3D Gamma Index Research Articles

Hybrid 3D analytical linear energy transfer calculation algorithm based on precalculated data from Monte Carlo simulations

The dose-averaged linear energy transfer (LETd ) for intensity-modulated proton therapy (IMPT) calculated by one-dimensional (1D) analytical models deviates from more accurate but time-consuming Monte Carlo (MC) simulations. We developed a fast hybrid three-dimensional (3D) analytical LETd calculation that is more accurate than 1D analytical model. We used the Geant4 MC code to generate 3D LETd distributions of monoenergetic proton beams in water for all energies and used a customized error function to fit the LETd lateral profiles at various depths to the MC simulation. The 3D LETd calculation kernel was a lookup table of these fitted coefficients, and LETd was determined directly from spot energies and voxel coordinates during analytical dose calculations. We validated our new method by comparing the calculated LETd distributions to MC results using 3D Gamma index analysis with 3%/2mm criteria in 12 patient geometries. The significance of the improvement in Gamma index analysis passing rates over the 1D analytical model was determined using the Wilcoxon rank-sum test. The passing rate of 3D Gamma analysis comparing LETd distributions from the hybrid 3D method and the 1D method to MC simulations was significantly improved from 94.0%±2.5% to 98.0%±1.0% (P=0.0003). The typical time to calculate dose and LETd simultaneously using an Intel Xeon E5-2680 2.50GHz workstation was approximately 2.5min. Our new method significantly improved the LETd calculation accuracy compared to the 1D method while maintaining significantly shorter calculation time even comparing with the GPU-based fast MC code.

Dosimetric performance of the Elekta Unity MR-linac system: 2D and 3D dosimetry in anthropomorphic inhomogeneous geometry

Following the clinical introduction of the Elekta Unity MR-linac, there is an urgent need for development of dosimetry protocols and tools, not affected by the presence of a magnetic field. This work presents a benchmarking methodology comprising 2D/3D passive dosimetry and involving on-couch adaptive treatment planning, a unique step in MR-linac workflows.Two identical commercially available 3D-printed head phantoms (featuring realistic bone anatomy and MR/CT contrast) were employed. One phantom incorporated a film dosimetry insert, while the second was filled with polymer gel. Gel dose-response characteristics were evaluated under the Unity irradiation and read-out conditions, using vials and a cubic container filled with gel from the same batch.Treatment plan for the head phantoms involved a hypothetical large C-shape brain lesion, partly surrounding the brainstem. An IMRT step-and-shoot 7-beam plan was employed. Pre-treatment on-couch MR-images were acquired in order for the treatment planning system to calculate the virtual couch shifts and perform adaptive planning. Absolute 2D and relative 3D measurements were compared against calculations related to both adapted and original plans. Real-time dose accumulation monitoring in the gel-filled phantom was also performed.Results from the vials and cubic container suggest that gel dose-response is linear in the dose range investigated and signal integrity is mature at the read-out timings considered. Head phantom 2D and 3D measurements agreed well with calculations with 3D gamma index passing rates above 90% in all cases, even with the most stringent criteria used (2 mm/2%). By exploiting the 3D information provided by the gel, comparison also involved DVHs, dose-volume and plan quality metrics, which also reflected the agreement between adapted and delivered plans within ±4%. No considerable discrepancies were detected between adapted and original plans.A novel methodology was developed and implemented, suitable for QA procedures in Unity. TPS calculations were validated within the experimental uncertainties involved.

Synthetic CT reconstruction using a deep spatial pyramid convolutional framework for MR-only breast radiotherapy.

The superior soft-tissue contrast achieved using magnetic resonance imaging (MRI) compared to x-ray computed tomography (CT) has led to the popularization of MRI-guided radiation therapy (MR-IGRT), especially in recent years with the advent of first and second generation MRI-based therapy delivery systems for MR-IGRT. The expanding use of these systems is driving interest in MRI-only RT workflows in which MRI is the sole imaging modality used for treatment planning and dose calculations. To enable such a workflow, synthetic CT (sCT) data must be generated based on a patient's MRI data so that dose calculations may be performed using the electron density information derived from CT images. In this study, we propose a novel deep spatial pyramid convolutional framework for the MRI-to-CT image-to-image translation task and compare its performance to the well established U-Net architecture in a generative adversarial network (GAN) framework. Our proposed framework utilizes atrous convolution in a method named atrous spatial pyramid pooling (ASPP) to significantly reduce the total number of parameters required to describe the model while effectively capturing rich, multi-scale structural information in a manner that is not possible in the conventional framework. The proposed framework consists of a generative model composed of stacked encoders and decoders separated by the ASPP module, where atrous convolution is applied at increasing rates in parallel to encode large-scale features. The performance of the proposed method is compared to that of the conventional GAN framework in terms of the time required to train the model and the image quality of the generated sCT as measured by the root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) depending on the size of the training data set. Dose calculations based on sCT data generated using the proposed architecture are also compared to clinical plans to evaluate the dosimetric accuracy of the method. Significant reductions in training time and improvements in image quality are observed at every training data set size when the proposed framework is adopted instead of the conventional framework. Over 1042 test images, values of 17.7±4.3HU, 0.9995±0.0003, and 71.7±2.3 are observed for the RMSE, SSIM, and PSNR metrics, respectively. Dose distributions calculated based on sCT data generated using the proposed framework demonstrate passing rates equal to or greater than 98% using the 3D gamma index with a 2%/2mm criterion. The deep spatial pyramid convolutional framework proposed here demonstrates improved performance compared to the conventional GAN framework that has been applied to the image-to-image translation task of sCT generation. Adopting the method is a first step toward an MRI-only RT workflow that enables widespread clinical applications for MR-IGRT including online adaptive therapy.

