Complementary characterization method of 3D arsenic doping by using medium energy ion scattering

We report on a new characterization method of 3D-doping performed by arsenic implantation into FinFET-like nanostructures by using Medium Energy Ion Scattering. Because of its good depth resolution (0.25 nm) at the surface, it is one of techniques of choice suitable to analyse the ultra-shallow doping of thin crystal films. However, with the constraints related to the nanostructures' geometry and the low lateral resolution of the MEIS beam (0.5 x 1 mm(2)), we developed an adequate protocol allowing their analysis with this technique. It encompasses three different geometries to account for the MEIS spectra of the arsenic implanted in each part of the nanostructures. The originality of the protocol is that, according to the chosen analysis geometry, the overall spectrum of arsenic is not the same because the contributions of each part of the patterns to its formation are different. By using two of them, we observed double peaks of arsenic. Thanks to 3D deconvolutions performed with PowerMEIS simulations, we were able to identify the contribution of the tops, sidewalls and bottoms in their formation. Thus, by separating the spectrum of the dopants implanted in the Fins (tops + sidewalls) from that of the bottoms, we were able to characterize the 3D doping conformity in the patterns. Two different implantation methods with the associated local doses computed in each single part were investigated. We found that the distribution of the dopants implanted by using the conventional implanter method is very different from that of plasma doping

Classification of various sources of error in range assessment using proton radiography and neural networks in head and neck cancer patients

This study evaluates the suitability of convolutional neural networks (CNN) to automatically process proton radiography (PR) based images. CNNs are used to classify PR images impaired by several sources of error affecting the proton range, more precisely setup and calibration curve errors. PR simulations were performed in 40 head and neck cancer patients, at three different anatomical locations (fields A, B and C, centered for head and neck, neck and base of skull coverage). Field sizes were 26x26cm2 for field A and 4.5x4.5cm2 for fields B and C. Range shift maps were obtained by comparing an unperturbed reference PR against a PR where one or more sources of error affected the proton range. CT calibration curve errors in soft, bone and fat tissues and setup errors in the anterior-posterior and inferior-superior directions were simulated individually and in combination. A CNN was trained for each type of PR field, leading to 3 CNNs trained with a mixture of range shift maps arising from one or more sources of range error. To test the full/partial/wrong agreement between predicted and actual sources of range error in the range shift maps, exact, partial and wrong match percentages were computed for an independent test dataset containing range shift maps arising from isolated or combined errors, retrospectively. The CNN corresponding to field A showed superior capability to detect isolated and combined errors, with exact matches of 92% and 71% respectively. Field B showed exact matches of 80% and 54%, and field C resulted in exact matches of 77% and 41%. The suitability of CNNs to classify PR based images containing different sources of error affecting the proton range was demonstrated. This procedure enables the detection of setup and calibration curve errors when they appear individually or in combination, providing valuable information for the interpretation of PR images

Stopping and straggling of 60-250-keV backscattered protons on nanometric Pt films

The stopping power and straggling of backscattered protons on nanometric Pt films were measured at low to medium energies (60-250 keV) by using the medium-energy ion scattering technique. The stopping power results are in good agreement with the most recent measurements by Primetzhofer Phy s. Rev. B 86, 094102 (2012) and are well described by the free electron gas model at low projectile energies. Nevertheless, the straggling results are strongly underestimated by well-established formulas up to a factor of two. Alternatively, we propose a model for the energy-loss straggling that takes into account the inhomogeneous electron-gas response, based on the electron-loss function of the material, along with bunching effects. This approach yields remarkable agreement with the experimental data, indicating that the observed enhancement in energy-loss straggling is due to bunching effects in an inhomogeneous electron system. Nonlinear effects are of minor importance for the energy-loss straggling

Platform for automatic patient quality assurance via Monte Carlo simulations in proton therapy

