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Cone-beam computed tomography (CBCT)- and magnetic resonance (MR)-images allow a daily observation of patient anatomy but are not directly suited for accurate proton dose calculations. This can be overcome by creating synthetic CTs (sCT) using deep convolutional neural networks. In this study, we compared sCTs based on CBCTs and MRs for head and neck (H&N) cancer patients in terms of image quality and proton dose calculation accuracy. A dataset of 27 H&N-patients, treated with proton therapy (PT), containing planning CTs (pCTs), repeat CTs, CBCTs and MRs were used to train two neural networks to convert either CBCTs or MRs into sCTs. Image quality was quantified by calculating mean absolute error (MAE), mean error (ME) and Dice similarity coefficient (DSC) for bones. The dose evaluation consisted of a systematic non-clinical analysis and a clinical recalculation of actually used proton treatment plans. Gamma analysis was performed for non-clinical and clinical treatment plans. For clinical treatment plans also dose to targets and organs at risk (OARs) and normal tissue complication probabilities (NTCP) were compared. CBCT-based sCTs resulted in higher image quality with an average MAE of 40 ± 4 HU and a DSC of 0.95, while for MR-based sCTs a MAE of 65 ± 4 HU and a DSC of 0.89 was observed. Also in clinical proton dose calculations, sCTCBCT achieved higher average gamma pass ratios (2%/2 mm criteria) than sCTMR (96.1% vs. 93.3%). Dose-volume histograms for selected OARs and NTCP-values showed a very small difference between sCTCBCT and sCTMR and a high agreement with the reference pCT. CBCT- and MR-based sCTs have the potential to enable accurate proton dose calculations valuable for daily adaptive PT. Significant image quality differences were observed but did not affect proton dose calculation accuracy in a similar manner. Especially the recalculation of clinical treatment plans showed high agreement with the pCT for both sCTCBCT and sCTMR.

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.

Objective: 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. Approach: 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. Main results: Large mean and standard deviations were found in WEPL images obtained with large ΔE values (ΔE = 9 or 7 MeV), for any ΔX. WEPL images obtained with ΔE ≤ 5 MeV and ΔX ≤ 5 mm resulted in a WEPL accuracy with mean values within ±0.5% and standard deviations around 1%. Significance: An optimal FP calibration in the framework of this study was established, characterized by 3 MeV ≤ ΔE ≤ 5 MeV and 2 mm ≤ ΔX ≤ 5 mm. 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.

PURPOSE: Adaptive proton therapy (APT) of lung cancer patients requires frequent volumetric imaging of diagnostic quality. Cone-beam CT (CBCT) can provide these daily images, but x-ray scattering limits CBCT-image quality and hampers dose calculation accuracy. The purpose of this study was to generate CBCT-based synthetic CTs using a deep convolutional neural network (DCNN) and investigate image quality and clinical suitability for proton dose calculations in lung cancer patients.METHODS: A dataset of 33 thoracic cancer patients, containing CBCTs, same-day repeat CTs (rCT), planning-CTs (pCTs) and clinical proton treatment plans, was used to train and evaluate a DCNN with and without a pCT-based correction method. Mean absolute error (MAE), mean error (ME), peak signal-to-noise ratio and structural similarity were used to quantify image quality. The evaluation of clinical suitability was based on recalculation of clinical proton treatment plans. Gamma pass ratios, mean dose to target volumes and organs at risk, and normal tissue complication probabilities (NTCP) were calculated. Furthermore, proton radiography simulations were performed to assess the HU-accuracy of sCTs in terms of range errors.RESULTS: On average, sCTs without correction resulted in a MAE of 34±6 HU and ME of 4±8 HU. The correction reduced the MAE to 31±4HU (ME to 2±4HU). Average 3%/3mm gamma pass ratios increased from 93.7% to 96.8%, when the correction was applied. The patient specific correction reduced mean proton range errors from 1.5 to 1.1 mm. Relative mean target dose differences between sCTs and rCT were below ±0.5% for all patients and both synthetic CTs (with/without correction). NTCP values showed high agreement between sCTs and rCT (CONCLUSION: CBCT-based sCTs can enable accurate proton dose calculations for APT of lung cancer patients. The patient specific correction method increased the image quality and dosimetric accuracy but had only a limited influence on clinically relevant parameters. This article is protected by copyright. All rights reserved.