Dosimetric evaluation of lung treatment plans produced by the Prowess Panther system using Monte Carlo simulation

Purpose: This study evaluated the accuracy of lung dose calculation done by the fast photon effective (FPE) and the collapsed cone convolution (CCC) algorithms of the Prowess Panther treatment planning system (TPS) using Monte Carlo (MC) simulation. Materials and methods: A set of treatment plans of test cases including an acrylic phantom, the QUASAR multi-purpose body phantom, and one lung cancer patient, were created the system to assess the accuracy of the FPE and CCC algorithms. The DICOM-RT files of the plans were imported to the EGSnrc-based Monte Carlo simulation for dose calculations. The plans generated by the TPS and Monte Carlo simulation were compared using relative dose error comparison and 3D gamma index. The gamma index, using global methods, was implemented in PTW-VeriSoft with 3%/3 mm, 2%/2 mm criteria. Results: There was a good agreement between Monte Carlo-simulated and TPS-calculated doses for both the QUASAR multi-purpose body phantom and one lung cancer patient. However, discrepancies for the FPE algorithm were found to be 10% in the inhomogeneous medium such as the lung. Conclusions: The FPE algorithm may not accurately predict the dose distributions in and near the inhomogeneous structures. Monte Carlo simulation and CCC algorithm are more accurate than the FPE algorithm in calculating the dose in an inhomogeneous medium. The FPE and CCC algorithms must be validated before clinical implementation of the system.

MRI-based iPAGAT polymer gel dosimetry using fast recovery spin echo sequences

In an estimation of iPAGAT polymer gel dosimeters using a magnetic resonance imaging (MRI) system, the dosimetric characteristics were investigated with two echo sequences: a spin echo (SE) and a fast recovery fast spin echo (FRFSE) sequence. FRFSE can shorten total scan time compared to SE because of its short repetition time (TR) and long echo train length (ETL). Although the R2-dose response measured from FRFSE was decreased compared to SE, both responses were fitted quadratic curve with a high correlation coefficient. In addition, their calibrated dose distributions showed high conformity to the planned data in the three-dimensional (3D) gamma index, dose volume histogram (DVH) and surface rendering analyses; however, results of SE were a little superior to FRFSE. In conclusion, it is suggested that FRFSE can present the accurate characteristics of polymer gel dosimeters under the optimum parameters.

Generating synthesized computed tomography (CT) from cone-beam computed tomography (CBCT) using CycleGAN for adaptive radiation therapy

Throughout the course of delivering a radiation therapy treatment, which may take several weeks, a patient’s anatomy may change drastically, and adaptive radiation therapy (ART) may be needed. Cone-beam computed tomography (CBCT), which is often available during the treatment process, can be used for both patient positioning and ART re-planning. However, due to the prominent amount of noise, artifacts, and inaccurate Hounsfield unit (HU) values, the dose calculation based on CBCT images could be inaccurate for treatment planning. One way to solve this problem is to convert CBCT images to more accurate synthesized CT (sCT) images. In this work, we have developed a cycle-consistent generative adversarial network framework (CycleGAN) to synthesize CT images from CBCT images. This model is capable of image-to-image translation using unpaired CT and CBCT images in an unsupervised learning setting. The sCT images generated from CBCT through this CycleGAN model are visually and quantitatively similar to real CT images with decreased mean absolute error (MAE) from 69.29 HU to 29.85 HU for head-and-neck (H&N) cancer patients. The dose distributions calculated on the sCT by CycleGAN demonstrated a higher accuracy than those on CBCT in a 3D gamma index analysis with increased gamma index pass rate from 86.92% to 96.26% under 1 mm/1% criteria, when using the deformed planning CT image (dpCT) as the reference. We also compared the CycleGAN model with other unsupervised learning methods, including deep convolutional generative adversarial networks (DCGAN) and progressive growing of GANs (PGGAN), and demonstrated that CycleGAN outperformed the other two models. A phantom study has been conducted to compare sCT with dpCT, and the increase of structural similarity index from 0.91 to 0.93 shows that CycleGAN performed better than DIR in terms of preserving anatomical accuracy.