For radiation therapy, it is crucial to ensure that the delivered dose matches the planned dose. Errors in the dose calculations done in the treatment planning system (TPS), treatment delivery errors, other software bugs or data corruption during transfer might lead to significant differences between predicted and delivered doses. As such, patient specific quality assurance (QA) of dose distributions, through experimental validation of individual fields, is necessary. These measurement based approaches, however, are performed with 2D detectors, with limited resolution and in a water phantom. Moreover, they are work intensive and often impose a bottleneck to treatment efficiency. In this work, we investigated the potential to replace measurement-based approach with a simulation-based patient specific QA using a Monte Carlo (MC) code as independent dose calculation engine in combination with treatment log files. Our developed QA platform is composed of a web interface, servers and computation scripts, and is capable to autonomously launch simulations, identify and report dosimetric inconsistencies. To validate the beam model of independent MC engine, in-water simulations of mono-energetic layers and 30 SOBP-type dose distributions were performed. Average Gamma passing ratio 99 ± 0.5% for criteria 2%/2 mm was observed. To demonstrate feasibility of the proposed approach, 10 clinical cases such as head and neck, intracranial indications and craniospinal axis, were retrospectively evaluated via the QA platform. The results obtained via QA platform were compared to QA results obtained by measurement-based approach. This comparison demonstrated consistency between the methods, while the proposed approach significantly reduced in-room time required for QA procedures

Inter- and intrafractional 4D dose accumulation for evaluating ΔNTCP robustness in lung cancer.

BACKGROUND AND PURPOSE Model-based selection of proton therapy patients relies on a predefined reduction in normal tissue complication probability (NTCP) with respect to photon therapy. The decision is necessarily made based on the treatment plan, but NTCP can be affected when the delivered treatment deviates from the plan due to delivery inaccuracies. Especially for proton therapy of lung cancer, this can be important because of tissue density changes and, with pencil beam scanning, the interplay effect between the proton beam and breathing motion. MATERIALS AND METHODS In this work, we verified whether the expected benefit of proton therapy is retained despite delivery inaccuracies by reconstructing the delivered treatment using log-file based dose reconstruction and inter- and intrafractional accumulation. Additionally, the importance of two uncertain parameters for treatment reconstruction, namely deformable image registration (DIR) algorithm and α/β ratio, was assessed. RESULTS The expected benefit or proton therapy was confirmed in 97% of all studied cases, despite regular differences up to 2 percent point (p.p.) NTCP between the delivered and planned treatments. The choice of DIR algorithm affected NTCP up to 1.6 p.p., an order of magnitude higher than the effect of α/β ratio. CONCLUSION For the patient population and treatment technique employed, the predicted clinical benefit for patients selected for proton therapy was confirmed for 97.0 % percent of all cases, although the NTCP based proton selection was subject to 2 p.p. variations due to delivery inaccuracies

Characterization of oxygen self-diffusion in TiO2 resistive-switching layers by nuclear reaction profiling

Oxygen self-diffusion was investigated in TiO2 layers employed for resistive-switching memories using resonant nuclear reaction profiling (NRP) and 18O labeling. The layers were grown using physical vapor deposition technique (sputtering) and were polycrystalline. The diffusivity was measured over the temperature range 600–800 °C and the activation energy for oxygen self-diffusion in sputter-deposited TiO2 films determined to be 1.09 ± 0.16 eV, a value consistent with results obtained by previous studies (Marmitt et al., 2017).This study was financed in part by the Coordena de Aperfei oamento de Pessoal de Nvel Superior – Brasil (CAPES) – Finance Code 001, by CNPq and PRONEX-FAPERGS. We acknowledge support from the NCRIS ANFF and Heavy-Ion Accelerator Capabilities, with particular thanks to Dr. Fouad Karouta for the deposition of the (Si3N4) at ANU. RGE and MV further acknowledge the ARC funding program for financial support

Comparison of CBCT based synthetic CT methods suitable for proton dose calculations in adaptive proton therapy

