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DOE OSTI2022

MemGaze: Rapid and Effective Load-Level Memory Trace Analysis

A major challenge of memory analysis tools is combining high-resolution analysis and low overhead measurement. Currently, hardware/software-based analysis of load-level sequences incurs time slowdowns of O(100×). We present MemGaze, a tool for low-overhead, high-resolution memory analysis. MemGaze uses Intel’s Processor Tracing (PT) instruction ptwrite to collect sampled and compressed memory address traces for load-level, sequence-aware analysis of data reuse. We describe multi-resolution analysis for locations vs. operations, accesses vs. spatio-temporal reuse, and reuse (distance, rate, volume) vs. access patterns. Both trace size and resolution are controllable. We use MemGaze to elucidate the memory effects of different data structures and algorithms. For sampled traces that are ˜1% of a full one, analysis metrics have 1-25% MAPE for histograms of varying dynamic sequence lengths. With current suboptimal kernel support (PT runs continuously), MemGaze’s time overhead is typically 10–95%; 7× at worst. However, when PT runs only during samples, overhead is 10–35% on memory intensive regions and correlates with executed ptwrites.

Kilic, Ozgur O.↗

DOE OSTI2017

Generating Performance Models for Irregular Applications

Many applications have irregular behavior --- non-uniform input data, input-dependent solvers, irregular memory accesses, unbiased branches --- that cannot be captured using today's automated performance modeling techniques. We describe new hierarchical critical path analyses for the \Palm model generation tool. To create a model's structure, we capture tasks along representative MPI critical paths. We create a histogram of critical tasks with parameterized task arguments and instance counts. To model each task, we identify hot instruction-level sub-paths and model each sub-path based on data flow, instruction scheduling, and data locality. We describe application models that generate accurate predictions for strong scaling when varying CPU speed, cache speed, memory speed, and architecture. We present results for the Sweep3D neutron transport benchmark; Page Rank on multiple graphs; Support Vector Machine with pruning; and PFLOTRAN's reactive flow/transport solver with domain-induced load imbalance.

analytical performance models↗

DOE OSTI2017

Real-Time Fine Grained Occupancy Estimation Using Depth Sensors on ARM Embedded Platforms

Occupancy estimation is an important primitive for a wide range of applications including building energy efficiency, safety, and security. In this paper, we explore the potential of using depth sensors to detect, estimate, identify, and track occupants in buildings. While depth sensors have been widely used for human detection and gesture recognition, computer vision algorithms are typically run on a powerful computer like XBOX or Intel R CoreTM i7 processor. In this work, we develop a prototype system called FORK using off-the-shelf components that performs the entire depth data processing on a cheaper and low power ARM processor in real-time. As ARM processors are extremely weak in running computer vision algorithms, FORK is designed to detect humans and track them in a very efficient way by leveraging a novel lightweight model based approach instead of traditional approaches based on histogram of oriented gradients (HOG) features. Unlike other camera based approaches, FORK is much less privacy invasive (even if the sensor is compromised). Based on a complete implementation, real-world deployment, and extensive evaluation at realistic scenarios, we observe that FORK achieves over 99% accuracy in real-time (4-9 FPS) in occupancy estimation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Spatiotemporal Learning in Power Modules: Wavelet-Enhanced Forecasting of Thermomechanical Degradation

Detecting internal defects in power electronics packages is critical for their performance and reliability, especially under extreme operating conditions, as these defects can lead to catastrophic failure if not properly addressed. Confocal scanning acoustic microscopy (C-SAM) plays a key role in the nondestructive evaluation of bond layer degradation within a power electronics package by detecting defects such as delamination, voids, and cracks. However, accurately quantifying and predicting these defects from C-SAM images remains a significant challenge due to the low noise-to-signal ratio, which typically arises from both imaging process and bond patterns itself. In this paper, we explore machine learning strategies for processing C-SAM images and providing predictive models of defect growth. We use C-SAM images of sintered copper and sintered silver samples, which are obtained under accelerated thermal experiments, as the representative dataset for our study. We investigate the effect of Fourier transforms and wavelet transforms on these datasets to remove high-frequency noise and address noise across multiple scales with histogram equalization to enhance the contrast and improve the visibility of defects. As a result, defect boundaries can be clearly distinguished, enabling more accurate tracking of their growth over time. We then employ different time-series forecasting algorithms on the denoised images to formulate an image-based lifetime prediction model. Statistical models and deep-learning techniques are trained on images obtained in the early stages of thermal shock, and defect growth in the later stages is predicted. Our work serves as a preliminary attempt to improve the accuracy of lifetime prediction models of power electronics packages, which is critical under extreme operating environments.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

