Convolution Superposition Research Articles

Evaluation of Dosimetric and Radiobiological Parameters for Different TPS Dose Calculation Algorithms and Plans for Lung Cancer Radiotherapy

Lung cancer presents a major public health concern in our country and around the globe. Radiotherapy is one of the main treatment modalities for lung cancer management for several years. This study aims to evaluate differences in the dosimetric and radiobiological parameters and in the dose distributions of Planning Target Volume (PTV) and organs at risk (OAR) in patients with lung tumors using different Treatment Planning System (TPS) algorithms and Volumetric Modulated Arc Therapy (VMAT) technique. This study was accomplished in a group of 19 patients with lung tumors who were treated in our clinic. In the treatment planning of the patients; Elekta-Monaco with Monte Carlo (XVMC), Pencil Beam algorithm; Varian-Eclipse with Anisotropic Analytical Algorithm (AAA), Acuros XB (AXB)algorithms and Tomo-plan Treatment Planning System with Convolution Superposition algorithm of Tomotherapy device were used. In these treatment planning systems, plans were done by 6 MV photon energy using Volumetric Arc Therapy (VMAT) techniques. The prescribed dose to the PTV was 60 Gy in 30 fractions. Statistical analysis was performed using SPSS Statistics v.29.0.2.0 programme. Statistically, significant differences were found in Dmean, Dmax, D2 and D98 values for PTV between the algorithms, while small differences were found in Dmax values of the contralateral lung, total lung and esophagus in critical organs. It is concluded that the difference between algorithms for PTV increases especially as the volume of the target tumor decreases. TPS with C/S algorithm gave closer results with XVMC. Algorithms were found to have an impact on radiobiological parameters.

Evaluation of monte carlo to support commissioning of the treatment planning system of new pencil beam scanning proton therapy facilities

ABSTRACT Objective. To demonstrate the potential of Monte Carlo (MC) to support the resource-intensive measurements that comprise the commissioning of the treatment planning system (TPS) of new proton therapy facilities. Approach. Beam models of a pencil beam scanning system (Varian ProBeam) were developed in GATE (v8.2), Eclipse proton convolution superposition algorithm (v16.1, Varian Medical Systems) and RayStation MC (v12.0.100.0, RaySearch Laboratories), using the beam commissioning data. All models were first benchmarked against the same commissioning data and validated on seven spread-out Bragg peak (SOBP) plans. Then, we explored the use of MC to optimise dose calculation parameters, fully understand the performance and limitations of TPS in homogeneous fields and support the development of patient-specific quality assurance (PSQA) processes. We compared the dose calculations of the TPSs against measurements (DDTPSvs.Meas.) or GATE (DDTPSvs.GATE) for an extensive set of plans of varying complexity. This included homogeneous plans with varying field-size, range, width, and range-shifters (RSs) (n = 46) and PSQA plans for different anatomical sites (n = 11). Main results. The three beam models showed good agreement against the commissioning data, and dose differences of 3.5% and 5% were found for SOBP plans without and with RSs, respectively. DDTPSvs.Meas. and DDTPSvs.GATE were correlated in most scenarios. In homogeneous fields the Pearson’s correlation coefficient was 0.92 and 0.68 for Eclipse and RayStation, respectively. The standard deviation of the differences between GATE and measurements (±0.5% for homogeneous and ±0.8% for PSQA plans) was applied as tolerance when comparing TPSs with GATE. 72% and 60% of the plans were within the GATE predicted dose difference for both TPSs, for homogeneous and PSQA cases, respectively. Significance. Developing and validating a MC beam model early on into the commissioning of new proton therapy facilities can support the validation of the TPS and facilitate comprehensive investigation of its capabilities and limitations.

