Metal oxides of resistive memories investigated by electron and ion backscattering

The memristor is one of the most promising devices being studied for multiple uses in future electronic systems, with applications ranging from nonvolatile memories to artificial neural networks. Its working is based on the forming and rupturing of nano-scaled conductive filaments, which drastically alters the device’s resistance. These filaments are formed by oxygen vacancy accumulation, hence a deep understanding of the self-diffusion of oxygen in these systems is necessary. Accurate measurements of oxygen self-diffusion on metal oxides was achieved with the development of a quantitative analysis of the energy spectrum of the backscattering of electrons. The novel technique called Electron Rutherford Backscattering Spectroscopy (ERBS) uses the scattering of high energy electrons ( 40 keV) to probe the sample’s near surface (10–100 nm). Measurements of the high energy loss region – called Reflection High-Energy Electron Loss Spectroscopy (RHEELS) – also exhibit characteristics of the material’s electronic structure. A careful procedure was developed for the fitting of ERBS spectra, which was then applied on the analysis of multi-layered samples of Si3N4/TiO2, and measurements of the band gap of common oxides, such as SiO2, CaCO3 and Li2CO3. Monte Carlo simulations were employed to study the effects of multiple elastic scatterings in ERBS spectra, and a dielectric function description of inelastic scatterings extended the simulation to also consider the plasmon excitation peaks observed in RHEELS. These analysis tools were integrated into a package named PowerInteraction. With its use, a series of measurements of oxygen self-diffusion in TiO2 were conducted. The samples were composed of two sputtered deposited TiO2 layers, one of which was enriched with the 18 mass oxygen isotope. After thermal annealing, diffusion profiles were obtained by tracking the relative concentration of oxygen isotopes in both films. From the logarithmic temperature dependence of the diffusion coefficients, an activation energy of 1.05 eV for oxygen self-diffusion in TiO2 was obtained. Common ion beam analysis, such as RBS and NRA/NRP (Nuclear Reaction Analysis/Profiling), were also used to provide complementary information.O memristor é um dos dispositivos mais promissores sendo estudados para múltiplos usos em sistemas eletrônicos, com aplicações desde memórias não voláteis a redes neurais artificiais. Seu funcionamento é baseado na formação e ruptura de filamentos condutores nanométricos, o que altera drasticamente a resistência do dispositivo. Estes filamentos são formados pela acumulação de vacâncias de oxigênio, portanto um profundo entendimento da autodifusão de oxigênio nestes sistemas é necessário. Medidas acuradas da difusão em óxidos metálicos foi obtida com o desenvolvimento de uma análise quantitativa do espectro em energia de elétrons retroespalhados. A inovadora técnica de RBS de elétrons (ERBS) utiliza elétrons de alta energia ( 40 keV) para investigar a região próxima a superfície (10–100 nm). Medidas da região de alta perda de energia – chamada de Spectroscopia de Perda de Alta-Energia de Elétrons Refletidos (RHEELS) – também exibe características da estrutura eletrônica dos materiais. Um procedimento cuidadoso para o ajuste de espectros de ERBS foi desenvolvido, e então aplicado na análise de amostras multi camada de Si3N4/TiO2, e medidas de band gap de alguns óxidos, como SiO2, CaCO3 e Li2CO3. Simulações de Monte Carlo foram empregadas no estudo dos efeitos de espalhamento múltiplo nos espectros de ERBS, e uma descrição dielétrica dos espalhamentos inelásticos extendeu as simulação para também considerarem os picos de exitação plasmônica observados em RHEELS. Estas ferramentas de análise foram integradas em um pacote chamado PowerInteraction. Com o uso deste, uma série de medidas de autodifusão de oxigênio em TiO2 foram conduzidas. As amostras eram compostas por dois filmes de TiO2 depositados por sputtering, um dos quais enriquecido com isótopo 18 de oxigênio. Após tratamentos térmicos, perfis de difusão foram obtidos pelo rastreio das concentrações relativas dos isótopos de oxigênio nos dois filmes. Do comportamento logarítmico dos coeficientes de difusão em relação à temperatura, uma energia de ativação de 1.05 eV para a autodifusão de oxigênio em TiO2 foi obtida. Análises por feixes de íons, como RBS e NRA/NRP (Análise/Perfilometria por Reação Nuclear), também forneceram informações complementares

Positioning system for radiotherapy

A system (1) for positioning a patient for radiotherapy in accordance with patient specific data including a first volumetric image (e.g. a pCT image) of the patient comprising tissue label data and dose specification data is provided that comprises an imaging device (2), a positioning device (3) and an optimization controller (4). The imaging device (2) is configured to provide a second volumetric image (e.g. an rCT image) of the patient including a designated part of the patient to be treated. The positioning device (3) is provided to hold the patient in a variable position and/or orientation in the beam of radiation for an accurate intervention to a designated part of the patient. The optimization controller (4) comprises a dose based control module configured to provide registration control data (ΔP) to guide the positioning device (3) so that the actually applied treatment dose optimally matches the planned treatment dose

