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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Digital Mapping and Environmental Characterization of National Wild and Scenic River Systems

Spatially accurate geospatial information is required to support decision-making regarding sustainable future hydropower development. Under a memorandum of understanding among several federal agencies, a pilot study was conducted to map a subset of National Wild and Scenic Rivers (WSRs) at a higher resolution and provide a consistent methodology for mapping WSRs across the United States and across agency jurisdictions. A subset of rivers (segments falling under the jurisdiction of the National Park Service) were mapped at a high resolution using the National Hydrography Dataset (NHD). The spatial extent and representation of river segments mapped at NHD scale were compared with the prevailing geospatial coverage mapped at a coarser scale. Accurately digitized river segments were linked to environmental attribution datasets housed within the Oak Ridge National Laboratory s National Hydropower Asset Assessment Program database to characterize the environmental context of WSR segments. The results suggest that both the spatial scale of hydrography datasets and the adherence to written policy descriptions are critical to accurately mapping WSRs. The environmental characterization provided information to deduce generalized trends in either the uniqueness or the commonness of environmental variables associated with WSRs. Although WSRs occur in a wide range of human-modified landscapes, environmental data layers suggest that they provide habitats important to terrestrial and aquatic organisms and recreation important to humans. Ultimately, the research findings herein suggest that there is a need for accurate, consistent, mapping of the National WSRs across the agencies responsible for administering each river. Geospatial applications examining potential landscape and energy development require accurate sources of information, such as data layers that portray realistic spatial representations.

Environmental concern↗
DOE OSTI2015

Dose domain regularization of MLC leaf patterns for highly complex IMRT plans

Purpose: The advent of automated beam orientation and fluence optimization enables more complex intensity modulated radiation therapy (IMRT) planning using an increasing number of fields to exploit the expanded solution space. This has created a challenge in converting complex fluences to robust multileaf collimator (MLC) segments for delivery. A novel method to regularize the fluence map and simplify MLC segments is introduced to maximize delivery efficiency, accuracy, and plan quality. Methods: In this work, we implemented a novel approach to regularize optimized fluences in the dose domain. The treatment planning problem was formulated in an optimization framework to minimize the segmentation-induced dose distribution degradation subject to a total variation regularization to encourage piecewise smoothness in fluence maps. The optimization problem was solved using a first-order primal-dual algorithm known as the Chambolle-Pock algorithm. Plans for 2 GBM, 2 head and neck, and 2 lung patients were created using 20 automatically selected and optimized noncoplanar beams. The fluence was first regularized using Chambolle-Pock and then stratified into equal steps, and the MLC segments were calculated using a previously described level reducing method. Isolated apertures with sizes smaller than preset thresholds of 1–3 bixels, which are square units of an IMRT fluence map from MLC discretization, were removed from the MLC segments. Performance of the dose domain regularized (DDR) fluences was compared to direct stratification and direct MLC segmentation (DMS) of the fluences using level reduction without dose domain fluence regularization. Results: For all six cases, the DDR method increased the average planning target volume dose homogeneity (D95/D5) from 0.814 to 0.878 while maintaining equivalent dose to organs at risk (OARs). Regularized fluences were more robust to MLC sequencing, particularly to the stratification and small aperture removal. The maximum and mean aperture sizes using the DDR were consistently larger than those from DMS for all tested number of segments. Conclusions: The fluence map to MLC segmentation conversion problem was formulated as a secondary optimization problem in the dose domain to minimize the smoothness-regularized dose discrepancy. The large scale optimization problem was solved using a primal-dual algorithm that transformed complicated fluences into maps that were more robust to the MLC segmentation and sequencing, affording fewer and larger segments with minimal degradation to dose distribution.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SE Great Basin Play Fairway Analysis PFA Models and Probability Map

This submission includes a Na/K geothermometer probability greater than 200 deg C map, as well as two play fairway analysis (PFA) models. The probability map acts as a composite risk segment for the PFA models. The PFA models differ in their application of magnetotelluric conductors as composite risk segments. These PFA models map out the geothermal potential in the region of SE Great Basin, Utah.