Weakly supervised medical image registration with multi-information guidance

In recent years, the registration method based on deep learning has received extensive attention from scholars due to its superiority in real-time performance. Most of the work directly use convolutional neural networks (CNNs) to map the image to be registered into the transform space. However, the receptive field of CNNs is limited, and multi-layer convolution superposition is needed to obtain a relatively large receptive field. Transformer-based methods can better express spatial relationships through attention mechanisms. However, the self-attention and the multi-head mechanisms make each small block calculate the relationship with other small blocks regardless of distance. Due to the limited moving range of corresponding voxel points in the medical images, this long-distance dependence may cause the model to be interfered by long-distance voxels. In this paper, we convert the spatial transformation of the corresponding voxel points into the calculation of the basic vector basis to propose the SV-basis module and design a two-stage multi-scale registration model. Experiments are carried out on brain and lung datasets to prove the effectiveness and universality of the proposed registration method. According to the anatomical characteristics of medical images, the corresponding loss function is designed to introduce mask information into the registration task. The experimental results show that the proposed method can accurately register brain and lung images.

Compressive nonstationary near-field acoustic holography for reconstructing the instantaneous sound field

A compressive nonstationary near-field acoustic holography based on time-domain plane wave superposition method is proposed to precisely reconstruct the instantaneous sound field using the fewer measurement points. In the proposed method, by means of an instantaneous propagation kernel, a time-domain convolution superposition formula is established for relating the time-variant pressure of the hologram plane to the pressure time-wavenumber spectra of the virtual source plane. Next, compressed sensing theory and sparse representation framework are introduced into the time-marching solving inverse process of the established formula, and the smoothed l0 norm and revised Newton optimization algorithm are employed to solve the serious cumulative ill-posed problem over time for acquiring the pressure time-wavenumber spectra. The key is to approximately replace the discontinuous ℓ0 norm by using a suitable continuous function, and the revised Newton algorithm is applied to minimize the continuous function for obtaining the optimal solution. Finally, with the aid of a time-domain convolution formula, the solved pressure time-wavenumber spectra are employed to calculate the time-variant pressure on the reconstruction plane. The proposed method not only can evidently suppress the ill-posed problem of time-marching solving inverse process for improving the reconstruction accuracy, but also can greatly reduce the measurement points and microphone number for drastically decreasing the measurement cost on the premise of ensuring the reconstruction accuracy and spatial resolution. A numerical simulation is conducted, and the simulation effects prove that the proposed method can accurately reconstruct the instantaneous sound field with the fewer measurement points in time–space domains. The reconstruction results are also compared to those of time-domain plane wave superposition method with Tikhonov regularization to verify the superiority of the proposed method with fewer measurement points under different parameter configurations. An experiment of an impaction steel plate is implemented for further validating the competence of the proposed method.

Using density computed tomography images for photon dose calculations in radiation oncology: A patient study.

Conventional workflows for dose calculations require conversions between Hounsfield Units (HU) and the mass or electron density for Computed Tomography (CT) images in the Treatment Planning System (TPS). These conversions are scanner- and mostly kVp-dependent. A density representation or reconstruction at the CT level can potentially simplify the workflow. This study aimed to investigate the agreement between these two methods for patients and different calculation algorithms. Density conversions for conventional HU-density conversions were first established using two phantoms with appropriate inserts. Next, the differences in density and dose calculations between both methods were assessed using 95% Limits of Agreement (LOA) Bland-Altman analysis for 44 consecutive clinical patient cases. These cases represented a mix of indications, algorithms (collapsed cone, convolution superposition, ray tracing, finite-size pencil beam, and Monte Carlo), and scan kVp (80 to 140) in two different commercial TPS. No statistically significant bias in density or dose calculations was found between the two methods. Furthermore, 95% LOAs between both methods were±0.05g/cm3 and±0.1Gy for density and dose, respectively. Small but clinically irrelevant dose differences were found in high-density gradient regions for convolution superposition calculations or CT scans with non-delayed contrast agent injections with targets nearby vessels. The in vivo density-reconstructed images at the CT level were assessed to be equivalent. Therefore, they can simplify and improve clinical workflows, allowing patient-specific acquisitions for contouring and density-reconstructed images for dose calculations.

