Toward Closing the Loop in Image-to-Image Conversion in Radiotherapy: A Quality Control Tool to Predict Synthetic Computed Tomography Hounsfield Unit Accuracy.

In recent years, synthetic Computed Tomography (CT) images generated from Magnetic Resonance (MR) or Cone Beam Computed Tomography (CBCT) acquisitions have been shown to be comparable to real CT images in terms of dose computation for radiotherapy simulation. However, until now, there has been no independent strategy to assess the quality of each synthetic image in the absence of ground truth. In this work, we propose a Deep Learning (DL)-based framework to predict the accuracy of synthetic CT in terms of Mean Absolute Error (MAE) without the need for a ground truth (GT).

Technical note: Flat panel proton radiography with a patient specific imaging field for accurate WEPL assessment.

Background: Proton radiography (PR) uses highly energetic proton beams to create images where energy loss is the main contrast mechanism. Water-equivalent path length (WEPL) measurements using flat panel PR (FP-PR) have potential for in vivo range verification. However, an accurate WEPL measurement via FP-PR requires irradiation with multiple energy layers, imposing high imaging doses.

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

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.

Comparison of the suitability of CBCT- and MR-based synthetic CTs for daily adaptive proton therapy in head and neck patients.

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.

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 (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).

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.

Validation of linear energy transfer computed in a Monte Carlo dose engine of a commercial treatment planning system.

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 (LET). The aim of this study was to validate the LET calculations for proton therapy beams implemented in a commercially available treatment planning system (TPS) using microdosimetry measurements and independent LET calculations (Open-MCsquare (MCS)).

Unraveling structural and compositional information in 3D FinFET electronic devices.

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.

Determination of Dimension and Conformal Arsenic Doping Profile of a Fin Field Effect Transistors by Time-of-Flight Medium Energy Ion Scattering.

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.

New approach for structural characterization of planar sets of nanoparticles embedded into a solid matrix.

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.