15 GEOTHERMAL ENERGY↗
DOE OSTI2014

SPECT Analysis of Cardiac Perfusion Changes After Whole-Breast/Chest Wall Radiation Therapy With or Without Active Breathing Coordinator: Results of a Randomized Phase 3 Trial

Purpose: Cardiac muscle perfusion, as determined by single-photon emission computed tomography (SPECT), decreases after breast and/or chest wall (BCW) irradiation. The active breathing coordinator (ABC) enables radiation delivery when the BCW is farther from the heart, thereby decreasing cardiac exposure. We hypothesized that ABC would prevent radiation-induced cardiac toxicity and conducted a randomized controlled trial evaluating myocardial perfusion changes after radiation for left-sided breast cancer with or without ABC. Methods and Materials: Stages I to III left breast cancer patients requiring adjuvant radiation therapy (XRT) were randomized to ABC or No-ABC. Myocardial perfusion was evaluated by SPECT scans (before and 6 months after BCW radiation) using 2 methods: (1) fully automated quantitative polar mapping; and (2) semiquantitative visual assessment. The left ventricle was divided into 20 segments for the polar map and 17 segments for the visual method. Segments were grouped by anatomical rings (apical, mid, basal) or by coronary artery distribution. For the visual method, 2 nuclear medicine physicians, blinded to treatment groups, scored each segment's perfusion. Scores were analyzed with nonparametric tests and linear regression. Results: Between 2006 and 2010, 57 patients were enrolled and 43 were available for analysis. The cohorts were well matched. The apical and left anterior descending coronary artery segments had significant decreases in perfusion on SPECT scans in both ABC and No-ABC cohorts. In unadjusted and adjusted analyses, controlling for pretreatment perfusion score, age, and chemotherapy, ABC was not significantly associated with prevention of perfusion deficits. Conclusions: In this randomized controlled trial, ABC does not appear to prevent radiation-induced cardiac perfusion deficits.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Bayesian Segmentation (BCNN) v.1

Deep learning has been applied with great success to the segmentation of 3D X-Ray Computed Tomography (CT) scans. Establishing the credibility of these segmentations requires uncertainty quantification (UQ) to identify problem areas. Bayesian neural networks (BNNs), which use variational inference to learn the posterior distribution of the neural network weights, have been proposed to incorporate UQ into deep learning models. This software is an implementation of a novel 3D Bayesian convolutional neural network (BCNN) that provides accurate binary segmentations and uncertainty maps for 3D volumes. In particular, the uncertainty maps generated by this BCNN capture continuity and visual gradients, making them interpretable as confidence intervals for segmentation usable in numerical simulations. SAND2019-12927 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

LaBonte, TylerMatthew↗
DOE OSTI2015

Magnetic Resonance–Based Automatic Air Segmentation for Generation of Synthetic Computed Tomography Scans in the Head Region