Sparse Coding for N-Gram Feature Extraction and Training for File Fragment Classification

File fragment classification is an important step in the task of file carving in digital forensics. In file carving, files must be reconstructed based on their content as a result of their fragmented storage on disk or in memory. Existing methods for classification of file fragments typically use hand-engineered features such as byte histograms or entropy measures. In this paper, we propose an approach using sparse coding that enables automated feature extraction. Sparse coding, or sparse dictionary learning, is an unsupervised learning algorithm, and is capable of extracting features based simply on how well those features can be used to reconstruct the original data. With respect to file fragments, we learn sparse dictionaries for n-grams, continuous sequences of bytes, of different sizes. These dictionaries may then be used to estimate n-gram frequencies for a given file fragment, but for significantly larger n-gram sizes than are typically found in existing methods which suffer from combinatorial explosion. To demonstrate the capability of our sparse coding approach, we used the resulting features to train standard classifiers such as support vector machines (SVMs) over multiple file types. Experimentally, we achieved significantly better classification results with respect to existing methods, especially when the features were used in supplement to existing hand-engineered features.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Fiber Uncertainty Visualization for Bivariate Data With Parametric and Nonparametric Noise Models

Visualization and analysis of multivariate data and their uncertainty are top research challenges in data visualization. Constructing fiber surfaces is a popular technique for multivariate data visualization that generalizes the idea of level-set visualization for univariate data to multivariate data. Here, in this paper, we present a statistical framework to quantify positional probabilities of fibers extracted from uncertain bivariate fields. Specifically, we extend the state-of-the-art Gaussian models of uncertainty for bivariate data to other parametric distributions (e.g., uniform and Epanechnikov) and more general nonparametric probability distributions (e.g., histograms and kernel density estimation) and derive corresponding spatial probabilities of fibers. In our proposed framework, we leverage Green's theorem for closed-form computation of fiber probabilities when bivariate data are assumed to have independent parametric and nonparametric noise. Additionally, we present a nonparametric approach combined with numerical integration to study the positional probability of fibers when bivariate data are assumed to have correlated noise. For uncertainty analysis, we visualize the derived probability volumes for fibers via volume rendering and extracting level sets based on probability thresholds. We present the utility of our proposed techniques via experiments on synthetic and simulation datasets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Uncertainty Visualization of Critical Points of 2D Scalar Fields for Parametric and Nonparametric Probabilistic Models

This paper presents a novel end-to-end framework for closed-form computation and visualization of critical point uncertainty in 2D uncertain scalar fields. Critical points are fundamental topological descriptors used in the visualization and analysis of scalar fields. The uncertainty inherent in data (e.g., observational and experimental data, approximations in simulations, and compression), however, creates uncertainty regarding critical point positions. Uncertainty in critical point positions, therefore, cannot be ignored, given their impact on downstream data analysis tasks. Here, in this work, we study uncertainty in critical points as a function of uncertainty in data modeled with probability distributions. Although Monte Carlo (MC) sampling techniques have been used in prior studies to quantify critical point uncertainty, they are often expensive and are infrequently used in production-quality visualization software. We, therefore, propose a new end-to-end framework to address these challenges that comprises a threefold contribution. First, we derive the critical point uncertainty in closed form, which is more accurate and efficient than the conventional MC sampling methods. Specifically, we provide the closed-form and semianalytical (a mix of closed-form and MC methods) solutions for parametric (e.g., uniform, Epanechnikov) and nonparametric models (e.g., histograms) with finite support. Second, we accelerate critical point probability computations using a parallel implementation with the VTK-m library, which is platform portable. Finally, we demonstrate the integration of our implementation with the ParaView software system to demonstrate near-real-time results for real datasets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Completeness and coverage of open-access freshwater fish distribution data in the United States