Traffic Sign Detection Based on Improved YOLOv5

<p>As a popular research direction in the field of intelligent transportation, various scholars have widely concerned themselves with traffic sign detection However, there are still some key issues that need to be further solved in order to thoroughly apply related technologies to real scenarios, such as the feature extraction scheme of traffic sign images, and the optimal selection of detection methods. For the purpose of overcoming these difficulties. This paper proposes a YOLO-based traffic sign detection framework. Firstly, a lightweight convolution attention mechanism is embedded into the backbone network to obtain the information of space and channel; Secondly, the multi-scale awareness module is used to replace large convolution with 3×3 convolution superposition to improve the receptive field area of the object in the model and enhance the feature fusion performance of the model; Finally, CIoU is used as the loss function of the bounding box to locate the experimental object with high precision. The experimental results show that on the CCTSDB data set, the MAP of this method reaches 91.0%, which is 3.5% higher than the original YOLOv5. Compared with other mainstream object detection algorithms, it has a certain degree of improvement, which proves the effectiveness of this method.</p> <p> </p>

The effect of common dental fixtures on treatment planning and delivery for head and neck intensity modulated proton therapy.

Proton treatment plan perturbation by common dental fixtures such as amalgams (Am) and porcelain-fused-to-metal (PFM) crowns has, to date, been uncharacterized. Previous studies have been conducted to determine the physical effect of these materials within the beam path for single spots, but their effects on complex treatment plans and clinical anatomy have not yet been quantified. The present manuscript aims to study the effect of Am and PFM fixtures on proton treatment planning in a clinical setting. An anthropomorphic phantom with removable tongue, maxilla, and mandible modules was simulated on a clinical computed tomography (CT) scanner. Spare maxilla modules were modified to include either a 1.5mm depth central groove occlusal amalgam (Am) or a porcelain-fused-to-metal (PFM) crown, implanted on the first right molar. Modified tongue modules were 3D printed to accommodate several axial or sagittal oriented pieces of EBT-3 film. Clinically representative spot-scanning proton plans were generated in Eclipse v.15.6 using the proton convolution superposition (PCS) algorithm v.15.6.06 using a multi-field optimization (MFO) technique with the goal of delivering a uniform 54Gy dose to a clinical target volume (CTV) typical of a base-of-tongue (BoT) treatment. A typical geometric beam arrangement of two anterior oblique (AO) beams and a posterior beam was employed. Plans optimized without any material overrides were delivered to the phantom A) without implants; B) with Am fixture; or C) with PFM crown. Plans were also reoptimized and delivered with inclusion of material overrides to equate relative stopping power of the fixture with that of a previously measured result. Plans exhibit slightly greater dose weight towards AO beams. The optimizer accounted for inclusion of fixture overrides by increasing beam weights to the beam closest to the implant. Film measurements exhibited cold spots directly within the beam path through the fixture in plans with and without overridden materials. Cold spots were somewhat mitigated in plans including overridden materials in the structure set but were not entirely eliminated. Cold spots associated with Am and PFM fixtures were quantified at 17% and 14% for plans without overrides, respectively, and 11% and 9% with using Monte Carlo simulation. Compared with film measurements and Monte Carlo simulation, the treatment planning system underestimates the dose shadowing effect in plans including material overrides. Dental fixtures create a dose shadowing effect directly in line with the beam path through the material. This cold spot is partially mitigated by overriding the material to measured relative stopping powers. Due to uncertainties in modeling perturbation through the fixture, the magnitude of the cold spot is underestimated using the institutional TPS when compared to measurement and MC simulation.

Enhanced transfer learning method for rolling bearing fault diagnosis based on linear superposition network

Deep transfer learning is used to solve the problem of unsupervised intelligent fault diagnosis of rolling bearings. However, when the data distribution between two domains is different, the existing deep transfer learning models which only rely on the domain-invariant features are not enough to complete the target domain data learning. To solve this problem, an enhanced transfer learning method based on the linear superposition network is proposed for rolling bearing fault diagnosis. This method improves the structure of the one-dimensional convolutional neural network (1D-CNN) by constructing linear superposition convolution blocks. At the same time, the loss function of transfer learning is constructed by using the pseudo-label of the target domain from the network, which enhances the ability of rolling bearing fault feature extraction. Compared with the traditional feature-based transfer learning methods, the proposed enhanced transfer learning method based on the linear superposition network can make the network place more stress on the feature learning of the target domain. Experimental results on the Paderborn University (PU) dataset show that, compared with the improved deep adaptation network (DAN) model, the proposed method improves the average diagnosis accuracy by 21% on six transfer tasks, showing improved bearing fault diagnostic precision.