Purpose: To incorporate a novel imaging sequence for robust air and tissue segmentation using ultrashort echo time (UTE) phase images and to implement an innovative synthetic CT (synCT) solution as a first step toward MR-only radiation therapy treatment planning for brain cancer. Methods and Materials: Ten brain cancer patients were scanned with a UTE/Dixon sequence and other clinical sequences on a 1.0 T open magnet with simulation capabilities. Bone-enhanced images were generated from a weighted combination of water/fat maps derived from Dixon images and inverted UTE images. Automated air segmentation was performed using unwrapped UTE phase maps. Segmentation accuracy was assessed by calculating segmentation errors (true-positive rate, false-positive rate, and Dice similarity indices using CT simulation (CT-SIM) as ground truth. The synCTs were generated using a voxel-based, weighted summation method incorporating T2, fluid attenuated inversion recovery (FLAIR), UTE1, and bone-enhanced images. Mean absolute error (MAE) characterized Hounsfield unit (HU) differences between synCT and CT-SIM. A dosimetry study was conducted, and differences were quantified using γ-analysis and dose-volume histogram analysis. Results: On average, true-positive rate and false-positive rate for the CT and MR-derived air masks were 80.8% ± 5.5% and 25.7% ± 6.9%, respectively. Dice similarity indices values were 0.78 ± 0.04 (range, 0.70-0.83). Full field of view MAE between synCT and CT-SIM was 147.5 ± 8.3 HU (range, 138.3-166.2 HU), with the largest errors occurring at bone–air interfaces (MAE 422.5 ± 33.4 HU for bone and 294.53 ± 90.56 HU for air). Gamma analysis revealed pass rates of 99.4% ± 0.04%, with acceptable treatment plan quality for the cohort. Conclusions: A hybrid MRI phase/magnitude UTE image processing technique was introduced that significantly improved bone and air contrast in MRI. Segmented air masks and bone-enhanced images were integrated into our synCT pipeline for brain, and results agreed well with clinical CTs, thereby supporting MR-only radiation therapy treatment planning in the brain.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Data from "A Bayesian Record Linkage Approach to Applications in Tree Demography Using Overlapping LiDAR Scans"

Processed LiDAR data and environmental covariates from 2015 and 2019 LiDAR scans in the Vicinity of Snodgrass Mountain (Western Colorado, USA), in a geographic subset used in primary analysis for the research paper.This package contains LiDAR-derived canopy height maps for 2015 and 2019, crown polygons derived from the height maps using a segmentation algorithm, and environmental covariates supporting the model of forest growth. Source datasets include August 2015 and August 2019 discrete-return LiDAR point clouds collected by Quantum Geospatial for terrain mapping purposes on behalf of the Colorado Hazard Mapping Program and the Colorado Water Conservation Board. Both datasets adhere to the USGS QL2 quality standard. The point cloud data were processed using the R package lidR to generate a canopy height model representing maximum vegetation height above the ground surface, using a pit-free algorithm.This dataset was compiled to assess how spatial patterns of tree growth in montane and subalpine forests are influenced by water and energy availability. Understanding these growth patterns can provide insight into forest dynamics in the Southern Rocky Mountains under changing climatic conditions.This dataset contains .tif, .csv, and .txt files. This dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Automated Assembly Progress Monitoring in Modular Construction Factories Using Computer Vision-Based Instance Segmentation

Modular construction has recently gained interest as a transformative construction method. In this method, a large portion of the construction is performed inside factories, where processes are fast-paced and interdependent; therefore, any deviation from the schedule can delay the production. Such deviations are frequent in modular factories due to the labor-intensive nature of the tasks. This propagation of delays can be mitigated by continuously monitoring each process; however, current manual monitoring methods are laborious, and recently proposed contact sensor-based methods are intrusive to the work. In addition, recent computer vision-based monitoring methods inside factories are limited to detection algorithms that fail to provide the pixel-level accuracy required for assembly progress monitoring in highly occluded factory scenes, and they require a large number of manual annotations. Therefore, this paper proposes a method to monitor the installation of subassemblies in modular construction factories using mask R-CNN instance segmentation and improves the data efficiency of the model using a copy-paste augmentation method. This method was validated on the CCTV videos captured from a modular construction factory in the US, resulting in a 9% mAP improvement in segmentation.

computer vision↗
DOE OSTI2021

Quantifying the unknown impact of segmentation uncertainty on image-based simulations

