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At least 775 records · Page 43

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DOE OSTI2016

SU-F-J-133: Adaptive Radiation Therapy with a Four-Dimensional Dose Calculation Algorithm That Optimizes Dose Distribution Considering Breathing Motion

Purpose: To model patient motion and produce four-dimensional (4D) optimized dose distributions that consider motion-artifacts in the dose calculation during the treatment planning process. Methods: An algorithm for dose calculation is developed where patient motion is considered in dose calculation at the stage of the treatment planning. First, optimal dose distributions are calculated for the stationary target volume where the dose distributions are optimized considering intensity-modulated radiation therapy (IMRT). Second, a convolution-kernel is produced from the best-fitting curve which matches the motion trajectory of the patient. Third, the motion kernel is deconvolved with the initial dose distribution optimized for the stationary target to produce a dose distribution that is optimized in four-dimensions. This algorithm is tested with measured doses using a mobile phantom that moves with controlled motion patterns. Results: A motion-optimized dose distribution is obtained from the initial dose distribution of the stationary target by deconvolution with the motion-kernel of the mobile target. This motion-optimized dose distribution is equivalent to that optimized for the stationary target using IMRT. The motion-optimized and measured dose distributions are tested with the gamma index with a passing rate of >95% considering 3% dose-difference and 3mm distance-to-agreement. If the dose delivery per beam takes place over several respiratory cycles, then the spread-out of the dose distributions is only dependent on the motion amplitude and not affected by motion frequency and phase. This algorithm is limited to motion amplitudes that are smaller than the length of the target along the direction of motion. Conclusion: An algorithm is developed to optimize dose in 4D. Besides IMRT that provides optimal dose coverage for a stationary target, it extends dose optimization to 4D considering target motion. This algorithm provides alternative to motion management techniques such as beam-gating or breath-holding and has potential applications in adaptive radiation therapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-J-175: Evaluation of Metal Artifact Reduction Algorithms in Computed Tomography and Their Application to Radiation Therapy Treatment Planning

Purpose: High-Z (metal) implants in CT scans cause significant streak-like artifacts in the reconstructed dataset. This results in both inaccurate CT Hounsfield units for the tissue as well as obscuration of the target and organs at risk (OARs) for radiation therapy planning. Herein we analyze two metal artifact reduction algorithms: GE’s Smart MAR and a Metal Deletion Technique (MDT) for geometric and Hounsfield Unit (HU) accuracy. Methods: A CT-to-electron density phantom, with multiple inserts of various densities and a custom Cerrobend insert (Zeff=76.8), is utilized in this continuing study. The phantom is scanned without metal (baseline) and again with the metal insert. Using one set of projection data, reconstructed CT volumes are created with filtered-back-projection (FBP) and the MAR and the MDT algorithms. Regions-of-Interest (ROIs) are evaluated for each insert for HU accuracy; the metal insert’s Full-Width-Half-Maximum (FWHM) is used to evaluate the geometric accuracy. Streak severity is quantified with an HU error metric over the phantom volume. Results: The original FBP reconstruction has a Root-Mean-Square-Error (RMSE) of 57.55 HU (STD=29.19, range=−145.8 to +79.2) compared to baseline. The MAR reconstruction has a RMSE of 20.98 HU (STD=13.92, range=−18.3 to +61.7). The MDT reconstruction has a RMSE of 10.05 HU (STD=10.5, range=−14.8 to +18.6). FWHM for baseline=162.05; FBP=161.84 (−0.13%); MAR=162.36 (+0.19%); MDT=162.99 (+0.58%). Streak severity metric for FBP=19.73 (22.659% bad pixels); MAR=8.743 (9.538% bad); MDT=4.899 (5.303% bad). Conclusion: Image quality, in terms of HU accuracy, in the presence of high-Z metal objects in CT scans is improved by metal artifact reduction reconstruction algorithms. The MDT algorithm had the highest HU value accuracy (RMSE=10.05 HU) and best streak severity metric, but scored the worst in terms of geometric accuracy. Qualitatively, the MAR and MDT algorithms increased detectability of inserts, although there is a loss of in-plane resolution near the metallic insert.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-T-209: Multicriteria Optimization Algorithm for Intensity Modulated Radiation Therapy Using Pencil Proton Beam Scanning

