of SPE. The RB-IMPT plan for CSI investigated in this study is robust enough for target coverage, even if there are random SPEs of 1mm. However, this leads to an increase in the dose to the critical organ located close to the target.

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.