Leaf Gap Research Articles

Technical Note: Investigating of dosimetric leaf gap and leaf transmission factor variations across gantry and collimator angles in volumetric modulation arc therapy.

This study investigates the influence of gantry and collimator angles on the dosimetric leaf gap (DLG) and leaf transmission factor (LTF) in a Varian LINAC equipped with rounded-end multi-leaf collimators (MLCs). While Varian guidelines recommend DLG measurements at zero degrees for both gantry and collimator, this research aims to address the knowledge gap by assessing DLG and LTF variations at different gantry and collimator angles. Measurements were conducted using a Varian TrueBeam LINAC with a Millennium 120-leaf MLC and Eclipse TPS version 16.1. The beams utilized in this study had energies of 6 MV, 10 MV, 6 FFF, and 10 FFF. LTF and DLG were determined using ionization chambers in solid water phantoms at various gantry angles (0°, 45°, 90°, 135°, 180°, 225°, 270°, and 315°). For each gantry angle, measurements were also taken at various collimator angles (0°, 45°, 90°, and 315°). Dosimetric impacts were evaluated through VMAT Picket Fence tests and patient-specific verification using portal dosimetry for 10 clinical VMAT plans. LTF values showed no significant variation across gantry and collimator angles. However, DLG values exhibited notable differences depending on the gantry angle and were independent of the collimator angle. The highest DLG value was observed at a gantry angle of 270 degrees, while the lowest was at 90 degrees. The AXB DLGAverage (averaging seven measurements of DLGs at different gantry angles) model demonstrated the best agreement between measured and calculated dose distributions, indicating the importance of considering averaged DLG values across multiple gantry angles for accurate dose calculations. Our study highlights the variability of DLG with gantry angle alterations, contrary to Varian guidelines recommending DLG measurements at zero gantry angle only. We advocate for utilizing an averaged DLG value from measurements across multiple gantry angles, as outlined in our methodology.

Improving the performance of deep learning models in predicting and classifying gamma passing rates with discriminative features and a class balancing technique: a retrospective cohort study

BackgroundThe purpose of this study was to improve the deep learning (DL) model performance in predicting and classifying IMRT gamma passing rate (GPR) by using input features related to machine parameters and a class balancing technique.MethodsA total of 2348 fields from 204 IMRT plans for patients with nasopharyngeal carcinoma were retrospectively collected to form a dataset. Input feature maps, including fluence, leaf gap, leaf speed of both banks, and corresponding errors, were constructed from the dynamic log files. The SHAP framework was employed to compute the impact of each feature on the model output for recursive feature elimination. A series of UNet++ based models were trained on the obtained eight feature sets with three fine-tuning methods including the standard mean squared error (MSE) loss, a re-sampling technique, and a proposed weighted MSE loss (WMSE). Differences in mean absolute error, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were compared between the different models.ResultsThe models trained with feature sets including leaf speed and leaf gap features predicted GPR for failed fields more accurately than the other models (F(7, 147) = 5.378, p < 0.001). The WMSE loss had the highest accuracy in predicting GPR for failed fields among the three fine-tuning methods (F(2, 42) = 14.149, p < 0.001), while an opposite trend was observed in predicting GPR for passed fields (F(2, 730) = 9.907, p < 0.001). The WMSE_FS5 model achieved a superior AUC (0.92) and more balanced sensitivity (0.77) and specificity (0.89) compared to the other models.ConclusionsMachine parameters can provide discriminative input features for GPR prediction in DL. The novel weighted loss function demonstrates the ability to balance the prediction and classification accuracy between the passed and failed fields. The proposed approach is able to improve the DL model performance in predicting and classifying GPR, and can potentially be integrated into the plan optimization process to generate higher deliverability plans.Trial registration: This clinical trial was registered in the Chinese Clinical Trial Registry on March 26th, 2020 (registration number: ChiCTR2000031276). https://clinicaltrials.gov/ct2/show/ChiCTR2000031276

Mengekstrak Parameter Fungsi Skala Kanopi di Kawasan Konservasi Lembah Danum, Sabah Menggunakan Data Pesawat Udara LiDAR