Purpose: To provide a multicriteria optimization algorithm for intensity modulated radiation therapy using pencil proton beam scanning. Methods: Intensity modulated radiation therapy using pencil proton beam scanning requires efficient optimization algorithms to overcome the uncertainties in the Bragg peaks locations. This work is focused on optimization algorithms that are based on Monte Carlo simulation of the treatment planning and use the weights and the dose volume histogram (DVH) control points to steer toward desired plans. The proton beam treatment planning process based on single objective optimization (representing a weighted sum of multiple objectives) usually leads to time-consuming iterations involving treatment planning team members. We proved a time efficient multicriteria optimization algorithm that is developed to run on NVIDIA GPU (Graphical Processing Units) cluster. The multicriteria optimization algorithm running time benefits from up-sampling of the CT voxel size of the calculations without loss of fidelity. Results: We will present preliminary results of Multicriteria optimization for intensity modulated proton therapy based on DVH control points. The results will show optimization results of a phantom case and a brain tumor case. Conclusion: The multicriteria optimization of the intensity modulated radiation therapy using pencil proton beam scanning provides a novel tool for treatment planning. Work support by a grant from Varian Inc.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-G-JeP1-07: Development of a Programmable Motion Testbed for the Validation of Ultrasound Tracking Algorithms

Purpose: To develop an advanced testbed that combines a 3D motion stage and ultrasound phantom to optimize and validate 2D and 3D tracking algorithms for real-time motion management during radiation therapy. Methods: A Siemens S2000 Ultrasound scanner utilizing a 9L4 transducer was coupled with the Washington University 4D Phantom to simulate patient motion. The transducer was securely fastened to the 3D stage and positioned to image three cylinders of varying contrast in a Gammex 404GS LE phantom. The transducer was placed within a water bath above the phantom in order to maintain sufficient coupling for the entire range of simulated motion. A programmed motion sequence was used to move the transducer during image acquisition and a cine video was acquired for one minute to allow for long sequence tracking. Images were analyzed using a normalized cross-correlation block matching tracking algorithm and compared to the known motion of the transducer relative to the phantom. Results: The setup produced stable ultrasound motion traces consistent with those programmed into the 3D motion stage. The acquired ultrasound images showed minimal artifacts and an image quality that was more than suitable for tracking algorithm verification. Comparisons of a block matching tracking algorithm with the known motion trace for the three features resulted in an average tracking error of 0.59 mm. Conclusion: The high accuracy and programmability of the 4D phantom allows for the acquisition of ultrasound motion sequences that are highly customizable; allowing for focused analysis of some common pitfalls of tracking algorithms such as partial feature occlusion or feature disappearance, among others. The design can easily be modified to adapt to any probe such that the process can be extended to 3D acquisition. Further development of an anatomy specific phantom better resembling true anatomical landmarks could lead to an even more robust validation. This work is partially funded by NIH grant R01CA190298.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Resolution recovery for Compton camera using origin ensemble algorithm

Purpose: Compton cameras (CCs) use electronic collimation to reconstruct the images of activity distribution. Although this approach can greatly improve imaging efficiency, due to complex geometry of the CC principle, image reconstruction with the standard iterative algorithms, such as ordered subset expectation maximization (OSEM), can be very time-consuming, even more so if resolution recovery (RR) is implemented. We have previously shown that the origin ensemble (OE) algorithm can be used for the reconstruction of the CC data. Here we propose a method of extending our OE algorithm to include RR. Methods: To validate the proposed algorithm we used Monte Carlo simulations of a CC composed of multiple layers of pixelated CZT detectors and designed for imaging small animals. A series of CC acquisitions of small hot spheres and the Derenzo phantom placed in air were simulated. Images obtained from (a) the exact data, (b) blurred data but reconstructed without resolution recovery, and (c) blurred and reconstructed with resolution recovery were compared. Furthermore, the reconstructed contrast-to-background ratios were investigated using the phantom with nine spheres placed in a hot background. Results: Our simulations demonstrate that the proposed method allows for the recovery of the resolution loss that is due to imperfect accuracy of event detection. Additionally, tests of camera sensitivity corresponding to different detector configurations demonstrate that the proposed CC design has sensitivity comparable to PET. When the same number of events were considered, the computation time per iteration increased only by a factor of 2 when OE reconstruction with the resolution recovery correction was performed relative to the original OE algorithm. We estimate that the addition of resolution recovery to the OSEM would increase reconstruction times by 2–3 orders of magnitude per iteration. Conclusions: The results of our tests demonstrate the improvement of image resolution provided by the OE reconstructions with resolution recovery. The quality of images and their contrast are similar to those obtained from the OE reconstructions from scans simulated with perfect energy and spatial resolutions.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