We have developed a methodology that analyzes the dimensions and conformal doping profiles in fin field effect transistors (FinFET) using time-of-flight medium energy ion scattering (TOF-MEIS). The structure of a 3D FinFET and As dopant profiles were determined by comprehensive simulations of TOF-MEIS measurements made in three different scattering geometries. The width and height of a FinFET and the As doping profiles in the top, side, and bottom of fin were analyzed simultaneously. The results showed the dimension and conformal doping profile of nanostructures with complex shape can be determined by TOF-MEIS nondestructively, quantitatively, and with subnm depth resolution without any sputtering and matrix effects.

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).

It is well established that the shape of spectra acquired using reflection electron energy loss spectroscopy (REELS) is determined by the dielectric function. Extracting the dielectric function from REELS spectra is a real challenge as it is governed by strong multiple scattering. It is generally assumed that the contribution of shallow core levels (with binding energies over 100 eV) to the REELS data can be neglected when one interprets valence band REELS data that usually extends to, at most, 100 eV energy loss. Here we show that, especially when incoming energies over 1 keV are used, this is not the case and that the intensity of the REELS spectrum can only be calculated correctly if the shallow core levels are taken into account. The implications for this for the extraction of the dielectric function from REELS data is discussed.

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 × 1 mm2), 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.

This study evaluates the suitability of convolutional neural networks (CNNs) 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 26 × 26cm2 for field A and 4.5 × 4.5cm2 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 three 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.

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.

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.

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.

The relative biological effectiveness (RBE) of protons is highly variable and difficult to quantify. However, RBE is related to the local ionization density, which can be related to the physical measurable dose weighted linear energy transfer (LETD). The aim of this study was to validate the LETD calculations for proton therapy beams implemented in a commercially available treatment planning system (TPS) using microdosimetry measurements and independent LETD calculations (Open-MCsquare (MCS)). The TPS (RayStation v6R) was used to generate treatment plans on the CIRS-731-HN anthropomorphic phantom for three anatomical sites (brain, nasopharynx, neck) for a spherical target ( = 5 cm) with uniform target dose to calculate the LETD distribution. Measurements were performed at the University Medical Center Groningen proton therapy center (Proteus Plus, IBA) using a µ +-probe utilizing silicon on insulator microdosimeters capable of detecting lineal energies as low as 0.15 keV µm-1 in tissue. Dose averaged mean lineal energy depth-profiles were measured for 70 and 130 MeV spots in water and for the three treatment plans in water and an anthropomorphic phantom. The measurements were compared to the LETD calculated in the TPS and MCS independent dose calculation engine. D • was compared to D • LETD in terms of a gamma-index with a distance-to-agreement criteria of 2 mm and increasing dose difference criteria to determine the criteria for which a 90% pass rate was accomplished. Measurements of D • were in good agreement with the D • LETD calculated in the TPS and MCS. The 90% passing rate threshold was reached at different D • LETD difference criteria for single spots (TPS: 1% MCS: 1%), treatment plans in water (TPS: 3% MCS: 6%) and treatment plans in an anthropomorphic phantom (TPS: 6% MCS: 1%). We conclude that D • LETD calculations accuracy in the RayStation TPS and open MCSquare are within 6%, and sufficient for clinical D • LETD evaluation and optimization. These findings remove an important obstacle in the road towards clinical implementation of D • LETD evaluation and optimization of proton therapy treatment plans. Novelty and significance The dose weighed linear energy transfer (LETD) distribution can be calculated for proton therapy treatment plans by Monte Carlo dose engines. The relative biological effectiveness (RBE) of protons is known to vary with the LETD distribution. Therefore, there exists a need for accurate calculation of clinical LETD distributions. Previous LETD validations have focused on general purpose Monte Carlo dose engines which are typically not used clinically. We present the first validation of mean lineal energy measurements of the LETD against calculations by the Monte Carlo dose engines of the Raystation treatment planning system and open MCSquare.

Arsenic profiles in plasma doped silicon wafers were traced by scattering of H+ and H-2(+) ions at medium energies. Two wafers were doped with the same bias, gas pressure, total implanted dose and AsH3 concentration. After implantation, the wafers were submitted to industrial cleaning processes, resulting in the formation of a surface SiO2 layer, and one wafer was subjected to an additional thermal treatment. Scattering spectra of single and molecular ion beams with the same energy per nucleon and charge state differed only by the energy broadening due to the break-up of the molecule, allowing depth profiling by calculation of the dwell time before the backscattering collision. For the SiO2 layers of these samples a density reduction of, on average, 13% was observed, compared to thermally grown SiO2. In addition, the arsenic depth-profile determined were in close agreement with independent findings obtained by electron techniques.