Forest restoration is an effort to restore the growth and balance degraded forest ecosystems, through various approaches such as reforestation, land restoration, endangered species conservation, and forest fire prevention. Conventional field data collection challenges, such as limited spatial coverage, tall tree measurement difficulties, and human observation errors, hinder canopy-scale functional traits monitoring and extraction across forest treatments. The study addresses the importance of forest restoration and the challenges associated with conventional field measurements in extracting canopy scale functional traits by utilizing Airborne light detection and ranging (LiDAR) technology for more accurate information involving the old growth, natural regeneration, and active restored forests in Danum Valley Conservation Area and INFAPRO Sabah. The study utilized diverse processing algorithms, allometric equations, and correlative modeling methods. At the grid level, various LiDAR functional traits were calculated, leaf area index, gap fraction, and canopy density, alongside assessment of multiple correlative modeling strategies. Linear regression and analysis of variance analyzed relationships between LiDAR-derived and field-derived canopy scale functional traits between different forest treatments. The results showed high R-squared values ranging from 0.73 to 0.91, Anova F-statistics and probability values (p) showed that there is a strong relationship between the field data and predicted LiDAR values for three-canopy scale functional traits in different forest treatment types. These results indicate that LiDAR technology is effective in predicting canopy scale functional traits and has the potential to provide accurate and detailed information on forest restoration areas for future conservation and management efforts.

A novel hybrid-view technique for accurate mass estimation of kimchi cabbage using computer vision

This study addresses the accurate estimation of kimchi cabbage mass, as cabbage leaves exhibit size variability and complex leaf structures. Conventional mass estimation methods, which rely solely on external imaging, often overlook leaf gaps. To improve accuracy, we propose an innovative computer vision system utilizing hybrid-view images and detailed saturation analysis. Our system quantifies the impact of leaf gaps on mass using features from the saturation channel of images of bisected cabbage. Our proposed method can be easily integrated into existing workflows and has the potential to improve labor efficiency. Our approach outperforms the conventional method (R2 of 0.66 and relative error of 8.68%), achieving a 0.92 R2 value and lowering the relative error to 4.22%. This advancement offers a robust solution for the mass estimation of kimchi cabbage and suggests potential applications for other foods and crops with internal voids.

1646: Assessment of different methods of measuring the dosimetric leaf gap (DLG) for Varian TrueBeam HD.

1646: Assessment of different methods of measuring the dosimetric leaf gap (DLG) for Varian TrueBeam HD.

Investigating the Dosimetric Leaf Gap Correction Factor of Mobius3D Dose Calculation for Volumetric-modulated Arc Radiotherapy Plans.

The dosimetric leaf gap (DLG) is a parameter for correcting radiation transmission through the round leaf end of multileaf collimators. The purpose of this study was to determine and investigate the optimal DLG correction factor for 6 MV volumetric-modulated arc radiotherapy (VMAT) plan dose calculations in Mobius3D. Seventeen VMAT plans were selected for the DLG correction factor optimization process. The optimal DLG correction factor was defined as the minimum difference between the measured dose and the Mobius3D-calculated dose on the Mobius Verification Phantom™ with different DLG correction factors. Subsequently, the optimal DLG correction factor was applied for Mobius3D dose calculation, and accuracy was assessed by comparing the measured and calculated doses. For verification and validation, the 17 previous plans and 10 newly selected plans underwent Mobius3D calculations with the optimal DLG correction factor, and gamma analysis was performed to compare them to the treatment planning system (TPS). Gamma analysis was also performed between the electronic portal imaging device (EPID) and the TPS for cross-comparison between systems. The DLG correction factor was optimized to -1.252, which reduced the average percentage differences between measured and Mobius3D-calculated doses from 2.23% ±1.21% to 0.03% ±1.82%. The cross-comparison between Mobius3D/TPS and EPID/TPS revealed a similar trend in gamma passing rate (>95%) in both the verification and validation plans. The DLG correction factor strongly influences the accuracy of Mobius3D-calculated doses. Applying the optimal DLG correction factor can increase dose agreement and gamma passing rate between calculation and delivered doses of VMAT plans, which emphasizes the importance of optimizing this factor during the commissioning process.

Optimization of a commercial portal dose image prediction algorithm for pre-treatment verifications of plans using unflattened photon beams.