A Scalable O(N) Algorithm for Large-Scale Parallel First-Principles Molecular Dynamics Simulations

Traditional algorithms for first-principles molecular dynamics (FPMD) simulations only gain a modest capability increase from current petascale computers, due to their O(N 3 ) complexity and their heavy use of global communications. To address this issue, we are developing a truly scalable O(N) complexity FPMD algorithm, based on density functional theory (DFT), which avoids global communications. The computational model uses a general nonorthogonal orbital formulation for the DFT energy functional, which requires knowledge of selected elements of the inverse of the associated overlap matrix. We present a scalable algorithm for approximately computing selected entries of the inverse of the overlap matrix, based on an approximate inverse technique, by inverting local blocks corresponding to principal submatrices of the global overlap matrix. The new FPMD algorithm exploits sparsity and uses nearest neighbor communication to provide a computational scheme capable of extreme scalability. Accuracy is controlled by the mesh spacing of the finite difference discretization, the size of the localization regions in which the electronic orbitals are confined, and a cutoff beyond which the entries of the overlap matrix can be omitted when computing selected entries of its inverse. We demonstrate the algorithm's excellent parallel scaling for up to O(100K) atoms on O(100K) processors, with a wall-clock time of O(1) minute per molecular dynamics time step.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Generating Massive Scale-free Networks: Novel Parallel Algorithms using the Preferential Attachment Model

Recently, there has been substantial interest in the study of various random networks as mathematical models of complex systems. As real-life complex systems grow larger, the ability to generate progressively large random networks becomes all the more important. This motivates the need for efficient parallel algorithms for generating such networks. Naïve parallelization of sequential algorithms for generating random networks is inefficient due to inherent dependencies among the edges and the possibility of creating duplicate (parallel) edges. In this article, we present message passing interface-based distributed memory parallel algorithms for generating random scale-free networks using the preferential-attachment model. Our algorithms are experimentally verified to scale very well to a large number of processing elements (PEs), providing near-linear speedups. The algorithms have been exercised with regard to scale and speed to generate scale-free networks with one trillion edges in 6 minutes using 1,000 PEs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Resiliency in numerical algorithm design for extreme scale simulations