Brain Functional Networks with Dynamic Hypergraph Manifold Regularization for Classification of End-Stage Renal Disease Associated with Mild Cognitive Impairment

The structure and function of brain networks have been altered in patients with end-stage renal disease (ESRD). Manifold regularization (MR) only considers the pairing relationship between two brain regions and cannot represent functional interactions or higher-order relationships between multiple brain regions. To solve this issue, we developed a method to construct a dynamic brain functional network (DBFN) based on dynamic hypergraph MR (DHMR) and applied it to the classification of ESRD associated with mild cognitive impairment (ESRDaMCI). The construction of DBFN with Pearson’s correlation (PC) was transformed into an optimization model. Node convolution and hyperedge convolution superposition were adopted to dynamically modify the hypergraph structure, and then got the dynamic hypergraph to form the manifold regular terms of the dynamic hypergraph. The DHMR and L1 norm regularization were introduced into the PC-based optimization model to obtain the final DHMR-based DBFN (DDBFN). Experiment results demonstrated the validity of the DDBFN method by comparing the classification results with several related brain functional network construction methods. Our work not only improves better classification performance but also reveals the discriminative regions of ESRDaMCI, providing a reference for clinical research and auxiliary diagnosis of concomitant cognitive impairments.

Accuracy Evaluation of Collapsed Cone Convolution Superposition Algorithms for the Nasopharynx Interface in the Early Stage of Nasopharyngeal Carcinoma.

This study combined the use of radiation dosimeteric measurements and a custom-made anthropomorphic phantom in order to evaluate the accuracy of therapeutic dose calculations at the nasopharyngeal air-tissue interface. The doses at the nasopharyngeal air-tissue interface obtained utilizing the Pinnacle and TomoTherapy TPS, which are based on collapsed cone convolution superposition (CCCS) algorithms, were evaluated and measured under single 10 × 10 cm2, 2 × 2 cm2, two parallel opposed 2 × 2 cm2 and clinical fields for early stage of nasopharyngeal carcinoma by using EBT3, GR-200F, and TLD 100. At the air-tissue interface under a 10 × 10 cm2 field, the TPS dose calculation values were in good agreement with the dosimeter measurement with all differences within 3.5%. When measured the single field 2 × 2 cm2, the differences between the average dose were measured at the distal interface for EBT3, GR-200F, and TLD-100 and the calculation values were -15.8%, -16.4%, and -4.9%, respectively. When using the clinical techniques such as IMRT, VMAT, and tomotherapy, the measurement results at the interface for all three techniques did not imply under dose. Small-field sizes will lead to dose overestimation at the nasopharyngeal air-tissue interface due to electronic disequilibrium when using CCCS algorithms. However, under clinical applications of multiangle irradiation, the dose errors caused by this effect were not significant.

Extending in aqua portal dosimetry with dose inhomogeneity conversion maps for accurate patient dose reconstruction in external beam radiotherapy.

Background and purposeIn aqua dosimetry with electronic portal imaging devices (EPIDs) allows for dosimetric treatment verification in external beam radiotherapy by comparing EPID-reconstructed dose distributions (EPID_IA) with dose distributions calculated with the treatment planning system in water-equivalent geometries. The main drawback of the method is the inability to estimate the dose delivered to the patient. In this study, an extension to the method is presented to allow for patient dose reconstruction in the presence of inhomogeneities.Materials and methodsEPID_IA dose distributions were converted into patient dose distributions (EPID_IA_MC) by applying a 3D dose inhomogeneity conversion, defined as the ratio between patient and water-filled patient dose distributions computed using Monte Carlo calculations. EPID_IA_MC was evaluated against dose distributions calculated with a collapsed cone convolution superposition (CCCS) algorithm and with a GPU‐based Monte Carlo dose calculation platform (GPUMCD) using non-transit EPID measurements of 25 plans. In vivo EPID measurements of 20 plans were also analyzed.ResultsIn the evaluation of EPID_IA_MC, the average γ-mean values (2% local/2mm, 50% isodose volume) were 0.70 ± 0.14 (1SD) and 0.66 ± 0.10 (1SD) against CCCS and GPUMCD, respectively. Percentage differences in median dose to the planning target volume were within 3.9% and 2.7%, respectively. The number of in vivo dosimetric alerts with EPID_IA_MC was comparable to EPID_IA.ConclusionsEPID_IA_MC accommodates accurate patient dose reconstruction for treatment disease sites with significant tissue inhomogeneities within a simple EPID-based direct dose back-projection algorithm, and helps to improve the clinical interpretation of both pre-treatment and in vivo dosimetry results.

