3D mesh refinement procedure using the bisection and rivara algorithms with mesh quality assessment

Mesh refinement procedures for the solution of three dimensional problems are described. The computational\ud domain is represented by an assembly of tetrahedral elements and the mesh refinement is acheived by the bisection\ud and Rivara methods using an explicit mesh density function coupled with an automatic 3D mesh generator.\ud A couple of benchmark examples is used to compare the performance of both refinement methods in terms of mesh\ud and size qualities, number of generated elements and CPU time consume

Empowering Latino parents to transform the education of their children

This article emphasizes the role of parental involvement in the college preparation of Latino elementary and secondary school students. Although literature shows that education is highly valued in Latino families, actual college enrollment rates for Latino youth are below average. This has been attributed to barriers including lack of financial resources, problems in communication with schools, and low familiarity with the college planning process. The American Dream Academy is a university outreach program that is designed to help Latino families overcome these barriers. We conducted a qualitative analysis of speeches that were prepared and delivered by parents at graduation ceremonies of the program from 2007 to 2009. Our analysis revealed six themes: facing challenges, envisioning success, understanding the school system, taking ownership, community raising a child, and creating a supportive home environment. The findings enrich existing literature and help understand the complex systems that are at play with parental involvement in Latino families

Ricci flow for homogeneous compact models of the universe

Using quaternions, we give a concise derivation of the Ricci tensor for homogeneous spaces with topology of the 3-dimensional sphere. We derive explicit and numerical solutions for the Ricci flow PDE and discuss their properties. In the collapse (or expansion) of these models, the interplay of the various components of the Ricci tensor are studied. We dedicate this paper to honor the work of Josh Goldberg.Comment: 18 pages, 2 figure

Technical Note:First report on an in vivo range probing quality control procedure for scanned proton beam therapy in head and neck cancer patients

PURPOSE: The capability of proton therapy to provide highly conformal dose distributions is impaired by range uncertainties. The aim of this work is to apply range probing (RP), a form of a proton radiography-based quality control (QC) procedure for range accuracy assessment in head and neck cancer (HNC) patients in a clinical setting. METHODS AND MATERIALS: This study included seven HNC patients. RP acquisition was performed using a multi-layer ionization chamber (MLIC). Per patient, two RP frames were acquired within the first two weeks of treatment, on days when a repeated CT scan was obtained. Per RP frame, integral depth dose (IDD) curves of 81 spots around the treatment isocentre were acquired. Range errors are determined as a discrepancy between calculated IDDs in the treatment planning system and measured residual ranges by the MLIC. Range errors are presented relative to the water equivalent path length of individual proton spots. In addition to reporting results for complete measurement frames, an analysis, excluding range error contributions due to anatomical changes, is presented. RESULTS: Discrepancies between measured and calculated ranges are smaller when performing RP calculations on the day-specific patient anatomy rather than the planning CT. The patient-specific range evaluation shows an agreement between calculated and measured ranges for spots in anatomically consistent areas within 3% (1.5 standard deviation). CONCLUSIONS: The results of a RP-based QC procedure implemented in the clinical practice for HNC patients have been demonstrated. The agreement of measured and simulated proton ranges confirms the 3% uncertainty margin for robust optimization. Anatomical variations show a predominant effect on range accuracy, motivating efforts towards the implementation of adaptive radiotherapy

Deep learning prediction of proton and photon dose distributions for paediatric abdominal tumours

OBJECTIVE: Dose prediction using deep-learning networks prior to radiotherapy might lead to more efficient modality selections. The study goal was to predict proton and photon dose distributions based on the patient-specific anatomy and to assess their clinical usage for paediatric abdominal tumours. MATERIAL &METHODS: Data from 80 patients with neuroblastoma or Wilms' tumour was included. Pencil beam scanning (PBS) (5mm/3%) and volumetric-modulated arc therapy (VMAT) plans (5mm) were robustly optimized on the internal target volume (ITV). Separate 3-dimensional patch-based U-net networks were trained to predict PBS and VMAT dose distributions. Doses, planning-computed tomography images and relevant optimization masks (ITV, vertebra and organs-at-risk) of 60 patients were used for training with a 5-fold cross validation. The networks' performance was evaluated by computing the relative error between planned and predicted dose-volume histogram (DVH) parameters for 20 inference patients. In addition, the organs-at-risk mean dose difference between modalities was calculated using planned and predicted dose distributions (ΔDmean= DVMAT-DPBS). Two radiation oncologists performed a blind PBS/VMAT modality selection based on either planned or predicted ΔDmean. RESULTS: Average DVH differences between planned and predicted dose distributions were ≤|6%|for both modalities. The networks classified the organs-at-risk difference as a gain (ΔDmean>0) with 98% precision. An identical modality selection based on planned compared to predicted ΔDmean was made for 18/20 patients. CONCLUSION: Deep-learning networks for accurate prediction of proton and photon dose distributions for abdominal paediatric tumours were established. These networks allowing fast dose visualization might aid in identifying the optimal radiotherapy technique when experience and/or resources are unavailable

Range probing as a quality control tool for CBCT-based synthetic CTs:In vivo application for head and neck cancer patients