Abstract Image-based simulation, the use of 3D images to calculate physical quantities, relies on image segmentation for geometry creation. However, this process introduces image segmentation uncertainty because different segmentation tools (both manual and machine-learning-based) will each produce a unique and valid segmentation. First, we demonstrate that these variations propagate into the physics simulations, compromising the resulting physics quantities. Second, we propose a general framework for rapidly quantifying segmentation uncertainty. Through the creation and sampling of segmentation uncertainty probability maps, we systematically and objectively create uncertainty distributions of the physics quantities. We show that physics quantity uncertainty distributions can follow a Normal distribution, but, in more complicated physics simulations, the resulting uncertainty distribution can be surprisingly nontrivial. We establish that bounding segmentation uncertainty can fail in these nontrivial situations. While our work does not eliminate segmentation uncertainty, it improves simulation credibility by making visible the previously unrecognized segmentation uncertainty plaguing image-based simulation.

42 ENGINEERING↗
DOE OSTI2013

Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas-aided fuzzy C-means method

Purpose: Breast magnetic resonance imaging (MRI) plays an important role in the clinical management of breast cancer. Studies suggest that the relative amount of fibroglandular (i.e., dense) tissue in the breast as quantified in MR images can be predictive of the risk for developing breast cancer, especially for high-risk women. Automated segmentation of the fibroglandular tissue and volumetric density estimation in breast MRI could therefore be useful for breast cancer risk assessment. Methods: In this work the authors develop and validate a fully automated segmentation algorithm, namely, an atlas-aided fuzzy C-means (FCM-Atlas) method, to estimate the volumetric amount of fibroglandular tissue in breast MRI. The FCM-Atlas is a 2D segmentation method working on a slice-by-slice basis. FCM clustering is first applied to the intensity space of each 2D MR slice to produce an initial voxelwise likelihood map of fibroglandular tissue. Then a prior learned fibroglandular tissue likelihood atlas is incorporated to refine the initial FCM likelihood map to achieve enhanced segmentation, from which the absolute volume of the fibroglandular tissue (|FGT|) and the relative amount (i.e., percentage) of the |FGT| relative to the whole breast volume (FGT%) are computed. The authors' method is evaluated by a representative dataset of 60 3D bilateral breast MRI scans (120 breasts) that span the full breast density range of the American College of Radiology Breast Imaging Reporting and Data System. The automated segmentation is compared to manual segmentation obtained by two experienced breast imaging radiologists. Segmentation performance is assessed by linear regression, Pearson's correlation coefficients, Student's pairedt-test, and Dice's similarity coefficients (DSC). Results: The inter-reader correlation is 0.97 for FGT% and 0.95 for |FGT|. When compared to the average of the two readers’ manual segmentation, the proposed FCM-Atlas method achieves a correlation ofr = 0.92 for FGT% and r = 0.93 for |FGT|, and the automated segmentation is not statistically significantly different (p = 0.46 for FGT% and p = 0.55 for |FGT|). The bilateral correlation between left breasts and right breasts for the FGT% is 0.94, 0.92, and 0.95 for reader 1, reader 2, and the FCM-Atlas, respectively; likewise, for the |FGT|, it is 0.92, 0.92, and 0.93, respectively. For the spatial segmentation agreement, the automated algorithm achieves a DSC of 0.69 ± 0.1 when compared to reader 1 and 0.61 ± 0.1 for reader 2, respectively, while the DSC between the two readers’ manual segmentation is 0.67 ± 0.15. Additional robustness analysis shows that the segmentation performance of the authors' method is stable both with respect to selecting different cases and to varying the number of cases needed to construct the prior probability atlas. The authors' results also show that the proposed FCM-Atlas method outperforms the commonly used two-cluster FCM-alone method. The authors' method runs at ∼5 min for each 3D bilateral MR scan (56 slices) for computing the FGT% and |FGT|, compared to ∼55 min needed for manual segmentation for the same purpose. Conclusions: The authors' method achieves robust segmentation and can serve as an efficient tool for processing large clinical datasets for quantifying the fibroglandular tissue content in breast MRI. It holds a great potential to support clinical applications in the future including breast cancer risk assessment.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