The aim was to improve the portal dosimetry-based quality assurance results of conventional treatment plans by adjusting the multileaf collimator (MLC) dosimetric leaf gap (DLG) and transmission (T) values of the anisotropic analytic algorithm (AAA) used for portal dose image prediction (PDIP). The AAA-based PDIP v. 16.1 algorithm (PDIP-AAA) of the Eclipse TPS was configured for 6 MV FFF energy. Optimal DLG and T values were achieved for this algorithm by comparing predicted versus measured portal images of the Chair pattern. Twenty clinical plans using 6 MV FFF beams were verified using the optimal PDIP-AAA algorithm and the standard PDIP v. 16 algorithm (PDIP-vE), configured using the van Esch package. The 3% global/2 mm gamma passing rates (GPRs) and average gamma indexes (AGIs) were computed for each acquired image. For each plan, the mean GPR (GPRmean) and mean GAI (GAImean) were compared for both algorithms. A 2-tailed Student t-test (α = 0.05) was used to evaluate whether there was a statistically significant difference. Optimal values of DLG = 0.1 mm and T = 0.01 were found for the PDIP-AAA algorithm, providing significantly better values of GPRmean and AGImean than PDIP-vE (p < 0.001). All plans verified with PIDP-AAA showed GPRmean ≥ 95%. In contrast, only 45% of the plans reported GPRmean ≥ 95% with the PDIP-vE algorithm. The MLC parameters available in the PDIP-AAA model must be tuned to improve the accuracy of the predicted dose image. This work-around is not possible using the standard PDIP algorithm. The adjusted PDIP-AAA resulted in significantly better results than PDIP-vE.

Multi-institutional investigation into the robustness of intra-cranial multi-target stereotactic radiosurgery plans to delivery errors.

The use of modulated techniques for intra-cranial stereotactic radiosurgery (SRS) results in highly modulated fields with small apertures, which may be susceptible to uncertainties in the delivery device. This study aimed to quantify the impact of simulated delivery errors on treatment plan dosimetry and how this is affected by treatment planning system (TPS), plan geometry, delivery technique, and plan complexity. A beam modelling error was also included as context to the dose uncertainties due to treatment delivery errors. Delivery errors were assessed for multiple-target brain SRS plans obtained through the Trans-Tasman Radiation Oncology Group (TROG) international treatment planning challenge (2018). The challenge dataset consisted of five intra-cranial targets, each with a prescription of 20Gy. Of the final dataset of 54 plans, 51 were created using the volumetric modulated arc therapy (VMAT) technique and three used intensity modulated arc therapy (IMRT). Thirty-five plans were from the Varian Eclipse TPS, 17 from Elekta Monaco TPS, and one plan each from RayStation and Philips Pinnacle TPS. The errors introduced included: monitor unit calibration errors, multi-leaf collimator (MLC) bank offset, single MLC leaf offset, couch rotations, and collimator rotations. Dosimetric leaf gap (DLG) error was also included as a beam modelling error. Dose to targets was assessed via dose covering 98% of planning target volume (PTV) (D98%), dose covering 2% of PTV (D2%), and dose covering 99% of gross tumor volume (GTV) (D99%). Dose to organs at risk (OARs) was assessed using the volume of normal brain receiving 12Gy (V12Gy), mean dose to normal brain, and maximum dose covering 0.03cc brainstem (D0.03cc). Plan complexity was also assessed via edge metric, modulation complexity score (MCS), mean MLC gap, mean MLC speed, and plan modulation (PM). PTV D98% showed high robustness on average to most errors with the exception of a bank shift of 1.0mm and large rotational errors ≥1.0° for either the couch or collimator. However, in some cases, errors close to or within generally accepted machine tolerances resulted in clinically relevant impacts. The greatest impact upon normal brain V12Gy, mean dose to normal brain, and D0.03cc brainstem was found for DLG error in alignment with other recent studies. All delivery errors had on average a minimal impact across these parameters. Comparing plans from the Monaco TPS and the Eclipse TPS, showed a lesser increase to V12Gy, mean dose to normal brain, and D0.03cc brainstem for Monaco plans (p<0.01) when DLG error was simulated. Monaco plans also correlated to lower plan complexity. Using Spearman's correlation coefficient (r) a strong negative correlation (r≤-0.8) was found between the mean MLC gap and dose to OARs for DLG errors. Reducing MLC complexity and using larger mean MLC gaps is recommended to improve plan robustness and reduce sensitivity to delivery and modelling errors. For cases in which the calculated dose distribution or dose indices are close to the clinically acceptable limits, this is especially important.