The diffusivity of oxygen in thin, sputter-deposited TiO 2 films, as can be used in RRAMs, is measured using electron and ion backscattering techniques. The as-grown sample consisted of two layers (Ti 16 O 2 and Ti 18 O 2 ) and was annealed between 500 °C and 900 °C. The depth profiles of 18 O, as measured with both techniques, were similar. The extent of diffusion was much larger than expected from the literature data for O diffusion in single-crystal rutile, suggesting that defects in the sputter-deposited film play an essential role in the diffusion process.

International audience; Sputtering experiments with swift heavy ions in the electronic energy loss regime were performed by using the catcher technique in combination with elastic recoil detection analysis. The angular distribution of particles sputtered from the surface of LiF single crystals is composed of a jet-like peak superimposed on a broad isotropic distribution. By using incident ions of fixed energy but different charges states, the influence of the electronic energy loss on both components is probed. We find indications that isotropic sputtering originates from near-surface layers, whereas the jet component may be affected by contributions from depth up to about 150 nm.

Non-planar Fin Field Effect Transistors (FinFET) are already present in modern devices. The evolution from the well-established 2D planar technology to the design of 3D nanostructures rose new fabrication processes, but a technique capable of full characterization, particularly their dopant distribution, in a representative (high statistics) way is still lacking. Here we propose a methodology based on Medium Energy Ion Scattering (MEIS) to address this query, allowing structural and compositional quantification of advanced 3D FinFET devices with nanometer spatial resolution. When ions are backscattered, their energy losses unfold the chemistry of the different 3D compounds present in the structure. The FinFET periodicity generates oscillatory features as a function of backscattered ion energy and, in fact, these features allow a complete description of the device dimensions. Additionally, each measurement is performed over more than thousand structures, being highly representative in a statistical meaning. Finally, independent measurements using electron microscopy corroborate the proposed methodology.

International audience; Abstract Damaged regions of cylindrical shapes called ion tracks, typically in nano-meters wide and tens micro-meters long, are formed along the ion trajectories in many insulators, when high energy ions in the electronic stopping regime are injected. In most cases, the ion tracks were assumed as consequences of dense electronic energy deposition from the high energy ions, except some cases where the synergy effect with the nuclear energy deposition plays an important role. In crystalline Si (c-Si), no tracks have been observed with any monomer ions up to GeV. Tracks are formed in c-Si under 40 MeV fullerene (C 60 ) cluster ion irradiation, which provides much higher energy deposition than monomer ions. The track diameter decreases with decreasing the ion energy until they disappear at an extrapolated value of ~ 17 MeV. However, here we report the track formation of 10 nm in diameter under C 60 ion irradiation of 6 MeV, i.e., much lower than the extrapolated threshold. The diameters of 10 nm were comparable to those under 40 MeV C 60 irradiation. Furthermore, the tracks formed by 6 MeV C 60 irradiation consisted of damaged crystalline, while those formed by 40 MeV C 60 irradiation were amorphous. The track formation was observed down to 1 MeV and probably lower with decreasing the track diameters. The track lengths were much shorter than those expected from the drop of S e below the threshold. These track formations at such low energies cannot be explained by the conventional purely electronic energy deposition mechanism, indicating another origin, e.g., the synergy effect between the electronic and nuclear energy depositions, or dual transitions of transient melting and boiling.

Electron Rutherford backscattering measures the near-surface composition of samples quantitatively. For interpretation one usually relies on the single-scattering approximation. Here we present results for four compounds, containing oxygen and other species, varying from very light to very heavy. Two Monte Carlo codes are described that model these measurements. From these simulations it is clear that for all samples multiple scattering occurs frequently, but also that the single scattering interpretation deduces the right composition, except for O atoms in a very heavy matrix, where interpretation is more difficult. The intrinsic width of the peaks, a consequence of Doppler broadening due to the velocity of the (vibrating) atoms, turns out to be more sensitive to multiple scattering effects.