Positioning system for radiotherapy

A system (1) for positioning a patient for radiotherapy in accordance with patient specific data including a first volumetric image (e.g. a pCT image) of the patient comprising tissue label data and dose specification data is provided that comprises an imaging device (2), a positioning device (3) and an optimization controller (4). The imaging device (2) is configured to provide a second volumetric image (e.g. an rCT image) of the patient including a designated part of the patient to be treated. The positioning device (3) is provided to hold the patient in a variable position and/or orientation in the beam of radiation for an accurate intervention to a designated part of the patient. The optimization controller (4) comprises a dose based control module configured to provide registration control data (ΔP) to guide the positioning device (3) so that the actually applied treatment dose optimally matches the planned treatment dose

Positioning system for radiotherapy

A system (1) for positioning a patient for radiotherapy in accordance with patient specific data including a first volumetric image (e.g. a pCT image) of the patient comprising tissue label data and dose specification data is provided that comprises an imaging device (2), a positioning device (3) and an optimization controller (4). The imaging device (2) is configured to provide a second volumetric image (e.g. an rCT image) of the patient including a designated part of the patient to be treated. The positioning device (3) is provided to hold the patient in a variable position and/or orientation in the beam of radiation for an accurate intervention to a designated part of the patient. The optimization controller (4) comprises a dose based control module configured to provide registration control data (ΔP) to guide the positioning device (3) so that the actually applied treatment dose optimally matches the planned treatment dose

Positioning system for radiotherapy

A system (1) for positioning a patient for radiotherapy in accordance with patient specific data including a first volumetric image (e.g. a pCT image) of the patient comprising tissue label data and dose specification data is provided that comprises an imaging device (2), a positioning device (3) and an optimization controller (4). The imaging device (2) is configured to provide a second volumetric image (e.g. an rCT image) of the patient including a designated part of the patient to be treated. The positioning device (3) is provided to hold the patient in a variable position and/or orientation in the beam of radiation for an accurate intervention to a designated part of the patient. The optimization controller (4) comprises a dose based control module configured to provide registration control data (ΔP) to guide the positioning device (3) so that the actually applied treatment dose optimally matches the planned treatment dose

Positioning system for radiotherapy

A system (1) for positioning a patient for radiotherapy in accordance with patient specific data including a first volumetric image (e.g. a pCT image) of the patient comprising tissue label data and dose specification data is provided that comprises an imaging device (2), a positioning device (3) and an optimization controller (4). The imaging device (2) is configured to provide a second volumetric image (e.g. an rCT image) of the patient including a designated part of the patient to be treated. The positioning device (3) is provided to hold the patient in a variable position and/or orientation in the beam of radiation for an accurate intervention to a designated part of the patient. The optimization controller (4) comprises a dose based control module configured to provide registration control data (ΔP) to guide the positioning device (3) so that the actually applied treatment dose optimally matches the planned treatment dose

Positioning system for radiotherapy

A system (1) for positioning a patient for radiotherapy in accordance with patient specific data including a first volumetric image (e.g. a pCT image) of the patient comprising tissue label data and dose specification data is provided that comprises an imaging device (2), a positioning device (3) and an optimization controller (4). The imaging device (2) is configured to provide a second volumetric image (e.g. an rCT image) of the patient including a designated part of the patient to be treated. The positioning device (3) is provided to hold the patient in a variable position and/or orientation in the beam of radiation for an accurate intervention to a designated part of the patient. The optimization controller (4) comprises a dose based control module configured to provide registration control data (ΔP) to guide the positioning device (3) so that the actually applied treatment dose optimally matches the planned treatment dose

Evaluation of CBCT-based synthetic CTs for clinical adoption in proton therapy of head & neck patients.:E-Poster