Leaf area index (LAI) and gap fraction. A discussion

Methodological aspects of estimating leaf area from gap fraction measurements are discussed. Instead of the common practice of linking in the Beer-Lambert law leaf area index and clumping factor together, the clumping factor and Ross-Nilson geometry function as two structure parameters should be combined into the effective geometry function, which considers both the leaf angle distribution and clumping/regularity of foliage in the expression of the gap fraction of a vegetation layer. Key words: leaf area index; foliage clumping; gap fraction; LAI-2000; G-function

Generation, validation, and benchmarking of a commercial independent Monte Carlo calculation beam model for multi-target SRS

BackgroundDosimetric validation of single isocenter multi-target radiosurgery plans is difficult due to conditions of electronic disequilibrium and the simultaneous irradiation of multiple off-axis lesions dispersed throughout the volume. Here we report the benchmarking of a customizable Monte Carlo secondary dose calculation algorithm specific for multi-target radiosurgery which future users may use to guide their commissioning and clinical implementation. PurposeTo report the generation, validation, and clinical benchmarking of a volumetric Monte Carlo (MC) dose calculation beam model for single isocenter radiosurgery of intracranial multi-focal disease. MethodsThe beam model was prepared within SciMoCa (ScientificRT, Munich Germany), a commercial independent dose calculation software, with the aim of broad availability via the commercial software for use with single isocenter radiosurgery. The process included (1) definition & acquisition of measurement data required for beam modeling, (2) tuning model parameters to match measurements, (3) validation of the beam model via independent measurements and end-to-end testing, and finally, (4) clinical benchmarking and validation of beam model utility in a patient specific QA setting. We utilized a 6X Flattening-Filter-Free photon beam from a TrueBeam STX linear accelerator (Siemens Healthineers, Munich Germany). ResultsIn addition to the measured data required for standard IMRT/VMAT (depth dose, central axis profiles & output factors, leaf gap), beam modeling and validation for single-isocenter SRS required central axis and off axis (5 cm & 9 cm) small field output factors and comparison between measurement and simulation of backscatter with aperture for jaw much greater than MLCs. Validation end-to-end measurements included SRS MapCHECK in StereoPHAN geometry (2%/1 mm Gamma = 99.2% ± 2.2%), and OSL & scintillator measurements in anthropomorphic STEEV phantom (6 targets, volume = 0.1–4.1cc, distance from isocenter = 1.2–7.9 cm) for which mean difference was −1.9% ± 2.2%. For 10 patient cases, MC for individual PTVs was −0.8% ± 1.5%, −1.3% ± 1.7%, and −0.5% ± 1.8% for mean dose, D95%, and D1%, respectively. This corresponded to custom passing rates action limits per AAPM TG-218 guidelines of ±5.2%, ±6.4%, and ±6.3%, respectively. ConclusionsThe beam modeling, validation, and clinical action criteria outlined here serves as a benchmark for future users of the customized beam model within SciMoCa for single isocenter radiosurgery of multi-focal disease.

Deep learning-based detection and classification of multi-leaf collimator modeling errors in volumetric modulated radiation therapy.

The purpose of this study was to create and evaluate deep learning-based models to detect and classify errors of multi-leaf collimator (MLC) modeling parameters in volumetric modulated radiation therapy (VMAT), namely the transmission factor (TF) and the dosimetric leaf gap (DLG). A total of 33 clinical VMAT plans for prostate and head-and-neck cancer were used, assuming a cylindrical and homogeneous phantom, and error plans were created by altering the original value of the TF and the DLG by±10, 20, and 30% in the treatment planning system (TPS). The Gaussian filters of and 1.0 were applied to the planar dose maps of the error-free plan to mimic the measurement dose map, and thus dose difference maps between the error-free and error plans were obtained. We evaluated 3 deep learning-based models, created to perform the following detections/classifications: (1) error-free versus TF error, (2) error-free versus DLG error, and (3) TF versus DLG error. Models to classify the sign of the errors were also created and evaluated. A gamma analysis was performed for comparison. The detection and classification of TF and DLG error were feasible for ; however, a considerable reduction of accuracy was observed for depending on the magnitude of error and treatment site. The sign of errors was detectable by the specifically trained models for and 1.0. The gamma analysis could not detect errors. We demonstrated that the deep learning-based models could feasibly detect and classify TF and DLG errors in VMAT dose distributions, depending on the magnitude of the error, treatment site, and the degree of mimicked measurement doses.