Here this work is based on the seminar titled ‘Resiliency in Numerical Algorithm Design for Extreme Scale Simulations’ held March 1–6, 2020, at Schloss Dagstuhl, that was attended by all the authors. Advanced supercomputing is characterized by very high computation speeds at the cost of involving an enormous amount of resources and costs. A typical large-scale computation running for 48 h on a system consuming 20 MW, as predicted for exascale systems, would consume a million kWh, corresponding to about 100k Euro in energy cost for executing 10 23 floating-point operations. It is clearly unacceptable to lose the whole computation if any of the several million parallel processes fails during the execution. Moreover, if a single operation suffers from a bit-flip error, should the whole computation be declared invalid? What about the notion of reproducibility itself: should this core paradigm of science be revised and refined for results that are obtained by large-scale simulation? Naive versions of conventional resilience techniques will not scale to the exascale regime: with a main memory footprint of tens of Petabytes, synchronously writing checkpoint data all the way to background storage at frequent intervals will create intolerable overheads in runtime and energy consumption. Forecasts show that the mean time between failures could be lower than the time to recover from such a checkpoint, so that large calculations at scale might not make any progress if robust alternatives are not investigated. More advanced resilience techniques must be devised. The key may lie in exploiting both advanced system features as well as specific application knowledge. Research will face two essential questions: (1) what are the reliability requirements for a particular computation and (2) how do we best design the algorithms and software to meet these requirements? While the analysis of use cases can help understand the particular reliability requirements, the construction of remedies is currently wide open. One avenue would be to refine and improve on system- or application-level checkpointing and rollback strategies in the case an error is detected. Developers might use fault notification interfaces and flexible runtime systems to respond to node failures in an application-dependent fashion. Novel numerical algorithms or more stochastic computational approaches may be required to meet accuracy requirements in the face of undetectable soft errors. These ideas constituted an essential topic of the seminar. The goal of this Dagstuhl Seminar was to bring together a diverse group of scientists with expertise in exascale computing to discuss novel ways to make applications resilient against detected and undetected faults. In particular, participants explored the role that algorithms and applications play in the holistic approach needed to tackle this challenge. This article gathers a broad range of perspectives on the role of algorithms, applications and systems in achieving resilience for extreme scale simulations. The ultimate goal is to spark novel ideas and encourage the development of concrete solutions for achieving such resilience holistically.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Quantum cluster algorithm for data classification

Abstract We present a quantum algorithm for data classification based on the nearest-neighbor learning algorithm. The classification algorithm is divided into two steps: Firstly, data in the same class is divided into smaller groups with sublabels assisting building boundaries between data with different labels. Secondly we construct a quantum circuit for classification that contains multi control gates. The algorithm is easy to implement and efficient in predicting the labels of test data. To illustrate the power and efficiency of this approach, we construct the phase transition diagram for the metal-insulator transition of VO 2 , using limited trained experimental data, where VO 2 is a typical strongly correlated electron materials, and the metallic-insulating phase transition has drawn much attention in condensed matter physics. Moreover, we demonstrate our algorithm on the classification of randomly generated data and the classification of entanglement for various Werner states, where the training sets can not be divided by a single curve, instead, more than one curves are required to separate them apart perfectly. Our preliminary result shows considerable potential for various classification problems, particularly for constructing different phases in materials.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

An Evolutionary Algorithm to Personalize Stool-Based Colorectal Cancer Screening

Fecal immunochemical testing (FIT) is an established method for colorectal cancer (CRC) screening. Measured FIT-concentrations are associated with both present and future risk of CRC, and may be used for personalized screening. However, evaluation of personalized screening is computationally challenging. In this study, a broadly applicable algorithm is presented to efficiently optimize personalized screening policies that prescribe screening intervals and FIT-cutoffs, based on age and FIT-history. We present a mathematical framework for personalized screening policies and a bi-objective evolutionary algorithm that identifies policies with minimal costs and maximal health benefits. The algorithm is combined with an established microsimulation model (MISCAN-Colon), to accurately estimate the costs and benefits of generated policies, without restrictive Markov assumptions. The performance of the algorithm is demonstrated in three experiments. In Experiment 1, a relatively small benchmark problem, the optimal policies were known. The algorithm approached the maximum feasible benefits with a relative difference of 0.007%. Experiment 2 optimized both intervals and cutoffs, Experiment 3 optimized cutoffs only. Optimal policies in both experiments are unknown. Compared to policies recently evaluated for the USPSTF, personalized screening increased health benefits up to 14 and 4.3%, for Experiments 2 and 3, respectively, without adding costs. Generated policies have several features concordant with current screening recommendations. The method presented in this paper is flexible and capable of optimizing personalized screening policies evaluated with computationally-intensive but established simulation models. It can be used to inform screening policies for CRC or other diseases. For CRC, more debate is needed on what features a policy needs to exhibit to make it suitable for implementation in practice.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

A Compressed Sensing-based Image Reconstruction Algorithm for Solar Flare X-Ray Observations