A simple agent-based model to simulate 3D tumor-induced angiogenesis considering the evolution of the hypoxic conditions of the cells

In tumors, angiogenesis (conformation of a new vasculature from another primal one) is produced with the releasing of tumor angiogenic factors from hypoxic cells. These angiogenic substances are distributed around the tumor micro-environment by diffusion. When they reach the primal blood vessel bed, the sprouting and branching of a new micro-vascular network is produced. These new capillaries will supply oxygen to cells so that their hypoxic state is overcome. In this work, a new and simple 3D agent-based model to simulate tumor-induced angiogenesis is presented. In this approach, the evolution of the hypoxic conditions in cells along the related conformation of the new micro-vessels is considered. The importance that the relative position of the primal vasculature and tumor structure takes in the final distribution of the new micro-vasculature has also been addressed. The diffusion of angiogenic factors and oxygen has been modelled at the targets by numerical convolution superposition of the analytical solution from the sources. Qualitative and quantitative results show the importance of tip endothelial cells in overcoming hypoxic conditions in cells at early stages of angiogenesis. At final stages, anastomosis plays an important role in the reduction of hypoxia in cells.

Dosimetric Impact of Systematic and Random Errors With Multi-Leaf Collimator Leaf Positioning in Intracranial Stereotactic Radiosurgery

Modern medical Linac equipped with multileaf collimators (MLC) are widely used for intracranial stereotactic radiosurgery (SRS). This study aimed to evaluate dosimetric impact of systematic and random positioning errors with MLC leaves and to establish practical limits on MLC leaf positioning uncertainties in SRS treatments.A single institution retrospective review identified 10 clinical single-target conformal arc SRS plans and 7 single- or multiple-target VMAT SRS plans treating a total of 15 PTVs. Each conformal arc SRS plan was prescribed 20 Gy in one fraction to a PTV using 2-5 arc fields with PTV volume ranging from 0.10 to 2.48 cc, and each volumetric modulated arc therapy (VMAT) SRS plan was prescribed 20 - 27 Gy in 1 - 5 fractions to 1 - 4 PTVs, with PTV volume ranging from 0.11 to 7.73 cc. 6-MV flattening-filter free photon fields were used in all the plans. A 120-leaf MLC was used for beam modulation with a leaf width of 2.5 mm for the central 32 leaf-pairs and 5.0 mm for the peripheral 28 leaf-pairs. Systematic or random leaf positioning errors ranging from 0.1 to 1.0 mm at 0.1-mm intervals were introduced to each SRS plan. Plan dose was re-calculated with original field MUs with a 1.0 mm calculation resolution using a linear Boltzmann transport algorithm and was verified using another dose calculation engine with a convolution superposition algorithm. Variations in PTV dosimetric parameters including mean dose and D(95%) due to systematic and random leaf positioning errors were analyzed. PTV dose variation was also verified with measurement using an ion chamber in water equivalent phantom.Both the PTV mean dose and D(95%) decreased linearly with increasing systematic and random positioning errors in all the SRS plans. In systematic leaf positioning errors with a 1-mm shift towards the isocenter for all MLC leaves, the PTV mean dose and D(95%) decreased by an average of 8.7% (range: 5.1 - 12.4%) and 15.2% (range: 10.4 - 20.1%) respectively in the conformal arc plans; the PTV mean dose and D(95%) decreased by an average of 18.2% (range: 11.6 - 25.0%) and 21.8% (range: 15.1 - 27.3%) respectively in the VMAT plans. The PTV volume positively correlated with the declining rate of mean PTV dose (correlation coefficient: 0.80, P-value = 0.01) with the conformal arc SRS plans. In random leaf positioning errors, with a standard deviation of up to 1 mm, the decrease of PTV mean dose and D(95%) was up to 2.4% and 3.3% respectively in the conformal arc plans, and decreased up to 2.7% and 2.8% respectively in the VMAT plans.Manufacturers' specifications for MLC leaf positioning accuracy are not adequate for accurate intracranial SRS dose delivery. To ensure that both the PTV mean dose and D(95%) are within 5% from plan dose, the systematic MLC leaf positioning error needs to be < 0.3 mm for conformal arc SRS plans and < 0.2 mm for VMAT SRS plans. On the other hand, random leaf positioning errors have smaller impact on dose delivery accuracy within the limits specified by manufacturers.C. Han: None. J.R. Liu: None. D. Du: None. J. Liang: None. K. Qing: None. W.T. Watkins: None. S. Zhang: None. A. Liu: None.