Aim: Primary biodiversity data constitute observations of particular species at given points in space and time. Open-access databases provide unprecedented access to these data, but their utility depends on how complete species inventories are at a given surveying resolution and how uniformly distributed surveys are in space and time. Our aim was to characterize the completeness of freshwater fish data across spatial scales and among habitat types (lotic stream reaches and lentic waterbodies), and assess coverage bias in space and time. Location: Freshwater habitats of the contiguous United States. Methods: We compiled fish occurrence records from the Global Biodiversity Information Facility (GBIF), the Multi-state Aquatic Resources Information Server (MARIS), and three federally-administered surveys (EMAP, NAQWA, NRS). Next, we evaluated survey completeness across seven spatial resolutions representing nested stream catchments, and separately for lotic and lentic habitats. Lastly, we tested for bias in survey coverage along environmental and temporal gradients for each habitat type. Results: A total of 39,855,157 records representing 1925 species were available from 1800 to 2014. The percent of catchments surveyed increased as spatial resolution decreased while the density of occurrence records within surveyed catchments decreased. Species richness also increased with catchment size consistent with putative species–area relationships. Occurrence records were distributed among 72,114 lotic reaches and 3,325 lentic waterbodies throughout the contiguous US. Seven percent (N=5,055) of surveyed lotic reaches and 23% (N=771) of surveyed lentic waterbodies were well-surveyed based on the most conservative thresholds for two completeness indices. Most well-surveyed lotic reaches (95%) and undammed lentic waterbodies (97%) were completed since 1980, and most were surveyed during only one 20-year time interval (83% and 56% for lotic and lentic waterbodies, respectively). Coverage analyses indicated that contemporary and future climates corresponding to regions along the west coast and gulf coast are the most under-represented for both lotic and lentic habitats. Small lentic waterbodies (surface area < 0.1 km2) are also under-represented. Main Conclusions: Contemporary surveys of lotic reaches and, to a lesser extent, lentic waterbodies are plentiful and generally representative of climatic and landscape characteristics. Data compilation efforts should focus on adding historical data to public repositories. Clustering of well-surveyed waterbodies in MARIS-participating states highlights the value of state-agency data and demonstrates the need for dissemination of these data by more state agencies.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Direct current response of a thin scCVD diamond detector under increased applied field to 14.1 MeV neutrons

An avalanche effect yielding inherent gain can be exploited in thin, single-crystal chemical vapor deposition (scCVD) diamond. It occurs when a high enough bias is applied across the diamond thickness while avoiding breakdown. This charge multiplication effect was studied previously with alpha particles and heavy ions either by using the transient current technique or by measuring the energy spectrum. The measurements we obtained to evaluate the charge multiplication performance of a 10 μm thick scCVD diamond detector used a novel approach—we employed an electrometer to characterize the response of the detector by performing directly coupled current measurements (time-averaged charge, at 1 Hz sampling) when exposed to 14.1 MeV neutrons from deuterium-tritium fusion. We measured both the dark and irradiated currents from the detector over a range of applied displacement field values from 2 to 75 V/μm. A histogram method with central mean and standard deviation width was used to determine the current over each measurement duration typically from 100 to 300 seconds. The dark-subtracted irradiated current (i.e., contrast) was used to evaluate the gain of the detector at each applied displacement field. The contrast at an applied displacement field between 15 and 20 V/μm was higher than the expected linear increase in contrast proportional to the increased applied bias, indicating the possible presence of avalanche events in the diamond. The detector response also indicated possible polarization and charge depletion effects. These results provide an opportunity to further explore the use of thin scCVD diamond as a fast neutron current mode detector with inherent gain.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2013

PTV-based IMPT optimization incorporating planning risk volumes vs robust optimization