Characterization of a novel 3D printed patient specific phantom for quality assurance in cranial stereotactic radiosurgery applications

In single-isocenter stereotactic radiosurgery/radiotherapy (SRS/SRT) intracranial applications, multiple targets are being treated concurrently, often involving non-coplanar arcs, small photon beams and steep dose gradients. In search for more rigorous quality assurance protocols, this work presents and evaluates a novel methodology for patient-specific pre-treatment plan verification, utilizing 3D printing technology.In a patient’s planning CT scan, the external contour and bone structures were segmented and 3D-printed using high-density bone-mimicking material. The resulting head phantom was filled with water while a film dosimetry insert was incorporated. Patient and phantom CT image series were fused and inspected for anatomical coherence. HUs and corresponding densities were compared in several anatomical regions within the head. Furthermore, the level of patient-to-phantom dosimetric equivalence was evaluated both computationally and experimentally. A single-isocenter multi-focal SRS treatment plan was prepared, while dose distributions were calculated on both CT image series, using identical calculation parameters. Phantom- and patient-derived dose distributions were compared in terms of isolines, DVHs, dose-volume metrics and 3D gamma index (GI) analysis. The phantom was treated as if the real patient and film measurements were compared against the patient-derived calculated dose distribution.Visual inspection of the fused CT images suggests excellent geometric similarity between phantom and patient, also confirmed using similarity indices. HUs and densities agreed within one standard deviation except for the skin (modeled as ‘bone’) and sinuses (water-filled). GI comparison between the calculated distributions resulted in passing rates better than 97% (1%/1 mm). DVHs and dose-volume metrics were also in satisfying agreement. In addition to serving as a feasibility proof-of-concept, experimental absolute film dosimetry verified the computational study results. GI passing rates were above 90%.Results of this work suggest that employing the presented methodology, patient-equivalent phantoms (except for the skin and sinuses areas) can be produced, enabling literally patient-specific pre-treatment plan verification in intracranial applications.

35 Monte Carlo dose calculation of VMAT clinical treatment plans for Novalis TrueBeam STx linac using PRIMO

Introduction The aim of patient specific QA is to check if calculated dose by TPS corresponds to delivered dose to patient by the linac. With complex techniques like VMAT this is both more important and more challenging. QA both focus on dose calculation and dose delivery. Regarding dose calculation, Monte Carlo methods are considered as golden standard. Methods We use PRIMO to calculate VMAT clinical plans, for a TrueBeam. PRIMO combines the PENELOPE Monte Carlo code, the geometry of some linacs including their MLC, and a graphical user interface. PRIMO is adapted to perform Monte Carlo simulation in a clinical environment. The main reason is the geometry definition. Version 0.1.5 officially doesn’t support VMAT plans calculation. With in-house additional scripts and huge efforts, we succeeded to calculate VMAT plans, on CT, with TrueBeam. Version 0.3.1 now allows VMAT plans calculation in a very simple way. This latest version discontinues the support of Elekta linacs. We compared dose from Eclipse (Acuros 13.7, dose to medium) with PRIMO. As the beam production geometry is not known for TrueBeam, we use phase space made available by Varian. PRIMO 0.3.1 includes an additional Monte Carlo code: DPM. It is optimized for radiotherapy but with a reduced accuracy in air. Results With PRIMO 0.3.1, we compared dose for 11 clinical plans. 3D gamma index (2%/2 mm) is always above 95%. For 2 lung cases, this required use of PENELOPE rather than DPM. Calculation time with DPM vary from 20 to 60 min, mainly depending on PTV size. Calculation time is 6 times longer with PENELOPE (24 cores workstation). Uncertainty (2 sigma) is about 2.5%. Median dose difference is lower than 1%. We also successfully calculated SRS plan from iPlan. Conclusions PRIMO is a valid tool to calculate VMAT clinical plan, for a TrueBeam. It is adapted for use in a clinical environment.