Dosimetric Comparison of Single-Isocenter and Multiple-Isocenter Techniques for Two-Lesion Lung SBRT Using the RefleXion High-Speed Ring-Gantry System

Due to the technical challenges that accompany stereotactic body radiotherapy (SBRT) to multiple lesions, a single-isocenter treatment plan technique for multi-target scenarios may optimize the final, aggregate dose distribution. The RefleXion™ X1 is a new ring-gantry based system with several features that may optimize dosimetry in a multi-target SBRT scenario, including high speed rotation (60 revolutions per minute), a high-speed multi-leaf collimator (100 Hz), and a variable pitch couch. A comprehensive dosimetric comparison for multiple targets that contrasts multiple isocenters versus a single-isocenter technique has not been performed. Therefore, this investigation performed such a comparison in two-lesion lung SBRT cases.Three patients with two synchronous lung lesions with maximum dimension of 5 cm underwent single-isocenter and two-isocenter planning with the RefleXion treatment planning system using a Collapsed Cone Convolution Superposition algorithm. Treatment doses were in accordance with AAPM TG101 criteria (50Gy in 5 fractions) and planned such that 95% of the planning target volume (PTV) would receive at least 95% of the prescription dose. In the single-isocenter plans, the isocenter was placed between the two-lesions, whereas the two-isocenter plans localized the isocenters on each target with a limitation on the IEC-X direction to avoid couch-bore collisions. Conformity index (CI), homogenous index (HI), R50%, and D2cm were calculated. The volume of the normal lung receiving 5 Gy (V5), V10, V15, V20, mean lung dose, and dose to other OARs were evaluated. In addition, the treatment time to deliver each plan was recorded.The mean isocenter to the tumor distance in XYZ-space was 8.53 cm (range: 7.66-10.62 cm). The mean combined PTV was 42.47cc (range: 9.7- 97.8 cc). There was no significant difference in target coverage, maximum target dose, CI, HI, R50%, V20, V15, and V10 between single-isocenter and two-isocenters for lung SBRT plans. On average, single-isocenter plans contributed an increase in OAR dose, especially to the carina and esophagus, by 5.17% (range: 2.91-6.52%) and 15.62% (range: 6.44-22.97%), respectively. With the RefleXion X1 set at 850MU/min, the average difference in total beam-on time was approximately 33 seconds between the two approaches. However, set-up time for the second isocenter in the two-isocenter plan would likely prolong the total treatment delivery time.This study shows that a single-isocenter technique for the scenario of two lung lesions is dosimetrically feasible and clinically efficient with the RefleXion X1 in SBRT mode. Single-isocenter treatment delivery techniques may be a desirable option for efficient radio-ablative therapies directed at multiple metastatic targets.

Investigation of Buildup Region and Surface Dose: Comparison of Parallel Plane Ion Chamber, Treatment Planning System, and MC Simulation