Purpose: Robust optimization leads to intensity-modulated proton therapy (IMPT) plans that are less sensitive to uncertainties and superior in terms of organs-at-risk (OARs) sparing, target dose coverage, and homogeneity compared to planning target volume (PTV)-based optimized plans. Robust optimization incorporates setup and range uncertainties, which implicitly adds margins to both targets and OARs and is also able to compensate for perturbations in dose distributions within targets and OARs caused by uncertainties. In contrast, the traditional PTV-based optimization considers only setup uncertainties and adds a margin only to targets but no margins to the OARs. It also ignores range uncertainty. The purpose of this work is to determine if robustly optimized plans are superior to PTV-based plans simply because the latter do not assign margins to OARs during optimization. Methods: The authors retrospectively selected from their institutional database five patients with head and neck (H and N) cancer and one with prostate cancer for this analysis. Using their original images and prescriptions, the authors created new IMPT plans using three methods: PTV-based optimization, optimization based on the PTV and planning risk volumes (PRVs) (i.e., 'PTV+PRV-based optimization'), and robust optimization using the 'worst-case' dose distribution. The PRVs were generated by uniformly expanding OARs by 3 mm for the H and N cases and 5 mm for the prostate case. The dose-volume histograms (DVHs) from the worst-case dose distributions were used to assess and compare plan quality. Families of DVHs for each uncertainty for all structures of interest were plotted along with the nominal DVHs. The width of the 'bands' of DVHs was used to quantify the plan sensitivity to uncertainty. Results: Compared with conventional PTV-based and PTV+PRV-based planning, robust optimization led to a smaller bandwidth for the targets in the face of uncertainties {l_brace}clinical target volume [CTV] bandwidth: 0.59 [robust], 3.53 [PTV+PRV], and 3.53 [PTV] Gy (RBE){r_brace}. It also resulted in higher doses to 95% of the CTV {l_brace}D{sub 95%}: 60.8 [robust] vs 59.3 [PTV+PRV] vs 59.6 [PTV] Gy (RBE){r_brace}, smaller D{sub 5%} (doses to 5% of the CTV) minus D{sub 95%} {l_brace}D{sub 5%}- D{sub 95%}: 13.2 [robust] vs 17.5 [PTV+PRV] vs 17.2 [PTV] Gy (RBE){r_brace}. At the same time, the robust optimization method irradiated OARs less {l_brace}maximum dose to 1 cm{sup 3} of the brainstem: 48.3 [robust] vs 48.8 [PTV+PRV] vs 51.2 [PTV] Gy (RBE); mean dose to the oral cavity: 22.3 [robust] vs 22.9 [PTV+PRV] vs 26.1 [PTV] Gy (RBE); maximum dose to 1% of the normal brain: 66.0 [robust] vs 68.0 [PTV+PRV] vs 69.3 [PTV] Gy (RBE){r_brace}. Conclusions: For H and N cases studied, OAR sparing in PTV+PRV-based optimization was inferior compared to robust optimization but was superior compared to PTV-based optimization; however, target dose robustness and homogeneity were comparable in the PTV+PRV-based and PTV-based optimizations. The same pattern held for the prostate case. The authors' data suggest that the superiority of robust optimization is not due simply to its inclusion of margins for OARs, but that this is due mainly to the ability of robust optimization to compensate for perturbations in dose distributions within target volumes and normal tissues caused by uncertainties.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