SHADING OF TREE CANOPIES ON INTERTOWN ROADS THROUGHOUT FOREST LANDSCAPES: ABDİPAŞA-OVACUMA GREENWAY OF TURKEY

Greenways are the systems that link both the urban and rural landscapes. Thus, they establish cultural and natural corridors between these landscapes. Being one of the major rural landscapes, forests supply ecosystem services by their recreation potential as well as wood and oxygen production. Hence, forest ecosystems that include roads constitute rural greenways. The canopies of the trees and shrubs supply shading on these road surfaces. These greenways play a significant recreational and safety role within the overall forest landscapes. They provide shading, recreation, and safety for both the drivers and passengers. Thereby, tree and shrub canopy parameters such as the closure and coverage are the determining factors on the safety, shading and recreation potential. Amongst those tree and shrub canopy parameters, the Leaf Area Index (LAI), Light Transmission (LT), Gap Fraction (BO) and Canopy Openness (CO) are the prominent ones. Therefore, the greenway route along the road between Ulus Subprovince (Bartın) and Safranbolu Subprovince (Karabük) at the northwestern Turkey, was chosen as the study and research field in this study. The oriental planes (Platanus orientalis L.) are the immediate roadside trees with their canopies shading on the surface that road. Along 14 km road route, 71 different points were defined for the hemispherical photographing during the summer of 2021. Based on the analyses of these hemispherical photographs using the version 3.2 of the Hemisfer program, the LAI values ranged between 1.89 m2 m-2 and 2.79 m2 m-2 being 2.34 m2 m-2 on average. However, the average LT and CO percentages were 17.9% and 18.2%, whereas the average GF percentage was 7.7%. According to the overall results of this study, the canopy parameters indicated relatively high canopy closure and coverage values even though with alternating values and percentages for the different sections of the greenway route.

Delivery accuracy of VMAT on two beam-matched linacs provided by accelerated go live service.

Dosimetric accuracy is critical when a patient treated with volumetric modulated arc therapy (VMAT) is transferred to another beam-matched linac. To evaluate the performance of Accelerated Go Live (AGL) service, the measured beam characteristics and patient specific quality assurance (QA) results between two AGL-matched linacs were compared. Two VersaHD linacs were installed using the AGL service. After the installation, the beam data such as percentage depth dose (PDD), lateral profiles and output factors for all photon beams were measured. Relative doses were also measured as a function of the multi-leaf collimator (MLC) leaf gap width. Subsequently, VMAT plans were created for prostate, pelvis, head and neck, liver, lung cancers and multiple brain metastases. Dose distributions and point doses were measured by multi-dimensional detectors and ionization chambers for patient specific quality assurance, and comparisons were made between the two linacs. Dose differences in PDDs were all within±1% except the entrance region, and the averaged gamma indices of the lateral profiles were within 0.3. The differences in doses as a function of the MLC leaf gap width between the two linacs were within ±0.5%. For all the plans, gamma passing rates were all higher than 95% with criteria of 2%/2mm. The average and the SD of dose differences on the multi-dimensional detector between both measurements was 0.06±2.12%, and the average of point dose differences was -0.03±0.33%. We have evaluated the AGL performance in the context of beam characteristics and patient specific QA. It was demonstrated that the AGL service provides an accurate VMAT treatment reproducibility for many tumor sites with gamma pass rates greater than 95% under criteria of 2%/2mm.

TUNING AND VALIDATION OF THE NEW RADCALC 3D MONTE CARLO BASED PRE-TREATMENT PLAN VERIFICATION TOOL

Verification procedures for patient-specific quality assurance (QA) in advanced radiotherapy are laborious and time-consuming. Moreover, it has been shown that these procedures cannot detect some inaccuracies for some particular complex cases due to tissue inhomogeneity and highly modulated plans. Secondary dose calculation verification of radiotherapy plans is an important aspect in patient-specific QA. A suitably optimized software, RadCalc equipped with 3D Monte Carlo Module (MC), was used to dosimetrically verify radiotherapy treatment plans where the measured dose distributions can be inaccurate due to the TPS dose calculation algorithm and/or treatment unit delivery uncertainties. MC Models were built using specific commissioning measurements and then the Additional Radiation to Light Field Offset parameter (Dosimetric Leaf Gap parameter) was tuned to achieve the best dose comparison agreement with phantom patient-specific QA measurements. The results showed a good agreement between the TPS and the simulations. RadCalc MC also allows to better estimate the plan doses in lung cancer patients and to detect the presence of possible inaccuracies due to tissue inhomogeneity, which is not estimable with a homogeneous phantoms.