In these days, Monte Carlo (MC) simulation is a method that can calculate the radiation dose that occurs in an environment in the most accurate way. The correct measurement of the dose occurring on the patient’s surface is of great importance to estimate the reactions that may occur on the patient’s skin. This importance encouraged us to do this study. The aim of this study is to determine buildup region and surface doses using MC simulation and to compare them with results of the parallel plane ion chamber and Treatment Planning System (TPS) measurements for 6-MV photon beams. Surface doses normalized to the maximum dose for the parallel plane ion chamber, MC simulation, fast photon (FP) algorithm, and collapsed cone convolution superposition (CC) algorithm are 13.6%, 30.28%, 0%, and 27.33%, respectively. The CC algorithm and parallel plane ion chamber measurements are compatible with MC simulation but the FP algorithm has calculated the dose less to a depth of 0.8 cm. Measuring the surface dose and the doses in the buildup region is of great importance in terms of accurately predicting the complications that may occur in the patient’s skin and taking precautions early. Using some methods and correction factors, the surface dose and the doses that may occur in the buildup region can be accurately calculated. It is recommended not to use the FP algorithm for stereotactic body radiation therapy and intensity-modulated radiation therapy treatments, as it cannot calculate doses correctly in the buildup region and surface.

Reduction of superficial radiation dose with bolus in passive scattering proton beam therapy.

PurposeIn passive scattering proton beam therapy, scattered protons from the snout and aperture increase the superficial dose, however, treatment planning systems (TPSs) based on analytic algorithms (such as proton convolution superposition) are often inaccurate in this aspect. This additional dose can cause permanent alopecia or severe radiation dermatitis. This study aimed to evaluate the effect of bolus on the superficial radiation dose in passive scattering proton beam therapy.MethodsWe drew a clinical target volume (CTV) and a scalp‐p (phantom), and created plans using a TPS for a solid water phantom with and without bolus. We calculated the dose distribution in the established plans independently with Monte Carlo (MC) simulation and measured the actual dose distribution with an array of ion chambers and radiochromic films. To assess the clinical impact of bolus on scalp dose, we conducted independent dose verification using MC simulation in a clinical case.ResultsIn the solid water phantom without bolus, the calculated scalp‐p volume receiving 190 cGy was 20% with TPS but 80% with MC simulation when the CTV received 200 cGy. With 2 cm bolus, this decreased from 80% to 10% in MC simulation. With the measurements, average superficial dose to the scalp‐p was reduced by 5.2% when 2 cm bolus was applied. In the clinical case, the scalp‐c (clinical) volume receiving 3000 cGy decreased from 74% to 63% when 2 cm bolus was applied.ConclusionThis study revealed that bolus can reduce radiation dose at the superficial body area and alleviate toxicity in passive scattering proton beam therapy.

Optimized Estimation of Scattered Radiation for X-ray Image Improvement: Realistic Simulation

Image processing algorithms for compensation of the scattered radiation influence in X-ray imaging are proposed, studied and optimized by numerical simulation. These algorithms include the scattering estimation by convolution (superposition) technique, estimation of kernel functions by Monte Carlo (MC) simulation, the determination of the optimal number and shape of kernel functions and image segmentation. The determination of the number and shape of kernel functions was performed by the MC simulation of the realistic Zubal phantom and the clustering analysis of shape features of kernel functions. Testing simulation study of the algorithms for chest images at 75 keV proves that the optimal number of kernel functions is equal to 8. This number provides the three-fold contrast enhancement without using the anti-scatter grids. The achieved contrast is about 95% of the primary image contrast that exceeds contrast enhancements achieved with anti-scatter grids. An increased number of used kernel functions provides a better image contrast and better resolution of scattered radiation image, but estimation errors also increase due to the segmentation and deconvolution errors.

Dose Calculation Comparisons between Three Modern Treatment Planning Systems.

Purpose:Monaco treatment planning system (TPS) version 5.1 uses a Monte-Carlo (MC)-based dose calculation engine. The aim of this study is to verify and compare the Monaco-based dose calculations with both Pinnacle3 collapsed cone convolution superposition (CCCS) and Eclipse anisotropic analytical algorithm (AAA) calculations.Materials and Methods:For this study, 18 previously treated lung and head-and-neck (HN) cancer patients were chosen to compare the dose calculations between Pinnacle, Monaco, and Eclipse. Plans were chosen from those that had been treated using the Elekta VersaHD or a Novalis Tx linac. All of the treated volumetric-modulated arc therapy plans used 6 MV or 10 MV photon beams. The original plans calculated with CCCS or AAA along with the recalculated ones using MC from the three TPS were exported into Velocity software for intercomparison.Results:To compare the dose calculations, Planning target volume (PTV) heterogeneity indexes and conformity indexes were calculated from the dose volume histograms (DVH) of all plans. While mean lung dose (MLD), lung V5 and V20 values were recorded for lung plans, the computed dose to parotids, brainstem, and mandible were documented for HN plans. In plan evaluation, percent differences of the above dosimetric values in Monaco computation were compared against each of the other TPS computations.Conclusion:It could be concluded through this research that there can be differences in the calculation of dose across different TPSs. Although relatively small, these differences could become apparent when compared using DVH. These differences most likely arise from the different dose calculation algorithms used in each TPS. Monaco employs the MC allowing it to have much more detailed calculations that result in it being seen as the most accurate and the gold standard.