Objective models of compressed breast shapes undergoing mammography

Purpose: To develop models of compressed breasts undergoing mammography based on objective analysis, that are capable of accurately representing breast shapes in acquired clinical images and generating new, clinically realistic shapes. Methods: An automated edge detection algorithm was used to catalogue the breast shapes of clinically acquired cranio-caudal (CC) and medio-lateral oblique (MLO) view mammograms from a large database of digital mammography images. Principal component analysis (PCA) was performed on these shapes to reduce the information contained within the shapes to a small number of linearly independent variables. The breast shape models, one of each view, were developed from the identified principal components, and their ability to reproduce the shape of breasts from an independent set of mammograms not used in the PCA, was assessed both visually and quantitatively by calculating the average distance error (ADE). Results: The PCA breast shape models of the CC and MLO mammographic views based on six principal components, in which 99.2% and 98.0%, respectively, of the total variance of the dataset is contained, were found to be able to reproduce breast shapes with strong fidelity (CC view mean ADE = 0.90 mm, MLO view mean ADE = 1.43 mm) and to generate new clinically realistic shapes. The PCA models based on fewer principal components were also successful, but to a lesser degree, as the two-component model exhibited a mean ADE = 2.99 mm for the CC view, and a mean ADE = 4.63 mm for the MLO view. The four-component models exhibited a mean ADE = 1.47 mm for the CC view and a mean ADE = 2.14 mm for the MLO view. Paired t-tests of the ADE values of each image between models showed that these differences were statistically significant (max p-value = 0.0247). Visual examination of modeled breast shapes confirmed these results. Histograms of the PCA parameters associated with the six principal components were fitted with Gaussian distributions. The six-component model was also used to generate CC and MLO view mammogram breast shapes, using the mean PCA parameter values of these distributions and randomly generated values based on the fitted Gaussian distributions, which resemble clinically encountered breasts. A spreadsheet with the data necessary to apply this model is provided as the supplementary material. Conclusions: Our PCA models of breast shapes in both mammographic views successfully reproduce analyzed breast shapes and generate new clinically relevant shapes. This work can aid in research applications which incorporate breast shape modeling, such as x-ray scatter correction, dosimetry, and image registration.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

A gEUD-based inverse planning technique for HDR prostate brachytherapy: Feasibility study

Purpose: The purpose of this work was to study the feasibility of a new inverse planning technique based on the generalized equivalent uniform dose for image-guided high dose rate (HDR) prostate cancer brachytherapy in comparison to conventional dose-volume based optimization. Methods: The quality of 12 clinical HDR brachytherapy implants for prostate utilizing HIPO (Hybrid Inverse Planning Optimization) is compared with alternative plans, which were produced through inverse planning using the generalized equivalent uniform dose (gEUD). All the common dose-volume indices for the prostate and the organs at risk were considered together with radiobiological measures. The clinical effectiveness of the different dose distributions was investigated by comparing dose volume histogram and gEUD evaluators. Results: Our results demonstrate the feasibility of gEUD-based inverse planning in HDR brachytherapy implants for prostate. A statistically significant decrease in D{sub 10} or/and final gEUD values for the organs at risk (urethra, bladder, and rectum) was found while improving dose homogeneity or dose conformity of the target volume. Conclusions: Following the promising results of gEUD-based optimization in intensity modulated radiation therapy treatment optimization, as reported in the literature, the implementation of a similar model in HDR brachytherapy treatment plan optimization is suggested by this study. The potential of improved sparing of organs at risk was shown for various gEUD-based optimization parameter protocols, which indicates the ability of this method to adapt to the user's preferences.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

Needle segmentation using 3D Hough transform in 3D TRUS guided prostate transperineal therapy

Purpose: Prostate adenocarcinoma is the most common noncutaneous malignancy in American men with over 200 000 new cases diagnosed each year. Prostate interventional therapy, such as cryotherapy and brachytherapy, is an effective treatment for prostate cancer. Its success relies on the correct needle implant position. This paper proposes a robust and efficient needle segmentation method, which acts as an aid to localize the needle in three-dimensional (3D) transrectal ultrasound (TRUS) guided prostate therapy. Methods: The procedure of locating the needle in a 3D TRUS image is a three-step process. First, the original 3D ultrasound image containing a needle is cropped; the cropped image is then converted to a binary format based on its histogram. Second, a 3D Hough transform based needle segmentation method is applied to the 3D binary image in order to locate the needle axis. The position of the needle endpoint is finally determined by an optimal threshold based analysis of the intensity probability distribution. The overall efficiency is improved through implementing a coarse-fine searching strategy. The proposed method was validated in tissue-mimicking agar phantoms, chicken breast phantoms, and 3D TRUS patient images from prostate brachytherapy and cryotherapy procedures by comparison to the manual segmentation. The robustness of the proposed approach was tested by means of varying parameters such as needle insertion angle, needle insertion length, binarization threshold level, and cropping size. Results: The validation results indicate that the proposed Hough transform based method is accurate and robust, with an achieved endpoint localization accuracy of 0.5 mm for agar phantom images, 0.7 mm for chicken breast phantom images, and 1 mm for in vivo patient cryotherapy and brachytherapy images. The mean execution time of needle segmentation algorithm was 2 s for a 3D TRUS image with size of 264 Multiplication-Sign 376 Multiplication-Sign 630 voxels. Conclusions: The proposed needle segmentation algorithm is accurate, robust, and suitable for 3D TRUS guided prostate transperineal therapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