CWAN: Self-supervised learning for deep grape disease image composition

Grape leaf diseases seriously affect grape yield and directly lead to the loss of the industrial economy. An accurate diagnosis is crucial for effective spraying and control. Presently, the combination of image blending and a deep recognition model is the most effective method for accurately identifying grape leaf diseases. Existing methods of image blending depend primarily on single-channel and multi-scale channel blending, computing and optimizing the Poisson equation, and the iterative optimization of learning-based methods for simple composite results. Although these methods have had some success, they are not able to simultaneously address lesion and leaf gap size, color bleeding, and edge smoothness. In this paper, we propose a novel image blending method named cascade wavelet attention network (CWAN). Specifically, CWAN consists of a cascade wavelet attention (CWA) and a blending autoencoder. First, we build a CWA to decompose the image into a multi-level and multi-scale frequency domain image. Then, the detailed features of the source and target domains are extracted, and the edges are smoothed. Following this, a shared decoder is designed to fuse these features in the corresponding frequency-domain layers, and the result serves as the attention feature. Finally, we design a blending autoencoder with a feature fusion mechanism to seamlessly blend lesions and leaves enhanced by CWA. We also construct a weighted blended loss function to avoid leaf color infiltration into the lesion in the blended image. Comprehensive experiments on two grape leaf disease datasets. Qualitative and quantitative results demonstrate the superiority and effectiveness of our model.

Evaluation of plan complexity and dosimetric plan quality of total marrow and lymphoid irradiation using volumetric modulated arc therapy

To assess the impact of the planner's experience and optimization algorithm on the plan quality and complexity of total marrow and lymphoid irradiation (TMLI) delivered by means of volumetric modulated arc therapy (VMAT) over 2010-2022 at our institute. Eighty-two consecutive TMLI plans were considered. Three complexity indices were computed to characterize the plans in terms of leaf gap size, irregularity of beam apertures, and modulation complexity. Dosimetric points of the target volume (D2%) and organs at risk (OAR) (Dmean) were automatically extracted to combine them with plan complexity and obtain a global quality score (GQS). The analysis was stratified based on the different optimization algorithms used over the years, including a knowledge-based (KB) model. Patient-specific quality assurance (QA) using Portal Dosimetry was performed retrospectively, and the gamma agreement index (GAI) was investigated in conjunction with plan complexity. Plan complexity significantly reduced over the years (r=-0.50, p<0.01). Significant differences in plan complexity and plan dosimetric quality among the different algorithms were observed. Moreover, the KB model allowed to achieve significantly better dosimetric results to the OARs. The plan quality remained similar or even improved during the years and when moving to a newer algorithm, with GQS increasing from 0.019±0.002 to 0.025±0.003 (p<0.01). The significant correlation between GQS and time (r=0.33, p=0.01) indicated that the planner's experience was relevant to improve the plan quality of TMLI plans. Significant correlations between the GAI and the complexity metrics (r=-0.71, p<0.01) were also found. Both the planner's experience and algorithm version are crucial to achieve an optimal plan quality in TMLI plans. Thus, the impact of the optimization algorithm should be carefully evaluated when a new algorithm is introduced and in system upgrades. Knowledge-based strategies can be useful to increase standardization and improve plan quality of TMLI treatments.