One way of imaging X-ray emission from solar flares is to measure Fourier components of the spatial X-ray source distribution. We present a new compressed sensing-based algorithm named VIS-CS, which reconstructs the spatial distribution from such Fourier components. We demonstrate the application of the algorithm on synthetic and observed solar flare X-ray data from the Reuven Ramaty High Energy Solar Spectroscopic Imager satellite and compare its performance with existing algorithms. VIS-CS produces competitive results with accurate photometry and morphology, without requiring any algorithm- and X-ray-source-specific parameter tuning. Its robustness and performance make this algorithm ideally suited for the generation of quicklook images or large image cubes without user intervention, such as for imaging spectroscopy analysis.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2012

Cloud Screening and Quality Control Algorithm for Star Photometer Data: Assessment with Lidar Measurements and with All-sky Images

This paper presents the development and set up of a cloud screening and data quality control algorithm for a star photometer based on CCD camera as detector. These algorithms are necessary for passive remote sensing techniques to retrieve the columnar aerosol optical depth, delta Ae(lambda), and precipitable water vapor content, W, at nighttime. This cloud screening procedure consists of calculating moving averages of delta Ae() and W under different time-windows combined with a procedure for detecting outliers. Additionally, to avoid undesirable Ae(lambda) and W fluctuations caused by the atmospheric turbulence, the data are averaged on 30 min. The algorithm is applied to the star photometer deployed in the city of Granada (37.16 N, 3.60 W, 680 ma.s.l.; South-East of Spain) for the measurements acquired between March 2007 and September 2009. The algorithm is evaluated with correlative measurements registered by a lidar system and also with all-sky images obtained at the sunset and sunrise of the previous and following days. Promising results are obtained detecting cloud-affected data. Additionally, the cloud screening algorithm has been evaluated under different aerosol conditions including Saharan dust intrusion, biomass burning and pollution events.

data quality control algorithm↗

NASA NTRS2018

Comparison of First-Come First-Served and Optimization Based Scheduling Algorithms for Integrated Departure and Arrival Management

Korea Aerospace Research Institute (KARI) and National Aeronautics and Space Administration (NASA) are investigating scheduling algorithms that will be a part of an integrated arrival and departure management system. Inha University, one of the Korean collaborators of KARI, developed an Extended First-Come First-Served (EFCFS) algorithm that is robust and efficient. However, since the EFCFS algorithm sequentially computes the schedule based on priority, the end results may not be optimal for system efficiency. The approach based on Mixed Integer Linear Programming (MILP) originally developed by NASA and modified by KARI is known to produce better schedules at the expense of computational cost. In this paper, the two different scheduling approaches are compared using common traffic scenarios and constraints at Incheon International Airport. Capabilities to apply weight class based wake turbulence runway separation minima and Miles-in-Trail (MIT) restrictions at selected meter fixes are added to the previously developed EFCFS scheduler. Based on historic data, 40 departures and 20 arrivals are chosen in a one-hour period and 100 scenarios were created by randomly assigning gate numbers, gate departure times, and runway landing times. With the current runway separation requirements, MILP resulted in about ten to twenty percent smaller average delays depending on the constraints. With artificially increased separation minima, the difference between MILP and EFCFS became more noticeable. However, the EFCFS was about ten times faster with smaller variations among different scenarios and constraints. The comparison suggests that the MILP-based algorithm has a small advantage at the current traffic level; however, has potential to be more effective in higher demand or severe weather situations. The EFCFS algorithm may be better suited for real-time applications or investigating larger scale scheduling problems.

air traffic optimization↗

NASA NTRS2020

Genetic Algorithm for Optimization of Neural Networks for Bayesian Inference of Model Uncertainty

The objective of this work was to develop a genetic optimization algorithm that can design a neural network capable of producing uncertainty estimates along with predictions. This algorithm is necessary because the inclusion of uncertainty modeling in a neural network greatly complicates the network’s design space, making the development of a converging model extremely difficult and time consuming. The genetic algorithm presented in this work uses a number of value ranges for various configurable neural network parameters to create a randomly generated population of network architectures. The initially generated population is then evolved over the course of several generations, with the best performing models breeding to produce novel network configurations. Mutations are randomly applied to the network designs to facilitate the development of adaptations beneficial to the task being performed. An experiment was conducted to validate the proposed algorithm, in which the genetic optimizer was tasked with producing a neural network capable of predicting the sound pressure level (SPL) resulting from jet-surface interaction (JSI) noise. The data used for this task was generated at the NASA Glenn Research Center in the Aero-Acoustic Propulsion Laboratory. Starting with an initial population size of 35 randomly generated networks, and evolved over the course of 10 generations, the genetic algorithm produced a design able to predict SPL as a result of JSI noise within 0.272 dB, on average.