A support vector machine classifier reduces interscanner variation in the HRCT classification of regional disease pattern in diffuse lung disease: Comparison to a Bayesian classifier

Purpose: To investigate the effect of using different computed tomography (CT) scanners on the accuracy of high-resolution CT (HRCT) images in classifying regional disease patterns in patients with diffuse lung disease, support vector machine (SVM) and Bayesian classifiers were applied to multicenter data. Methods: Two experienced radiologists marked sets of 600 rectangular 20 Multiplication-Sign 20 pixel regions of interest (ROIs) on HRCT images obtained from two scanners (GE and Siemens), including 100 ROIs for each of local patterns of lungs-normal lung and five of regional pulmonary disease patterns (ground-glass opacity, reticular opacity, honeycombing, emphysema, and consolidation). Each ROI was assessed using 22 quantitative features belonging to one of the following descriptors: histogram, gradient, run-length, gray level co-occurrence matrix, low-attenuation area cluster, and top-hat transform. For automatic classification, a Bayesian classifier and a SVM classifier were compared under three different conditions. First, classification accuracies were estimated using data from each scanner. Next, data from the GE and Siemens scanners were used for training and testing, respectively, and vice versa. Finally, all ROI data were integrated regardless of the scanner type and were then trained and tested together. All experiments were performed based on forward feature selection and fivefold cross-validation with 20 repetitions. Results: For each scanner, better classification accuracies were achieved with the SVM classifier than the Bayesian classifier (92% and 82%, respectively, for the GE scanner; and 92% and 86%, respectively, for the Siemens scanner). The classification accuracies were 82%/72% for training with GE data and testing with Siemens data, and 79%/72% for the reverse. The use of training and test data obtained from the HRCT images of different scanners lowered the classification accuracy compared to the use of HRCT images from the same scanner. For integrated ROI data obtained from both scanners, the classification accuracies with the SVM and Bayesian classifiers were 92% and 77%, respectively. The selected features resulting from the classification process differed by scanner, with more features included for the classification of the integrated HRCT data than for the classification of the HRCT data from each scanner. For the integrated data, consisting of HRCT images of both scanners, the classification accuracy based on the SVM was statistically similar to the accuracy of the data obtained from each scanner. However, the classification accuracy of the integrated data using the Bayesian classifier was significantly lower than the classification accuracy of the ROI data of each scanner. Conclusions: The use of an integrated dataset along with a SVM classifier rather than a Bayesian classifier has benefits in terms of the classification accuracy of HRCT images acquired with more than one scanner. This finding is of relevance in studies involving large number of images, as is the case in a multicenter trial with different scanners.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

Dosimetric impact of Acuros XB deterministic radiation transport algorithm for heterogeneous dose calculation in lung cancer