AN INDUCTIVE MAPPING WITH CONVOLUTIONAL REPRESENTATIONS FOR HUMAN SETTLEMENT DETECTION: PRELIMINARY RESULTS

Undoubtedly, deep convolutional learning methods continue to improve performance in image-level classification for computer vision and remote sensing applications. However, the spatio-temporal nature of remote sensing imagery offers other interesting challenges in multiscale image understanding. Emerging opportunities include seeking fine-grained and neighborhood mapping with overhead imagery. Limitations due to lack of relevant scale ground-truth often mandates that these challenges are pursued as disjoint. We test this premise and explore representation spaces from a single deep convolutional network and their visualization to argue for a unified feature extraction framework. The objective is to utilize and re-purpose trained feature extractors without the need for network retraining on three remote sensing tasks i.e. superpixel mapping, pixel-level segmentation and semantic based image visualization. By leveraging the same convolutional feature extractors and viewing them as visual information extractors that encode different image representation spaces, we demonstrate surprising potential on multiscale experiments that incorporate edge-level details up to semantic-level informationWe test this premise and explore representation spaces from a single deep convolutional network and their visualization to argue for a novel unified feature extraction framework. The objective is to utilize and re-purpose trained feature extractors without the need for network retraining on three remote sensing tasks i.e. superpixel mapping, pixel-level segmentation and semantic based image visualization. By leveraging the same convolutional feature extractors and viewing them as visual information extractors that encode different image representation spaces, we demonstrate a preliminary inductive transfer learning potential on multiscale experiments that incorporate edge-level details up to semantic-level informationWe test this premise and explore representation spaces from a single deep convolutional network and their visualization to argue for a novel unified feature extraction framework. The objective is to utilize and re-purpose trained feature extractors without the need for network retraining on three remote sensing tasks i.e. superpixel mapping, pixel-level segmentation and semantic based image visualization. By leveraging the same convolutional feature extractors and viewing them as visual information extractors that encode different image representation spaces, we demonstrate a preliminary inductive transfer learning potential on multiscale experiments that incorporate edge-level details up to semantic-level information.

Lunga, Dalton↗
DOE OSTI2022

Testing Hidden Assumptions of Representativeness in Reach-Scale Studies of Hyporheic Exchange

Field studies of hyporheic exchange in mountain systems are often conducted using short study reaches and a limited number of observations. It is common practice to assume these study reaches represent hyporheic exchange at larger scales or different sites and to infer general relationships among potential causal mechanisms from the limited number of observations. However, these assumptions of representativeness are rarely tested. In this study, we develop numerical models from four segments of mountain streams in different geomorphologic settings and extract shorter reaches to test how representative exchange metrics are in shorter reaches compared to their reference segments. We also map the locations of the representative reaches to determine if a pattern exists based on location. Finally, we compare variance of these shorter within-site reaches to 29 additional reaches across the same basin to understand the impacts of inferring causal mechanisms, for example, the expectation that wide and narrow valley bottoms will yield different hyporheic exchange patterns. Our results show that the location and length strategy of the study reach must be considered before assuming an exchange metric to be representative of anything other than the exact segment studied. Further, it is necessary to quantify within and between site variations before making causal inferences based on observable characteristics, such as valley width or stream morphology. Our findings have implications for future field practices and how those practices are translated into models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Model morphing and sequence assignment after molecular replacement