29 Pseudo-CT generation for MRI-only radiotherapy treatment planning: comparison between patch-based, atlas-based and bulk density methods

Introduction Several methods have been developed to generate pseudo-CT (pCT) from MRI for dose calculation. The objective of this study was to compare an original non-local mean patch-basatlas-based method (ABM) and a bulk density method (BDM) (Fig.1). Methods Thirty-nine patients received VMAT for prostate cancer. T2-weighted MR images were acquired in addition to the planning CT images. pCT were generated from MRI by three methods: an original non-local mean PBM, ABM [1] (leave-one-out strategy) and BDM (water–air-bone density assignment). The PBM was carried out with feature extraction and approximate nearest neighbor search in a training cohort of 25 patients. The methods were compared in a validation cohort of 14 patients. Imaging endpoints were mean absolute error (MAE) and mean error (ME) of Hounsfield unit (HU) from voxel-wise comparisons between pCT and reference CT. Dosimetric endpoints consisted on the absolute mean DVH differences between the reference CT and the pCTs, for the organs at risk and PTV. 3D gamma index analyses (local, 1%/1 mm) were also performed. Results The PBM shows significantly less difference in MAE and ME compared to the ABM and BDM. The differences between pCT from PBM and CT were 0.7% for prostate PTV V 95 % , 0.5% for rectum V 70 Gy and 0.2% for bladder V 50 Gy . Compared to ABM, PBM showed significantly lower DVH difference: for the prostate PTV (from 70 to 78 Gy), the rectum (from 8.5 to 29 Gy, 40 to 48 Gy and 61 to 73 Gy) and the bladder (from 12 to 78 Gy). All the gamma passrate values were significantly higher with the PBM, than those of ABM and BDM. Conclusions In order to generate pCT from MRI for dose calculation, our non-local mean PBM provides lower imaging and dosimetric uncertainties than ABM and BDM. Download : Download high-res image (495KB) Download : Download full-size image

Validation and application of a fast Monte Carlo algorithm for assessing the clinical impact of approximations in analytical dose calculations for pencil beam scanning proton therapy.

Monte Carlo (MC) dose calculation is generally superior to analytical dose calculation (ADC) used in commercial TPS to model the dose distribution especially for heterogeneous sites, such as lung and head/neck patients. The purpose of this study was to provide a validated, fast, and open-source MC code, MCsquare, to assess the impact of approximations in ADC on clinical pencil beam scanning (PBS) plans covering various sites. First, MCsquare was validated using tissue-mimicking IROC lung phantom measurements as well as benchmarked with the general purpose Monte Carlo TOPAS for patient dose calculation. Then a comparative analysis between MCsquare and ADC was performed for a total of 50 patients with 10 patients per site (including liver, pelvis, brain, head-and-neck, and lung). Differences among TOPAS, MCsquare, and ADC were evaluated using four dosimetric indices based on the dose-volume histogram (target Dmean, D95, homogeneity index, V95), a 3D gamma index analysis (using 3%/3mm criteria), and estimations of tumor control probability (TCP). Comparison between MCsquare and TOPAS showed less than 1.8% difference for all of the dosimetric indices/TCP values and resulted in a 3D gamma index passing rate for voxels within the target in excess of 99%. When comparing ADC and MCsquare, the variances of all the indices were found to increase as the degree of tissue heterogeneity increased. In the case of lung, the D95s for ADC were found to differ by as much as 6.5% from the corresponding MCsquare statistic. The median gamma index passing rate for voxels within the target volume decreased from 99.3% for liver to 75.8% for lung. Resulting TCP differences can be large for lung (≤10.5%) and head-and-neck (≤6.2%), while smaller for brain, pelvis and liver (≤1.5%). Given the differences found in the analysis, accurate dose calculation algorithms such as Monte Carlo simulations are needed for proton therapy, especially for disease sites with high heterogeneity, such as head-and-neck and lung. The establishment of MCsquare can facilitate patient plan reviews at any institution and can potentially provide unbiased comparison in clinical trials given its accuracy, speed and open-source availability.

Deep Learning-Based Pseudo CT Reconstruction for MR Only-Guided Radiation Therapy of Lung SBRT