Purpose: The novel deterministic radiation transport algorithm, Acuros XB (AXB), has shown great potential for accurate heterogeneous dose calculation. However, the clinical impact between AXB and other currently used algorithms still needs to be elucidated for translation between these algorithms. The purpose of this study was to investigate the impact of AXB for heterogeneous dose calculation in lung cancer for intensity-modulated radiation therapy (IMRT) and volumetric-modulated arc therapy (VMAT). Methods: The thorax phantom from the Radiological Physics Center (RPC) was used for this study. IMRT and VMAT plans were created for the phantom in the Eclipse 11.0 treatment planning system. Each plan was delivered to the phantom three times using a Varian Clinac iX linear accelerator to ensure reproducibility. Thermoluminescent dosimeters (TLDs) and Gafchromic EBT2 film were placed inside the phantom to measure delivered doses. The measurements were compared with dose calculations from AXB 11.0.21 and the anisotropic analytical algorithm (AAA) 11.0.21. Two dose reporting modes of AXB, dose-to-medium in medium (D{sub m,m}) and dose-to-water in medium (D{sub w,m}), were studied. Point doses, dose profiles, and gamma analysis were used to quantify the agreement between measurements and calculations from both AXB and AAA. The computation times for AAA and AXB were also evaluated. Results: For the RPC lung phantom, AAA and AXB dose predictions were found in good agreement to TLD and film measurements for both IMRT and VMAT plans. TLD dose predictions were within 0.4%-4.4% to AXB doses (both D{sub m,m} and D{sub w,m}); and within 2.5%-6.4% to AAA doses, respectively. For the film comparisons, the gamma indexes ({+-}3%/3 mm criteria) were 94%, 97%, and 98% for AAA, AXB{sub Dm,m}, and AXB{sub Dw,m}, respectively. The differences between AXB and AAA in dose-volume histogram mean doses were within 2% in the planning target volume, lung, heart, and within 5% in the spinal cord. However, differences up to 8% between AXB and AAA were found at lung/soft tissue interface regions for individual IMRT fields. AAA was found to be 5-6 times faster than AXB for IMRT, while AXB was 4-5 times faster than AAA for VMAT plan. Conclusions: AXB is satisfactorily accurate for the dose calculation in lung cancer for both IMRT and VMAT plans. The differences between AXB and AAA are generally small except in heterogeneous interface regions. AXB D{sub w,m} and D{sub m,m} calculations are similar inside the soft tissue and lung regions. AXB can benefit lung VMAT plans by both improving accuracy and reducing computation time.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

A study of IMRT planning parameters on planning efficiency, delivery efficiency, and plan quality

Purpose: To improve planning and delivery efficiency of head and neck IMRT without compromising planning quality through the evaluation of inverse planning parameters.Methods: Eleven head and neck patients with pre-existing IMRT treatment plans were selected for this retrospective study. The Pinnacle treatment planning system (TPS) was used to compute new treatment plans for each patient by varying the individual or the combined parameters of dose/fluence grid resolution, minimum MU per segment, and minimum segment area. Forty-five plans per patient were generated with the following variations: 4 dose/fluence grid resolution plans, 12 minimum segment area plans, 9 minimum MU plans, and 20 combined minimum segment area/minimum MU plans. Each plan was evaluated and compared to others based on dose volume histograms (DVHs) (i.e., plan quality), planning time, and delivery time. To evaluate delivery efficiency, a model was developed that estimated the delivery time of a treatment plan, and validated through measurements on an Elekta Synergy linear accelerator. Results: The uncertainty (i.e., variation) of the dose-volume index due to dose calculation grid variation was as high as 8.2% (5.5 Gy in absolute dose) for planning target volumes (PTVs) and 13.3% (2.1 Gy in absolute dose) for planning at risk volumes (PRVs). Comparison results of dose distributions indicated that smaller volumes were more susceptible to uncertainties. The grid resolution of a 4 mm dose grid with a 2 mm fluence grid was recommended, since it can reduce the final dose calculation time by 63% compared to the accepted standard (2 mm dose grid with a 2 mm fluence grid resolution) while maintaining a similar level of dose-volume index variation. Threshold values that maintained adequate plan quality (DVH results of the PTVs and PRVs remained satisfied for their dose objectives) were 5 cm{sup 2} for minimum segment area and 5 MU for minimum MU. As the minimum MU parameter was increased, the number of segments and delivery time were decreased. Increasing the minimum segment area parameter decreased the plan MU, but had less of an effect on the number of segments and delivery time. Our delivery time model predicted delivery time to within 1.8%. Conclusions: Increasing the dose grid while maintaining a small fluence grid allows for improved planning efficiency without compromising plan quality. Delivery efficiency can be improved by increasing the minimum MU, but not the minimum segment area. However, increasing the respective minimum MU and/or the minimum segment area to any value greater than 5 MU and 5 cm{sup 2} is not recommended because it degrades plan quality.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2013

3D inpatient dose reconstruction from the PET-CT imaging of {sup 90}Y microspheres for metastatic cancer to the liver: Feasibility study