Morphology and anatomy of rhizome and root structures in seven Pteris L. species (Pteridaceae) from Southwestern Nigeria

Morphological and anatomical structures of seven Pteris species from various locations in Southwestern Nigeria were examined. This study aimed to affirm the affinities and relevance of rhizome and root structures within the genus Pteris. Mature healthy plants of each species were collected, and morphological observations of rhizomes and roots were documented for each species. The transverse sections of rhizomes were prepared by cutting at 10 µm thickness using Reichert Sliding Microtome while roots were embedded through standard procedures. The results showed that morphologically, rhizomes in P. acanthoneura, P. atrovirens, P. mildbraedii, P. togoensis and P. similis were erect to sub erect, but rhizomes were short creeping in P. ensiformis and P. vittata. The anatomy of the rhizomes showed a typical dictyostele pattern with variable number of leaf gaps, while roots of all the Pteris species were the diarch type with two xylem bundles alternating with two phloem bundles. The morphological and anatomical structures of the rhizomes and the roots provide useful specific distinctions of taxonomic importance for the genus Pteris and the family Pteridaceae.

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.

Characterization of Ethos therapy systems for adaptive radiation therapy: A multi-machine comparison.

The recently released Ethos therapy system (Varian Medical Systems) allows for online CBCT-guided adaptive radiation therapy (RT). The clinical introduction of multiple systems requires machine characterization and machine variation quantification to allow patient interchangeability between systems. Despite several clinical introductions, limited vendor-independent information on machine performance is available. Our aim was to determine the relevant dosimetric and mechanical characteristics of individual machines and to quantify machine variations. Six Ethos treatment machines, equipped with a 6-MV FFF beam including dual-layer MLC and kV-CBCT system, were recently introduced clinically after extensive machine characterization and pre-configured beam model verification. Point doses and profiles were measured and compared to vendor-provided reference data and dose calculations. Also, dose calculations were verified based on point measurements for non-standard fields and dose distributions for optimized treatment plans. Agreements between dose profiles (dose distributions) were quantified using 1D (3D) γ-analysis. Additionally, we quantified leaf transmission, dosimetric leaf gap (DLG) and couch attenuation, determined isocenter accuracy and kV-MV isocenter coincidence and verified the kV-CBCT system. Machine variations were quantified for all dosimetric and mechanical characteristics. For all machines, distinct agreements were found between measurements and vendor-provided reference data as well as measurements and dose calculations. Mean γ1%/1mm values for all profiles were below 0.30. All profiles, point measurements and dose distributions matched well among the six machines. Minimal machine variations were found in terms of DLG (0.05mm), leaf transmission (0.001%), isocenter accuracy (0.08mm), kV-MV isocenter coincidence (0.15mm), couch attenuation (0.69%), and CBCT imaging dose (0.29mGy). This study demonstrates excellent agreement between individual Ethos therapy systems and vendor-provided reference data as well as a pre-configured beam model. Furthermore, our results show good consistency among all machines and provide valuable insights on relevant machine characteristics. The systematically obtained results provide benchmark data for future clinical introduction of Ethos therapy systems.

Commissioning and Performance Evaluation of Varian TrueBeam Linear Accelerator

Aim: The aim of this study is to present a report on the commissioning results of the Varian TrueBeam machine with available photon energies so that it can benefit medical physicists during the entire commissioning process. Subjects and Methods: All tests were performed as per the recommendation of the Atomic Energy Regulatory Board. The beam profiles and percentage depth dose of both photon and electron beams were measured with Sun nuclear Radiation Field Analyzer and 0.125 cc ionization chamber. Output accuracy and consistency of photon and electron beams were measured using 0.6 cc cylindrical ionization chamber and 0.350 cc parallel plate ionization chamber, respectively. To measure the congruency of radiation and optical field and isocenter check with respect to gantry, collimator, and couch, self-developed films were used. Results: Isocenter shift due to gantry, collimator, and couch, rotation was found to be within ± 2-mm diameter circle. Congruency of optical and radiation fields was measured for symmetric fields 5 cm × 5 cm, 10 cm × 10 cm, 20 cm × 20 cm, and 30 cm × 30 cm for all available energy and was within ± 2 mm. Dosimetric leaf gap value for the multi-leaf collimator was measured with 0.125 cc ionization chamber and values are −0.853 mm, −1.106 mm, −1.097 mm, −0.765 mm, and −1.005 mm for energies 6 MV, 10 MV, 15 MV, 6 flattening filter free (FFF), and 10 FFF, respectively. Conclusions: All electrical, mechanical, dosimetric, and safety measurement were found to be within permissible limits. Before clinical use, the beam data are crosschecked with point dosimetry, portal dosimetry, and MapCHECK two-dimensional detector array and results are within tolerance.