PURPOSE: Cone‐beam CT (CBCT)‐based synthetic CTs (sCT) produced with a deep convolutional neural network (DCNN) show high image quality, suggesting their potential usability in adaptive proton therapy workflows. However, the nature of such workflows involving DCNNs prevents the user from having direct control over their output. Therefore, quality control (QC) tools that monitor the sCTs and detect failures or outliers in the generated images are needed. This work evaluates the potential of using a range‐probing (RP)‐based QC tool to verify sCTs generated by a DCNN. Such a RP QC tool experimentally assesses the CT number accuracy in sCTs. METHODS: A RP QC dataset consisting of repeat CTs (rCT), CBCTs, and RP acquisitions of seven head and neck cancer patients was retrospectively assessed. CBCT‐based sCTs were generated using a DCNN. The CT number accuracy in the sCTs was evaluated by computing relative range errors between measured RP fields and RP field simulations based on rCT and sCT images. RESULTS: Mean relative range errors showed agreement between measured and simulated RP fields, ranging from −1.2% to 1.5% in rCTs, and from −0.7% to 2.7% in sCTs. CONCLUSIONS: The agreement between measured and simulated RP fields suggests the suitability of sCTs for proton dose calculations. This outcome brings sCTs generated by DCNNs closer toward clinical implementation within adaptive proton therapy treatment workflows. The proposed RP QC tool allows for CT number accuracy assessment in sCTs and can provide means of in vivo range verification

Direct measurement of strontium-90 and uranium-238 in soils on a real-time basis: 1994 summary report

Traditional methodologies for quantitative characterization of radionuclide-contaminated soils over extended areas are often tedious, costly, and non-representative. A rapid characterization methodology was designed that provides reliable output with spatial resolution on the order of a few meters or less. It incorporates an innovative sensor of square plastic scintillating fibers that has been designed to be placed directly on or above a contaminated soil to detect and quantify high-energy beta particles associated with the decay chains of uranium and/or strontium. Under the direction and auspices of the DOE`s Characterization, Monitoring, and Sensor Technology Integrated Program, Pacific Northwest Laboratory (PNL) constructed a high-energy beta scintillation sensor that was optimized for the detection and quantification of uranium and strontium contamination in surface soils (in the presence of potentially interfering natural and anthropogenic radionuclides), demonstrated and evaluated this detector in various field and laboratory scenarios, and provides this document in completion of the aforementioned requirements

Direct measurement of strontium-90 and uranium-238 in soils on a real-time basis: 1994 summary report

Traditional methodologies for quantitative characterization of radionuclide-contaminated soils over extended areas are often tedious, costly, and non-representative. A rapid characterization methodology was designed that provides reliable output with spatial resolution on the order of a few meters or less. It incorporates an innovative sensor of square plastic scintillating fibers that has been designed to be placed directly on or above a contaminated soil to detect and quantify high-energy beta particles associated with the decay chains of uranium and/or strontium. Under the direction and auspices of the DOE`s Characterization, Monitoring, and Sensor Technology Integrated Program, Pacific Northwest Laboratory (PNL) constructed a high-energy beta scintillation sensor that was optimized for the detection and quantification of uranium and strontium contamination in surface soils (in the presence of potentially interfering natural and anthropogenic radionuclides), demonstrated and evaluated this detector in various field and laboratory scenarios, and provides this document in completion of the aforementioned requirements

Conservation of grassland birds in North America: understanding ecological processes in different regions

Many species of birds that depend on grassland or savanna habitats have shown substantial overall population declines in North America. To understand the causes of these declines, we examined the habitat requirements of birds in six types of grassland in different regions of the continent. Open habitats were originally maintained by ecological drivers (continual and pervasive ecological processes) such as drought, grazing, and fire in tallgrass prairie, mixed-grass prairie, shortgrass prairie, desert grassland, and longleaf pine savanna. By contrast, grasslands were created by occasional disturbances (e.g., fires or beaver [Castor canadensis] activity) in much of northeastern North America. The relative importance of particular drivers or disturbances differed among regions. Keystone mammal species grazers such as prairie-dogs (Cynomys spp.) and bison (Bison bison) in western prairies, and dam-building beavers in eastern regions of the continent. Deciduous forests played a crucial, and frequently unappreciated, role in maintaining many grassland systems. Although fire was important in preventing invasion of woody plants in the tallgrass and moist mixed prairies, grazing played a more important role in maintaining the typical grassland vegetation of shortgrass prairies and desert grasslands. Heavy grazing by prairiedogs or bison created a low \u27grazing lawn\u27 that is the preferred habitat for many grassland bird species that are restricted to the shortgrass prairie and desert grasslands. Ultimately, many species of grassland birds are vulnerable because people destroyed their breeding, migratory, and wintering habitat, either directly by converting it to farmland and building lots, or indirectly by modifying grazing patterns, suppressing fires, or interfering with other ecological processes that originally sustained open grassland. Understanding the ecological processes that originally maintained grassland systems is critically important for efforts to improve, restore, or create habitat for grassland birds and other grassland organisms. Consequently, preservation of large areas of natural or seminatural grassland, where these processes can be studied and core populations of grassland birds can flourish, should be a high priority. However, some grassland birds now primarily depend on artificial habitats that are managed to maximize production of livestock, timber, or other products. With a sound understanding of the habitat requirements of grassland birds and the processes that originally shaped their habitats, it should be possible to manage populations sustainably on \u27working land\u27 such as cattle ranches, farms, and pine plantations. Proper management of private land will be critical for preserving adequate breeding, migratory, and winter habitat for grassland and savanna species

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