The improved soft tissue contrast offered by magnetic resonance imaging (MRI) systems compared to traditional computed tomography (CT) systems makes MR only-guided radiation therapy (RT) an attractive goal for future RT applications. However, a major limitation in moving towards this goal is the necessity of the electron density information derived from CT in RT treatment planning for dose calculations. To overcome this problem, we propose a deep learning-based pseudo CT reconstruction method using a generative adversarial network (GAN) and investigate its use in MR-guided RT treatment planning for lung SBRT. In this study, we have used a GAN to reconstruct pseudo CT images for treatment planning in MR-guided RT of lung SBRT. A GAN consists of two competing networks: 1) a generator that produces pseudo CT outputs based on MR image inputs and 2) a discriminator that differentiates between real and generated images. The networks are trained iteratively to improve the capabilities of the discriminator and the quality of the generator outputs. The generator uses a U-net architecture of 16 convolutional layers with skip connections that transfer high resolution features. The discriminator consists of 5 convolutional layers. Registered MR and CT images from 37 patients previously treated at our institution were used to train the network. The images were masked to eliminate background elements outside of the body contour. Also, the ranges of pixel values in both image sets were restricted to create a more homogeneous training set. After training, the model was applied to unseen MR images from 4 patients for testing. Electron density information derived from the reconstructed pseudo CTs was used to compute the dose based on the same plan parameters used in the clinical plan. Dose-volume histograms (DVHs) were used as the basis of comparison between the proposed and clinical plans. A total of 2000 training epochs took approximately 6 days to complete. When applied to new image sets, the trained model had a throughput time of approximately 0.1 s/slice. DVHs based on the pseudo CTs were comparable to those of the clinically delivered plans. Calculated dose metrics for the proposed and clinical plans are summarized in Table 1 along with 3D Gamma Index pass rates. The comparable calculated dose demonstrates the efficacy of pseudo CT images in treatment planning. Adopting the proposed method offers the potential to eliminate extra radiation dose from unnecessary CT scans in a move towards MR-only RT.Abstract MO_28_2725; Table 1PTVLungEsophagusHeartSpinal Cord3D Gamma IndexPt.D95 (%)V20 (%)Dmax (Gy)Pass Rate (%)1Clinical99.9116.3169.6335.6921.5599.87Proposed99.9716.4169.7536.4221.842c.99.236.5032.8761.4826.9798.14p.99.076.5332.5160.7027.173c.68.5016.1743.3165.2116.9399.86p.67.5116.1442.7765.0416.974c.99.999.1063.1763.3725.1098.33p.100.008.8062.6862.6825.94 Open table in a new tab

OA234] Establishing acceptance criteria for VMAT Quality assurance (QA) in a new radiotherapy centre

Purpose In this poster we present the statistical analysis applied to establish the criteria of acceptance for Volumetric Arctherapy (VMAT) plans during the QA phase for a recently opened radiotherapy centre. Methods Using a sample of 200 plans, we have analysed the 2D and 3D gamma indices in the three axes (coronal, sagittal and transverse), varying from 1 mm, 1% to 5 mm, 5%, both local and global, and compared these indices with a 4D QA method. This analysis has been done for the whole sample of plans and separately for each pathology. Results Our results show no statistical significant difference between using 2D or 3D gamma indices nor for the different axes. However, the transverse axis shows a much larger dispersion in the gamma indices and is thus considered not optimal in our case. We find that the gamma indices for the global maximum of each plan are significant (2.5 sigma) larger than when using local maxima and this difference increases as we lower the threshold. We have compared the gamma indices against the 4D method threefold: i) correlation (quantified by the Pearson coefficient); ii) absolute difference; and iii) number of catastrophic events (defined as those that would be accepted in terms of the gamma index but would be rejected in terms of the 4D method). Conclusions We conclude that from our sample of VMAT plans, the optimal criterion is 3 mm, 3% local. These results are consistent over the whole sample and for each pathology. Furthermore, this analysis has allowed us to establish standard benchmarks for acceptance testing, with an overall standard of 95% for a gamma 3 mm, 3% local which can be relaxed or strengthened depending on the pathology (ranging from 92% for bone cancer to 97% for prostate cancer).

Composite QA for intensity-modulated radiation therapy using individual volume-based 3D gamma indices.

The aim of this study was to investigate the feasibility and sensitivity of using individual volume–based 3D gamma indices for composite dose–volume histogram (DVH)–based intensity-modulated radiation therapy (IMRT) quality assurance (QA). Composite IMRT QA for 15 cervical cancer patients was performed with ArcCHECK. The percentage dosimetric errors (%DEs) of DVH metrics when comparing treatment planning system and QA-reconstructed dose distribution, percentage gamma passing rates (%GPs) with different criteria for individual volumes and global gamma indices were evaluated, as well as their correlations. Receiver operating characteristic (ROC) curves were applied in order to study the sensitivities of the global and individual volume gamma indices. Most %DEs of the DVH metrics were within 3%. The γPTV and γrectum were <80% at 2%/2 mm; apart from these two individual volume indices, all other individual volume gamma indices and global indices had acceptable %GPs. For the criteria of 2%/2 mm, 3%/3 mm and 4%/4 mm, individual volume-based %GPs and global %GPs were correlated in 11, 1 and 12 out of 24 %DE metrics, and in 5, 4 and 5 out of 24 %DE metrics, respectively. Individual volume–based %GPs had a higher percentage of correlation with DVH metrics (%DEs) compared with global %GPs in composite IMRT QA. The areas under the curve (AUCs) of individual volume %GPs were higher than those of global %GPs. In conclusion, individual volume–based %GPs had a higher correlation with %DEs of metrics and a higher sensitivity presented by ROC analysis compared with global %GPs for composite IMRT QA. Thus, use of individual volume-based 3D gamma indices was found to be feasible and sensitive for composite IMRT QA.