Genetic algorithm↗

NASA NTRSDate not supplied

Verifying Autonomous Air Traffic Algorithms in the Presence of Sensor Error and Flight Perturbations

We offer a method to verify algorithms for air traffic control in the presence of sensor errors and flight perturbations. Since errors and perturbations are described by stochastic processes, verification by analytical methods is difficult. We turn to Monte Carlo simulation, but there are two major obstacles. Since air traffic algorithms are safety-critical, the algorithm must be established at a very high confidence level which requires an enormous number of trials. Another is that a simulation attempts to show that an algorithm correctly handles a hazard that might appear, but there is a lack of information about the frequency of occurrence of hazards in the airspace. This paper offers a solution to these and other obstacles. It recognizes there may be a number of algorithms that address different hazards, and the results of the different simulations must be combined to satisfy a global reliability requirement at a high confidence level. To demonstrate the feasibility of the approach, we apply it to an example, an initial effort in collision avoidance.

verification↗

NASA NTRSDate not supplied

Quantum Distributed Algorithms for Approximate Steiner Trees and Directed Minimum Spanning Trees

We present two algorithms in the Quantum CONGEST- CLIQUE model of distributed computation that succeed with high probability; one for producing an approximately optimal Steiner Tree, and one for producing an exact directed minimum spanning tree, each of which uses O ̃(n1/4) rounds of communication and O ̃(n9/4) messages, achieving a lower asymptotic round and message complexity than any known algorithms in the classical CONGEST-CLIQUE model. At a high level, we achieve these results by combining classical algorithms with fast quantum subroutines. Additionally, we characterize the constants and logarithmic factors involved in our algorithms, as well as related classical algorithms, revealing that advances are needed to render both practical.

quantum computing↗

NASA NTRSDate not supplied

Quantum Distributed Algorithms for Approximate Steiner Trees and Directed Minimum Spanning Trees

We present two algorithms in the Quantum CONGEST- CLIQUE model of distributed computation that succeed with high probability; one for producing an approximately optimal Steiner Tree, and one for producing an exact directed minimum spanning tree, each of which uses O ̃(n 1/4 ) rounds of communication and O ̃(n 9/4 ) messages, achieving a lower asymptotic round and message complexity than any known algorithms in the classical CONGEST-CLIQUE model. At a high level, we achieve these results by combining classical algorithms with fast quantum subroutines. Additionally, we characterize the constants and logarithmic factors involved in our algorithms, as well as related classical algorithms, revealing that advances are needed to render both practical.

quantum computing↗

DOE OSTI2023

A Reproducible Validation of Algorithms for Estimating Array Tilt and Azimuth from Photovoltaic Power Time Series

In this research, we assess the viability of four different, publicly available algorithms for estimating the azimuth and tilt parameters of solar photovoltaic systems using only the associated AC power time series data and site latitude-longitude coordinates. In this work, we curated a benchmarking data set of 44 fixed-tilt systems, comprising 275 measured AC power inverter data streams, with known azimuth and tilt parameters. Additionally, we isolated test cases in the data set with real-world issues, including shading and clipping, to determine how algorithm performance varies based on the presence of these phenomena. Using this data set for benchmarking, we evaluated the estimated vs. actual system characteristics for each algorithm, as well as the associated algorithm execution time using a standardized benchmarking process. The two highest performing algorithms were the Solar Data Tools and the PVWatts 5-based methods, which both achieved a median absolute error of approximately 5 and 1 degrees for azimuth and tilt, respectively. During run time analysis, the SDT method was approximately 5 times faster than the PVWatts 5-based method, with the median execution time for a stream varying between 6 and 8 seconds vs. a median run time of 31 seconds for the PVWatts 5-based method.

algorithm validation↗