Gold-implanted aluminum films are used to investigate how reflection electron energy loss spectra (REELS) change due to the presence of a small concentration of heavy atoms at a specific depth. Au ions were implanted with 30, 100 and 300 keV energy. REELS spectra were taken at energies between 10 and 40 keV. Large changes in the REELS spectra are observed after Au implantation, but the nature of the change indicates that they are not due to modification of the dielectric function of the implanted layer, but should be interpreted as changes in the partial intensities that make up the spectrum. Two models are used to describe the results quantitatively. One method assumes v-shaped trajectories (i.e. only a single elastic deflection) and the REELS spectrum can then be calculated in a closed form. The other method is a Monte-Carlo based simulation which allows for multiple elastic deflections. Both methods describe the experimental spectra quite well, but at larger energy losses significant deviations occur between the measured and calculated intensity for both the implanted and not-implanted films. The difference in the REELS spectrum before and after implantation is less affected by these discrepancies, and can be used to obtain an estimate of both the depth and concentration of the implanted Au atoms. Due to the presence of sharp plasmon features in the energy loss spectrum of aluminum the experiment can tell us directly which partial intensities are affected by the Au impurities, as the recoil energies due to elastic scattering make it possible to identify the contribution of Au to the first few plasmons. As the Au implantation fluence is known the measurement can be used to determine the ratio of the Au and Al elastic scattering cross sections, which deviates strongly from that calculated from the Rutherford formula.

Background and purpose Motion-induced uncertainties hamper the clinical implementation of pencil beam scanning proton therapy (PBS-PT). Prospective pretreatment evaluations only provide multiscenario predictions without giving a clear conclusion for the actual treatment. Therefore, in this proof-of-concept study we present a methodology for a fraction-wise retrospective four-dimensional (4D) dose reconstruction and accumulation aiming at the evaluation of treatment quality during and after treatment. Material and methods We implemented an easy-to-use, script-based 4D dose assessment of PBS-PT for patients with moving tumors in a commercially available treatment planning system. This 4D dose accumulation uses treatment delivery log files and breathing pattern records of each fraction as well as weekly repeated 4D-CT scans acquired during the treatment course. The approach was validated experimentally and was executed for an exemplary dataset of a lung cancer patient. Results The script-based 4D dose reconstruction and accumulation was implemented successfully, requiring minimal user input and a reasonable processing time (around 10 min for a fraction dose assessment). An experimental validation using a dynamic CIRS thorax phantom confirmed the precision of the 4D dose reconstruction methodology. In a proof-of-concept study, the accumulation of 33 reconstructed fraction doses showed a linear increase of D98 values. Projected treatment course D98 values revealed a CTV underdosage after fraction 25. This loss of target coverage was confirmed in a dose volume histogram comparison of the nominal, the projected (after 16 fractions) and the accumulated (after 33 fractions) dose distribution. Conclusions The presented method allows for the assessment of the conformity between planned and delivered dose as the treatment course progresses. The implemented approach considers the influence of changing patient anatomy and variations in the breathing pattern. This facilitates treatment quality evaluation and supports decisions regarding plan adaptation. In a next step, this approach will be applied to a larger patient cohort to investigate its capability as 4D quality control and decision support tool for treatment adaptation. (c) 2019 American Association of Physicists in Medicine

This work was realized with support from CNPq, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico – Brazil and the Australian Research Council.

In this work we demonstrate that Medium Energy Ion Scattering (MEIS) measurements in combination with Transmission Electron Microscopy (TEM) or Grazing Incidence Small Angle X-Ray Scattering (GISAXS) can provide a complete characterization of nanoparticle (NP) systems embedded into dielectric films. This includes the determination of the nanoparticle characteristics (location, size distribution and number concentration) as well as the depth distribution and concentration of the NP atomic components dispersed in the matrix. Our studies are performed considering a model case system consisting of planar arrangements of Au NPs (size range from 1 to 10 nm) containing three distinct Au concentrations embedded in a SiO2 film.

Reflection electron energy loss spectra from some insulating materials (CaCO3, Li2CO3, and SiO2) taken at relatively high incoming electron energies (5-40 keV) are analyzed. Here, one is bulk sensitive and a well-defined onset of inelastic excitations is observed from which one can infer the value of the band gap. An estimate of the band gap was obtained by fitting the spectra with a procedure that includes the recoil shift and recoil broadening affecting these measurements. The width of the elastic peak is directly connected to the mean kinetic energy of the atom in the material (Doppler broadening). The experimentally obtained mean kinetic energies of the O, C, Li, Ca, and Si atoms are compared with the calculated ones, and good agreement is found, especially if the effect of multiple scattering is taken into account. It is demonstrated experimentally that the onset of the inelastic excitation is also affected by Doppler broadening. Aided by this understanding, we can obtain a good fit of the elastic peak and the onset of inelastic excitations. For SiO2, good agreement is obtained with the well-established value of the band gap (8.9 eV) only if it is assumed that the intensity near the edge scales as (E - Egap)(1.5). For CaCO3, the band gap obtained here (7 eV) is about 1 eV larger than the previous experimental value, whereas the value for Li2CO3 (7.5 eV) is the first experimental estimate.