Questionnaire survey on treatment planning techniques for lung stereotactic body radiotherapy in Japan.

ABSTRACTThis study aimed to obtain details regarding treatment planning techniques for lung stereotactic body radiation therapy (SBRT) employed at each institution in Japan by using a questionnaire survey. An Internet questionnaire survey on SBRT procedures performed in 2016 was conducted by the QA/QC committee of the Japan Society of Medical Physics from April to June 2017. The questionnaire assessed two aspects: the environment for SBRT at each institution and the treatment planning techniques with and without respiratory motion management techniques (RMMT). Of the 309 evaluated responses, 218 institutions had performed SBRT. A total of 186 institutions performed SBRT without RMMT and 139 institutions performed SBRT with RMMT. When respiratory motion was ≥10 mm, 69 institutions applied RMMT. The leading RMMT were breath holding (77 institutions), respiratory gating (49 institutions) and real-time tumor tracking (11 institutions). The most frequently used irradiation technique was 3D conformal radiotherapy, which was used in 145 institutions without RMMT and 119 institutions with RMMT. Computed tomography (CT) images acquired under free breathing were mostly used for dose calculation for patients treated without RMMT. The usage ratio of IMRT/VMAT to SBRT is low in Japan, compared to elsewhere in the world (<20% vs ≥70%). Among the available dose calculation algorithms, superposition convolution was the most frequently used regardless of RMMT; however, 2% of institutions have not yet made heterogeneity corrections. In the prescription setting, about half of the institutions applied point prescriptions. The survey results revealed the most frequently used conditions, which may facilitate standardization of treatment techniques in lung SBRT.

Calculation of clinical dose distributions in proton therapy from microdosimetry.

To introduce a new algorithm-MicroCalc-for dose calculation by modeling microdosimetric energy depositions and the spectral fluence at each point of a particle beam. Proton beams are considered as a particular case of the general methodology. By comparing the results obtained against Monte Carlo computations, we aim to validate the microdosimetric formalism presented here and in previous works. In previous works, we developed a function on the energy for the average energy imparted to a microdosimetric site per event and a model to compute the energetic spectrum at each point of the patient image. The number of events in a voxel is estimated assuming a model in which the voxel is completely filled by microdosimetric sites. Then, dose at every voxel is computed by integrating the average energy imparted per event multiplied by the number of events per energy beam of the spectral distribution within the voxel. Our method is compared with the proton convolution superposition (PCS) algorithm implemented in Eclipse™ and the fast Monte Carlo code MCsquare, which is here considered the benchmark, for in-water calculations, using in both cases clinically validated beam data. Two clinical cases are considered: a brain and a prostate case. For a SOBP beam in water, the mean difference at the central axis found for MicroCalc is of 0.86% against 1.03% for PCS. Three-dimensional gamma analyses in the PTVs compared with MCsquare for criterion (3%, 3mm) provide gamma index of 95.07% with MicroCalc vs 94.50% with PCS for the brain case and 99.90% vs 100.00%, respectively, for the prostate case. For selected organs at risk in each case (brainstem and rectum), mean and maximum difference with respect to MCsquare dose are analyzed. In the brainstem, mean differences are 0.25Gy (MicroCalc) vs 0.56Gy (PCS), whereas for the rectum, these values are 0.05Gy (MicroCalc) vs 0.07Gy (PCS). The accuracy of MicroCalc seems to be, at least, not inferior to PCS, showing similar or better agreement with MCsquare in the considered cases. Additionally, the algorithm enables simultaneous computation of other quantities of interest. These results seem to validate the microdosimetric methodology in which the algorithm is based on.