A procedure for model building is described that combines morphing a model to match a density map, trimming the morphed model and aligning the model to a sequence. A procedure termed ‘morphing’ for improving a model after it has been placed in the crystallographic cell by molecular replacement has recently been developed. Morphing consists of applying a smooth deformation to a model to make it match an electron-density map more closely. Morphing does not change the identities of the residues in the chain, only their coordinates. Consequently, if the true structure differs from the working model by containing different residues, these differences cannot be corrected by morphing. Here, a procedure that helps to address this limitation is described. The goal of the procedure is to obtain a relatively complete model that has accurate main-chain atomic positions and residues that are correctly assigned to the sequence. Residues in a morphed model that do not match the electron-density map are removed. Each segment of the resulting trimmed morphed model is then assigned to the sequence of the molecule using information about the connectivity of the chains from the working model and from connections that can be identified from the electron-density map. The procedure was tested by application to a recently determined structure at a resolution of 3.2 Å and was found to increase the number of correctly identified residues in this structure from the 88 obtained using phenix.resolve sequence assignment alone (Terwilliger, 2003 ▶) to 247 of a possible 359. Additionally, the procedure was tested by application to a series of templates with sequence identities to a target structure ranging between 7 and 36%. The mean fraction of correctly identified residues in these cases was increased from 33% using phenix.resolve sequence assignment to 47% using the current procedure. The procedure is simple to apply and is available in the Phenix software package.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Dynamically-Spaced Geo-Grid Segmentation for Weighted Point Sampling on a Polygon Map Layer

Geo-grid algorithms divide a large polygon area into several smaller polygons, which are important for studying or executing a set of operations on underlying topological features of a map. The current geo-grid algorithms divide a large polygon in to a set of smaller but equal size polygons only (e.g. is ArcMaps Fishnet). The time to create a geo-grid is typically proportional to number of smaller polygons created. This raises two problems - (i) They cannot skip unwanted areas (such as water bodies, given about 71% percent of the Earth's surface is water-covered); (ii) They are incognizant to any underlying feature set that requires more deliberation. In this work, we propose a novel dynamically spaced geo-grid segmentation algorithm that overcomes these challenges and provides a computationally optimal output for borderline cases of an uneven polygon. Our method uses an underlying topological feature of population distributions, from the LandScan Global 2016 dataset, for creating grids as a function of these weighted features. We benchmark our results against available algorithms and found our approach improves geo-grid creation. Later on, we demonstrate the proposed approach is more effective in harvesting Points of Interest data from a crowd-sourced platform.

Sims, Kelly↗
DOE OSTI2017

Long-term Study of the Solar Filaments from the Synoptic Maps as Derived from H{sub α} Spectroheliograms of the Kodaikanal Observatory

The century long (1914–2007) H{sub α} (656.28 nm) spectroheliograms from the Kodaikanal Solar Observatory (KSO) have been recently digitized. Using these newly calibrated, processed images we study the evolution of dark elongated on-disk structures called filaments, which are potential representatives of magnetic activities on the Sun. To our knowledge, this is the oldest uniform digitized data set with daily images available today in H{sub α}. We generate Carrington maps for the entire time duration and try to find the correlations with maps of the same Carrington rotation from the Ca ii K KSO data. Filaments are segmented from the Carrington maps using a semi-automated technique and are studied individually to extract their centroids and tilts. We plot the time-latitude distribution of the filament centroids, producing a butterfly diagram which clearly shows the presence of poleward migration. We separate polar filaments for each cycle and try to estimate the delay between the polar filament number cycle and the sunspot number cycle peaks. We correlate this delay with the delay between polar reversal and sunspot number maxima. This provides new insight on the role of polar filaments on polar reversal.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Segmentation and Comparative Modeling in an 8.6-Å Cryo-EM Map of the Singapore Grouper Iridovirus

SGIV, or Singapore grouper iridovirus, is a large double-stranded DNA virus, reaching a diameter of 220 nm and packaging a genome of 140 kb. We present a 3D cryoelectron microscopy (cryo-EM) icosahedral reconstruction of SGIV determined at 8.6-Å resolution. It reveals several layers including a T = 247 icosahedral outer coat, anchor proteins, a lipid bilayer, and the encapsidated DNA. A new segmentation tool, iSeg, was applied to extract these layers from the reconstructed map. The outer coat was further segmented into major and minor capsid proteins. None of the proteins extracted by segmentation have known atomic structures. Here, we generated models for the major coat protein using three comparative modeling tools, and evaluated each model using the cryo-EM map. Our analysis reveals a new architecture in the Iridoviridae family of viruses. It shares similarities with others in the same family, e.g., Chilo iridescent virus, but also shows new features of the major and minor capsid proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