PurposeIn adaptive proton therapy, weekly verification CTs (rCTs) are commonly acquired and used to monitor patient anatomy. Cone-Beam CTs (CBCT) on the other hand are used for daily pre-treatment position verification. These CBCT images however suffer from severe imaging artifacts preventing accurate proton dose calculations, meaning that CBCTs are unsuitable for treatment planning purposes. Recent advances in converting CBCT images to high quality synthetic CTs (sCTs) using Deep Convolution Neural Networks (DCNN) show that these sCTs can be suitable for proton dose calculations and therefore assist clinical adaptation decisions.The aim of this study was to compare weekly high definition rCTs to same-day sCT images of head and neck cancer patients in order to verify dosimetric accuracy of DCNN generated CBCT-based sCTs.Materials and MethodsA dataset of 46 previously treated head and neck cancer patients was used to generate synthetic CTs from daily pre-treatment patient alignment CBCTs using a previously developed and trained U-net like DCNN. Proton dose was then recalculated on weekly rCTs and same-day sCTs utilizing clinical treatment plans. To assess the dosimetric accuracy of sCTs, dose to the clinical target volumes (CTV D98) and mean dose in selected organs-at-risk (OAR; Oral cavity, Parotid gland left, Submandibular gland right) was calculated and compared between rCTs and same-day sCTs. Furthermore, Normal Tissue Complication Probability (NTCP) models for xerostomia and dysphagia were used to assess the clinical significance of dose differences.ResultsFor target volumes, the average difference in D98% between rCT and sCT pairs (N=284) was 0.34±3.86 % [-0.18±2.06 Gy] for the low dose CTV (54.25 Gy) and 0.23±3.62 % [-0.16±2.48 Gy] for the high dose CTV (70 Gy). For the OARs the following mean dose differences were observed; Oral Cavity: 4.15±9.78 % [0.75±1.39 Gy], Parotid L: 5.34±11.6 % [0.58±1.40 Gy], Submandibular R: 2.17±8.55 % [0.55±2.57 Gy]. The average NTCP difference was -0.15±0.58 % for grade 3 dysphagia, -0.26±0.54 % for grade 3 xerostomia, -0.53±1.20 % for grade 2 dysphagia and -0.71±1.40 % for grade 2 xerostomia. ConclusionFor target coverage and NTCP difference, the deep learning based sCTs showed high agreement with weekly verification CTs. However, some outliers were observed (also indicated by the increased standard deviation) and warrant further investigation and improvements before clinical implementation. Furthermore, stringent quality control tools for synthetic CTs are required to allow reliable deployment in adaptive proton therapy workflows.<br/

Optimizing calibration settings for accurate water equivalent path length assessment using flat panel proton radiography

Proton range uncertainties can compromise the effectiveness of proton therapy treatments. Water equivalent path length (WEPL) assessment by flat panel detector proton radiography (FP-PR) can provide means of range uncertainty detection. Since WEPL accuracy intrinsically relies on the FP-PR calibration parameters, the purpose of this study is to establish an optimal calibration procedure that ensures high accuracy of WEPL measurements. To that end, several calibration settings were investigated. FP-PR calibration datasets were obtained simulating PR fields with different proton energies, directed towards water-equivalent material slabs of increasing thickness. The parameters investigated were the spacing between energy layers (ΔE) and the increment in thickness of the water-equivalent material slabs (ΔX) used for calibration. 30 calibrations were simulated, as a result of combining ΔE=9, 7, 5, 3, 1 MeV and ΔX=10, 8, 5, 3, 2, 1 mm. FP-PRs through a CIRS electron density phantom were simulated, and WEPL images corresponding to each calibration were obtained. Ground truth WEPL values were provided by range probing multi-layer ionization chamber simulations on each insert of the phantom. Relative WEPL errors between FP-PR simulations and ground truth were calculated for each insert. Mean relative WEPL errors and standard deviations across all inserts were computed for WEPL images obtained with each calibration. Large mean and standard deviations were found in WEPL images obtained with large ΔE values (ΔE= 9 or 7MeV), for any ΔX. WEPL images obtained with ΔE≤ 5MeV and ΔX≤ 5mm resulted in a WEPL accuracy with mean values within ±0.5% and standard deviations around 1%. An optimal FP calibration in the framework of this study was established, characterized by 3MeV≤ ΔE ≤ 5MeV and 2mm ≤ ΔX ≤ 5mm. Within these boundaries, highly accurate WEPL acquisitions using FP-PR are feasible and practical, holding the potential to assist future online range verification quality control procedures

Feasibility of patient specific quality assurance for proton therapy based on independent dose calculation and predicted outcomes

PURPOSE: Patient specific quality assurance (PSQA) is required to verify the treatment delivery and the dose calculation by the treatment planning system (TPS). The objective of this work is to demonstrate the feasibility to substitute resource consuming measurement based PSQA (PSQAM) by independent dose recalculations (PSQAIDC), and that PSQAIDC results may be interpreted in a clinically relevant manner using normal tissue complication probability (NTCP) and tumor control probability (TCP) models. METHODS AND MATERIALS: A platform for the automatic execution of the two following PSQAIDC workflows was implemented: (i) using the TPS generated plan and (ii) using treatment delivery log files (log-plan). 30 head and neck cancer (HNC) patients were retrospectively investigated. PSQAM results were compared with those from the two PSQAIDC workflows. TCP / NTCP variations between PSQAIDC and the initial TPS dose distributions were investigated. Additionally, for two example patients that showed low passing PSQAM results, eight error scenarios were simulated and verified via measurements and log-plan based calculations. For all error scenarios ΔTCP / NTCP values between the nominal and the log-plan dose were assessed. RESULTS: Results of PSQAM and PSQAIDC from both implemented workflows agree within 2.7% in terms of gamma pass ratios. The verification of simulated error scenarios shows comparable trends between PSQAM and PSQAIDC. Based on the 30 investigated HNC patients, PSQAIDC observed dose deviations translate into a minor variation in NTCP values. As expected, TCP is critically related to observed dose deviations. CONCLUSIONS: We demonstrated a feasibility to substitute PSQAM with PSQAIDC. In addition, we showed that PSQAIDC results can be interpreted in clinically more relevant manner, for instance using TCP / NTCP