Multidimensional correlation among plan complexity, quality and deliverability parameters for volumetric-modulated arc therapy using canonical correlation analysis.

A multidimensional exploratory statistical method, canonical correlation analysis (CCA), was applied to evaluate the impact of complexity parameters on the plan quality and deliverability of volumetric-modulated arc therapy (VMAT) and to determine parameters in the generation of an ideal VMAT plan. Canonical correlations among complexity, quality and deliverability parameters of VMAT, as well as the contribution weights of different parameters were investigated with 71 two-arc VMAT nasopharyngeal cancer (NPC) patients, and further verified with 28 one-arc VMAT prostate cancer patients. The average MU and MU per control point (MU/CP) for two-arc VMAT plans were 702.6 ± 55.7 and 3.9 ± 0.3 versus 504.6 ± 99.2 and 5.6 ± 1.1 for one-arc VMAT plans, respectively. The individual volume-based 3D gamma passing rates of clinical target volume (γCTV) and planning target volume (γPTV) for NPC and prostate cancer patients were 85.7% ± 9.0% vs 92.6% ± 7.8%, and 88.0% ± 7.6% vs 91.2% ± 7.7%, respectively. Plan complexity parameters of NPC patients were correlated with plan quality (P = 0.047) and individual volume-based 3D gamma indices γ(IV) (P = 0.01), in which, MU/CP and segment area (SA) per control point (SA/CP) were weighted highly in correlation with γ(IV) , and SA/CP, percentage of CPs with SA < 5 × 5 cm2 (%SA < 5 × 5 cm2) and PTV volume were weighted highly in correlation with plan quality with coefficients of 0.98, 0.68 and −0.99, respectively. Further verification with one-arc VMAT plans demonstrated similar results. In conclusion, MU, SA-related parameters and PTV volume were found to have strong effects on the plan quality and deliverability.

18 F-Fluoromisonidazole positron emission tomography/CT-guided volumetric-modulated arc therapy-based dose escalation for hypoxic subvolume in nasopharyngeal carcinomas: A feasibility study.

The purpose of this study is to investigate the feasibility of a simultaneously integrated boost to the hypoxic subvolume of nasopharyngeal carcinomas (NCPs) under the guidance of 18 F-fluoromisonidazole (FMISO) positron emission tomography (PET)/CT using volumetric-modulated arc therapy (VMAT) and intensity-modulated radiotherapy (IMRT) techniques. Eight patients with NPC were treated with simultaneous integrated boost-IMRT (treatment plan named IMRT70) with dose prescriptions of 70 Gy, 66 Gy, 60 Gy, and 54 Gy to the gross tumor volume (GTV), positive neck nodes, the planning target volume (PTV), and the clinically negative neck, respectively. Based on the same datasets, experimental plans with the same dose prescription plus a dose boost of 14 Gy (an escalation of 20% of the prescription dose) to the hypoxic volume target contoured on the pretreatment 18 F-FMISO PET/CT imaging were generated using IMRT and VMAT techniques, respectively (represented by IMRT84 and VMAT84). Two or more arcs (approximately 2-2.5 arcs, totally rotating angle <1000 degrees) were used in VMAT plans and 9 equally separated fields in IMRT plans. Dosimetric parameters, total monitor units, and delivery time were calculated for comparative study of plan quality and delivery efficiency between IMRT84 and VMAT84. In experimental plans, hypoxic target volumes successfully received the prescribed dose of 84 Gy in compliance with other dose constraints with either the IMRT technique or the VMAT technique. In terms of the target coverage, dose homogeneity, and organs at risk (OAR) sparing, there was no statistically significant difference between the actual treatment plan of IMRT70 and experimental plans. The total monitor unit of VMAT84 (525.7 ± 39.8) was significantly less than IMRT70 (1171.5 ± 167; P = .001) and IMRT84 (1388.3 ± 151.0; P = .001) per fraction, with 55.1% and 62.1% reduction. The average machine delivery time was 3.5 minutes for VMAT plans in comparison with approximately 8 minutes for IMRT plans, resulting in a reduction factor of 56.2%. For experimental plans, the 3D gamma index average was over 98.0% with no statistical significant difference when a 3%/3 mm gamma passing rate criteria was used. With the guidance of 18 F-FMISO PET/CT imaging, dose escalation to hypoxic zones within NPC could be achieved and delivered efficiently with the VMAT technique in comparison with the IMRT technique.