In-room imaging is a prerequisite for adaptive proton therapy. The use of onboard cone-beam computed tomography (CBCT) imaging, which is routinely acquired for patient position verification, can enable daily dose reconstructions and plan adaptation decisions. Image quality deficiencies though, hamper dose calculation accuracy and make corrections of CBCTs a necessity. This study compared three methods to correct CBCTs and create synthetic CTs that are suitable for proton dose calculations. CBCTs, planning CTs and repeated CTs (rCT) from 33 H&N cancer patients were used to compare a deep convolutional neural network (DCNN), deformable image registration (DIR) and an analytical image-based correction method (AIC) for synthetic CT (sCT) generation. Image quality of sCTs was evaluated by comparison with a same-day rCT, using mean absolute error (MAE), mean error (ME), Dice similarity coefficient (DSC), structural non-uniformity (SNU) and signal/contrast-to-noise ratios (SNR/CNR) as metrics. Dosimetric accuracy was investigated in an intracranial setting by performing gamma analysis and calculating range shifts. Neural network-based sCTs resulted in the lowest MAE and ME (37/2 HU) and the highest DSC (0.96). While DIR and AIC generated images with a MAE of 44/77 HU, a ME of -8/1 HU and a DSC of 0.94/0.90. Gamma and range shift analysis showed almost no dosimetric difference between DCNN and DIR based sCTs. The lower image quality of AIC based sCTs affected dosimetric accuracy and resulted in lower pass ratios and higher range shifts. Patient-specific differences highlighted the advantages and disadvantages of each method. For the set of patients, the DCNN created synthetic CTs with the highest image quality. Accurate proton dose calculations were achieved by both DCNN and DIR based sCTs. The AIC method resulted in lower image quality and dose calculation accuracy was reduced compared to the other methods

A Distributed GPU-based Framework for real-time 3D Volume Rendering of Large Astronomical Data Cubes

We present a framework to interactively volume-render three-dimensional data cubes using distributed ray-casting and volume bricking over a cluster of workstations powered by one or more graphics processing units (GPUs) and a multi-core CPU. The main design target for this framework is to provide an in-core visualization solution able to provide three-dimensional interactive views of terabyte-sized data cubes. We tested the presented framework using a computing cluster comprising 64 nodes with a total of 128 GPUs. The framework proved to be scalable to render a 204 GB data cube with an average of 30 frames per second. Our performance analyses also compare between using NVIDIA Tesla 1060 and 2050 GPU architectures and the effect of increasing the visualization output resolution on the rendering performance. Although our initial focus, and the examples presented in this work, is volume rendering of spectral data cubes from radio astronomy, we contend that our approach has applicability to other disciplines where close to real-time volume rendering of terabyte-order 3D data sets is a requirement.Comment: 13 Pages, 7 figures, has been accepted for publication in Publications of the Astronomical Society of Australi

Analysis of multi-layer ERBS spectra

A systematic way of analysis of multi-layer electron Rutherford backscattering spectra is described. The approach uses fitting in terms of physical meaningful parameters. Simultaneous analysis then becomes possible for spectra taken at different incoming energies and measurement geometries. Examples are given to demonstrate the level of detail that can be resolved by this technique.This work was realized with support from CNPq, Conselho Nacional de Desenvolvimento Científico e Tecnológico – Brazil and the Australian Research Council

Fast and Accurate Ray-Voxel Intersection Techniques for Iso-Surface Ray Tracing

A new intersection algorithm for iso-surface volume ray tracing increases performance by roughly a factor of three compared to previous accurate intersection algorithms while offering similar performance as previous algorithms that only approximated the solution. The image shows some of the test data sets: Bonsai tree, aneurism, engine, and skull. Visualizing iso-surfaces of volumetric data sets is becoming increasingly important for many practical applications. One crucial task in iso-surface ray tracing is to find the correct intersection of a ray with the trilinear-interpolated implicit surface defined by the data values at the vertices of a given voxel. Currently available solutions are either accurate but slow or they provide fast but only approximate solutions. In this paper, we analyze the available techniques and present a new intersection algorithm. We compare and evaluate the new algorithm against previous approaches using both synthetic test cases and real world data sets. The new algorithm is roughly three times faster but provides the same image quality and better numerical stability as previous accurate solutions.