TU-H-206-04: An Effective Homomorphic Unsharp Mask Filtering Method to Correct Intensity Inhomogeneity in Daily Treatment MR Images

Purpose: The daily treatment MRIs acquired on MR-IGRT systems, like diagnostic MRIs, suffer from intensity inhomogeneity issue, associated with B1 and B0 inhomogeneities. An improved homomorphic unsharp mask (HUM) filtering method, automatic and robust body segmentation, and imaging field-of-view (FOV) detection methods were developed to compute the multiplicative slow-varying correction field and correct the intensity inhomogeneity. The goal is to improve and normalize the voxel intensity so that the images could be processed more accurately by quantitative methods (e.g., segmentation and registration) that require consistent image voxel intensity values. Methods: HUM methods have been widely used for years. A body mask is required, otherwise the body surface in the corrected image would be incorrectly bright due to the sudden intensity transition at the body surface. In this study, we developed an improved HUM-based correction method that includes three main components: 1) Robust body segmentation on the normalized image gradient map, 2) Robust FOV detection (needed for body segmentation) using region growing and morphologic filters, and 3) An effective implementation of HUM using repeated Gaussian convolution. Results: The proposed method was successfully tested on patient images of common anatomical sites (H/N, lung, abdomen and pelvis). Initial qualitative comparisons showed that this improved HUM method outperformed three recently published algorithms (FCM, LEMS, MICO) in both computation speed (by 50+ times) and robustness (in intermediate to severe inhomogeneity situations). Currently implemented in MATLAB, it takes 20 to 25 seconds to process a 3D MRI volume. Conclusion: Compared to more sophisticated MRI inhomogeneity correction algorithms, the improved HUM method is simple and effective. The inhomogeneity correction, body mask, and FOV detection methods developed in this study would be useful as preprocessing tools for many MRI-related research and clinical applications in radiotherapy. Authors have received research grants from ViewRay and Varian.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TU-C-17A-07: FusionARC Treatment with Adaptive Beam Selection Method

Purpose: Recently, a new treatment scheme, FusionARC, has been introduced to compensate for the pitfalls in single-arc VMAT planning. It basically allows for the static field treatment in selected locations, while the remaining is treated by single-rotational arc delivery. The important issue is how to choose the directions for static field treatment. This study presents an adaptive beam selection method to formulate fusionARC treatment scheme. Methods: The optimal plan for single-rotational arc treatment is obtained from two-step approach based on the reweighted total-variation (TV) minimization. To choose the directions for static field treatment with extra segments, a value of our proposed cost function at each field is computed on the new fluence-map, which adds an extra segment to the designated field location only. The cost function is defined as a summation of equivalent uniform dose (EUD) of all structures with the fluence-map, while assuming that the lower cost function value implies the enhancement of plan quality. Finally, the extra segments for static field treatment would be added to the selected directions with low cost function values. A prostate patient data was applied and evaluated with three different plans: conventional VMAT, fusionARC, and static IMRT. Results: The 7 field locations, corresponding to the lowest cost function values, are chosen to insert extra segment for step-and-shoot dose delivery. Our proposed fusionARC plan with the selected angles improves the dose sparing to the critical organs, relative to static IMRT and conventional VMAT plans. The dose conformity to the target is significantly enhanced at the small expense of treatment time, compared with VMAT plan. Its estimated treatment time, however, is still much faster than IMRT. Conclusion: The fusionARC treatment with adaptive beam selection method could improve the plan quality with insignificant damage in the treatment time, relative to the conventional VMAT.

60 APPLIED LIFE SCIENCES↗