Independent dose verification system with Monte Carlo simulations using TOPAS for passive scattering proton therapy at the National Cancer Center in Korea

For the independent validation of treatment plans, we developed a fully automated Monte Carlo (MC)-based patient dose calculation system with the tool for particle simulation (TOPAS) and proton therapy machine installed at the National Cancer Center in Korea to enable routine and automatic dose recalculation for each patient. The proton beam nozzle was modeled with TOPAS to simulate the therapeutic beam, and MC commissioning was performed by comparing percent depth dose with the measurement. The beam set-up based on the prescribed beam range and modulation width was automated by modifying the vendor-specific method. The CT phantom was modeled based on the DICOM CT files with TOPAS-built-in function, and an in-house-developed C++ code directly imports the CT files for positioning the CT phantom, RT-plan file for simulating the treatment plan, and RT-structure file for applying the Hounsfield unit (HU) assignment, respectively. The developed system was validated by comparing the dose distributions with those calculated by the treatment planning system (TPS) for a lung phantom and two patient cases of abdomen and internal mammary node. The results of the beam commissioning were in good agreement of up to 0.8 mm2 for B8 option in both of the beam range and the modulation width of the spread-out Bragg peaks. The beam set-up technique can predict the range and modulation width with an accuracy of 0.06% and 0.51%, respectively, with respect to the prescribed range and modulation in arbitrary points of B5 option (128.3, 132.0, and 141.2 mm2 of range). The dose distributions showed higher than 99% passing rate for the 3D gamma index (3 mm distance to agreement and 3% dose difference) between the MC simulations and the clinical TPS in the target volume. However, in the normal tissues, less favorable agreements were obtained for the radiation treatment planning with the lung phantom and internal mammary node cases. The discrepancies might come from the limitations of the clinical TPS, which is the inaccurate dose calculation algorithm for the scattering effect, in the range compensator and inhomogeneous material. Moreover, the steep slope of the compensator, conversion of the HU values to the human phantom, and the dose calculation algorithm for the HU assignment also could be reasons of the discrepancies. The current study could be used for the independent dose validation of treatment plans including high inhomogeneities, the steep compensator, and riskiness such as lung, head & neck cases. According to the treatment policy, the dose discrepancies predicted with MC could be used for the acceptance decision of the original treatment plan.

Dose warping performance in deformable image registration in lung

PurposeIt is unclear that spatial accuracy can reflect the impact of deformed dose distribution. In this study, we used dosimetric parameters to compare an in-house deformable image registration (DIR) system using NiftyReg, with two commercially available systems, MIM Maestro (MIM) and Velocity AI (Velocity). MethodsFor 19 non-small-cell lung cancer patients, the peak inspiration (0%)-4DCT images were deformed to the peak expiration (50%)-4DCT images using each of the three DIR systems, which included computation of the deformation vector fields (DVF). The 0%-gross tumor volume (GTV) and the 0%-dose distribution were also then deformed using the DVFs. The agreement in the dose distributions for the GTVs was evaluated using generalized equivalent uniform dose (gEUD), mean dose (Dmean), and three-dimensional (3D) gamma index (criteria: 3mm/3%). Additionally, a Dice similarity coefficient (DSC) was used to measure the similarity of the GTV volumes. ResultsDmean and gEUD demonstrated good agreement between the original and deformed dose distributions (differences were generally less than 3%) in 17 of the patients. In two other patients, the Velocity system resulted in differences in gEUD of 50.1% and 29.7% and in Dmean of 11.8% and 4.78%. The gamma index comparison showed statistically significant differences for the in-house DIR vs. MIM, and MIM vs. Velocity. ConclusionsThe finely tuned in-house DIR system could achieve similar spatial and dose accuracy to the commercial systems. Care must be taken, as we found errors of more than 5% for Dmean and 30% for gEUD, even with a commercially available DIR tool.

Individual volume-based 3D gamma indices for pretreatment VMAT QA.