Purpose: The introduction of radioembolization with microspheres represents a significant step forward in the treatment of patients with metastatic disease to the liver. This technique uses semiempirical formulae based on body surface area or liver and target volumes to calculate the required total activity for a given patient. However, this treatment modality lacks extremely important information, which is the three-dimensional (3D) dose delivered by microspheres to different organs after their administration. The absence of this information dramatically limits the clinical efficacy of this modality, specifically the predictive power of the treatment. Therefore, the aim of this study is to develop a 3D dose calculation technique that is based on the PET imaging of the infused microspheres.Methods: The Fluka Monte Carlo code was used to calculate the voxel dose kernel for {sup 90}Y source with voxel size equal to that of the PET scan. The measured PET activity distribution was converted to total activity distribution for the subsequent convolution with the voxel dose kernel to obtain the 3D dose distribution. In addition, dose-volume histograms were generated to analyze the dose to the tumor and critical structures.Results: The 3D inpatient dose distribution can be reconstructed from the PET data of a patient scanned after the infusion of microspheres. A total of seven patients have been analyzed so far using the proposed reconstruction method. Four patients underwent treatment with SIR-Spheres for liver metastases from colorectal cancer and three patients were treated with Therasphere for hepatocellular cancer. A total of 14 target tumors were contoured on post-treatment PET-CT scans for dosimetric evaluation. Mean prescription activity was 1.7 GBq (range: 0.58–3.8 GBq). The resulting mean maximum measured dose to targets was 167 Gy (range: 71–311 Gy). Mean minimum dose to 70% of target (D70) was 68 Gy (range: 25–155 Gy). Mean minimum dose to 90% of target (D90) was 53 Gy (range: 13–125 Gy).Conclusions: A three-dimensional inpatient dose reconstruction method has been developed that is based on the PET/CT data of a patient treated with {sup 90}Y microspheres. It allows for a complete description of the absorbed dose by the tumor and critical structures. It represents the first step in building predictive models for treatment outcomes for patients receiving this therapeutic modality as well as it allows for better analysis of patients' dose response and will ultimately improve future treatment administration.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2013

Cobalt-60 tomotherapy: Clinical treatment planning and phantom dose delivery studies

Purpose: Investigations have shown that a Cobalt-60 (Co-60) radioactive source has the potential to play a role in intensity modulated radiation therapy (IMRT). In this paper, Co-60 tomotherapy's conformal dose delivery potential is evaluated by delivering conformal dose plans on a cylindrical homogeneous phantom containing clinical structures similar to those found in a typical head and neck (H and N) cancer. Also, the clinical potential of Co-60 tomotherapy is investigated by generating 2D clinical treatment plans for H and N and prostate anatomical regions. These plans are compared with the 6 MV based treatment plans for modalities such as linear accelerator-based tomotherapy and broad beam IMRT, and 15 MV based 3D conformal radiation therapy (3DCRT).Methods: For experimental validation studies, clinical and nonclinical conformal dose patterns were delivered on circular, homogeneous phantoms containing GafChromic film. For clinical planning study, dose calculations were performed with the EGSnrc Monte Carlo program, where a Theratronics 780C Co-60 unit and a 6 MV linear accelerator were modeled with a MIMiC binary multileaf collimator. An inhouse inverse treatment planning system was used to optimize tomotherapy plans using the same optimization parameters for both Co-60 and 6 MV beams. The IMRT and 3DCRT plans for the clinical cases were generated entirely in the Eclipse treatment planning system based on inhouse IMRT and 3DCRT site specific protocols.Results: The doses delivered to the homogeneous phantoms agreed with the calculations, indicating that it is possible to deliver highly conformal doses with the Co-60 unit. The dose distributions for Co-60 tomotherapy clinical plans for both clinical cases were similar to those obtained with 6 MV based tomotherapy and IMRT, and much more conformal compared to 3DCRT plans. The dose area histograms showed that the Co-60 plans achieve the dose objectives for the targets and organs at risk.Conclusions: These results confirm that Co-60 tomotherapy is capable of providing state-of-the-art conformal dose delivery and could be used for the treatment of targets in both small and larger separation anatomical regions.

61 RADIATION PROTECTION AND DOSIMETRY↗