Although gamma analysis is still a widely accepted quantitative tool to analyze and report patient‐specific QA for intensity‐modulated radiotherapy (IMRT) and volumetric‐modulated arc therapy (VMAT), the correlation between the 2D percentage gamma passing rate (%GP), and the clinical dosimetric difference for IMRT and VMAT has been questioned. The purpose of this study was to investigate the feasibility of individual volume‐based 3D gamma indices for pretreatment VMAT QA. Percentage dosimetric errors (%DE) of dose‐volume histogram metrics (includes target volumes and organ at risks) between the treatment planning system and QA‐reconstructed dose distribution, %GPs for individual volume and global gamma indices, as well their correlations and sensitivities were investigated for one‐ and two‐arc VMAT plans. The %GPs of individual volumes had a higher percent of correlation with individual 15 %DE metrics compared with global %GPs. For two‐arc VMAT at 2%/2 mm, 3%/3 mm, and 4%/4 mm criteria, individual volume %GPs were correlated with 9, 12, and 9 out of 15 %DE metrics, while global %GPs were correlated with only 2 out of 15 %DE metrics, respectively. For one‐arc VMAT at 2%/2 mm, 3%/3 mm, and 4%/4 mm criteria, individual volume %GPs were correlated with 18, 16, and 13 out of 23 %DE metrics, and global %GPs were correlated with 19, 12, and 1 out 23 %DE metrics, respectively. The area under curves (AUC) of individual volume %GPs were higher than those of global %GPs for two‐arc VMAT plans, but with mixed results for one‐arc VMAT plans. In a conclusion, the idea of individual volume %GP was created and investigated to better serve for VMAT QA and individual volume‐based %GP had a higher percent of correlation with DVH 15 %DE metrics compared with global %GP for both one‐ and two‐arc VMAT plans.

Moving GPU-OpenCL-based Monte Carlo dose calculation toward clinical use: Automatic beam commissioning and source sampling for treatment plan dose calculation.

We have previously developed a GPU‐based Monte Carlo (MC) dose engine on the OpenCL platform, named goMC, with a built‐in analytical linear accelerator (linac) beam model. In this paper, we report our recent improvement on goMC to move it toward clinical use. First, we have adapted a previously developed automatic beam commissioning approach to our beam model. The commissioning was conducted through an optimization process, minimizing the discrepancies between calculated dose and measurement. We successfully commissioned six beam models built for Varian TrueBeam linac photon beams, including four beams of different energies (6 MV, 10 MV, 15 MV, and 18 MV) and two flattening‐filter‐free (FFF) beams of 6 MV and 10 MV. Second, to facilitate the use of goMC for treatment plan dose calculations, we have developed an efficient source particle sampling strategy. It uses the pre‐generated fluence maps (FMs) to bias the sampling of the control point for source particles already sampled from our beam model. It could effectively reduce the number of source particles required to reach a statistical uncertainty level in the calculated dose, as compared to the conventional FM weighting method. For a head‐and‐neck patient treated with volumetric modulated arc therapy (VMAT), a reduction factor of ~2.8 was achieved, accelerating dose calculation from 150.9 s to 51.5 s. The overall accuracy of goMC was investigated on a VMAT prostate patient case treated with 10 MV FFF beam. 3D gamma index test was conducted to evaluate the discrepancy between our calculated dose and the dose calculated in Varian Eclipse treatment planning system. The passing rate was 99.82% for 2%/2 mm criterion and 95.71% for 1%/1 mm criterion. Our studies have demonstrated the effectiveness and feasibility of our auto‐commissioning approach and new source sampling strategy for fast and accurate MC dose calculations for treatment plans.

P20. New software solution for TPS quality control

Introduction In radiation therapy, one of the most time consuming step is due to the Treatment Planning System (TPS) and quality control algorithms for the dose computations. Using Artificial Neural Network (ANN), it is possible to compute the doses inside a phantom resulting from ionizing radiations [1] , [2] . Material and methods More precisely, once the ANN is trained, it can compute a dose for every voxels of the phantom according to the beam characteristics. The main advantage is the computation time: the time-consuming step is the training of the ANN, that has to be performed only once and offline. The execution time, i.e. the dose associated to a voxel, is very low. We present an evolution of the platform presented in [3] , to take into account various field sizes and shapes, and also consider different orientation of the beam to the phantom. To validate the ANN computations, we compare them with Monte Carlo simulations. Homogeneous phantoms are used to validate interpolations on various densities and depth. Then, realistic phantoms are created to validate the dose distribution computed for a square beam in complex areas (head and neck, lungs, prostatic area). Finally, IMRT treatment simulations are validated. Results We compare the results obtained using our approach, the Eclipse TPS (AAA), and Monte Carlo simulations for a 10 cm x 10 cm photon beam on phantoms extracted from clinical data. The same procedure is applied to validate IMRT treatments. To compare the dose distributions, 3D gamma indices are computed, as well as profile extractions. For IMRT treatments, the dose distribution is computed in less than one minute on a traditional computer. Conclusions We have demonstrated the performance of our dose distribution engine for IMRT. Our goal is to provide a fast and accurate solution for TPS quality control. Therefore, we are currently working on the development of arctherapy treatment simulations.