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At least 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Data-Driven Affinely Adjustable Robust Volt/VAr Control

Recent years have seen the increasing proliferation of distributed energy resources with intermittent power outputs, posing new challenges to the voltage management in distribution networks. To this end, this paper proposes a data-driven affinely adjustable robust Volt/VAr control (AARVVC) scheme, which modulates the smart inverter’s reactive power in an affine function of its active power, based on the voltage sensitivities with respect to real/reactive power injections. To achieve a fast and accurate estimation of voltage sensitivities, we propose a data-driven method based on deep neural network (DNN), together with a rule-based bus-selection process using the bidirectional search method. Our method only uses the operating statuses of selected buses as inputs to DNN, thus significantly improving the training efficiency and reducing information redundancy. Finally, a distributed consensus-based solution, based on the alternating direction method of multipliers (ADMM), for the AARVVC is applied to decide the inverter’s reactive power adjustment rule with respect to its active power. Only limited information exchange is required between each local agent and the central agent to obtain the slope of the reactive power adjustment rule, and there is no need for the central agent to solve any (sub)optimization problems. Finally, numerical results on the modified IEEE-123 bus system validate the effectiveness and superiority of the proposed data-driven AARVVC method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Robust Decentralized Learning Using ADMM With Unreliable Agents

Many signal processing and machine learning problems can be formulated as consensus optimization problems which can be solved efficiently via a cooperative multi-agent system. However, the agents in the system can be unreliable due to a variety of reasons: noise, faults and attacks. Providing erroneous updates leads the optimization process in a wrong direction, and degrades the performance of distributed machine learning algorithms. This paper considers the problem of decentralized learning using ADMM in the presence of unreliable agents. First, we rigorously analyze the effect of erroneous updates (in ADMM learning iterations) on the convergence behavior of the multi-agent system. We show that the algorithm linearly converges to a neighborhood of the optimal solution under certain conditions and characterize the neighborhood size analytically. Next, we provide guidelines for network design to achieve a faster convergence to the neighborhood. Here, we also provide conditions on the erroneous updates for exact convergence to the optimal solution. Finally, to mitigate the influence of unreliable agents, we propose ROAD , a robust variant of ADMM, and show its resilience to unreliable agents with an exact convergence to the optimum.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

A Two-Stage Kalman Filter Approach for Robust and Real-Time Power System State Estimation

As electricity demand continues to grow and renewable energy increases its penetration in the power grid, realtime state estimation becomes essential for system monitoring and control. Recent development in phasor technology makes it possible with high-speed time-synchronized data provided by Phasor Measurement Units (PMU). In this paper we present a two-stage Kalman filter approach to estimate the static state of voltage magnitudes and phase angles, as well as the dynamic state of generator rotor angles and speeds. Kalman filters achieve optimal performance only when the system noise characteristics have known statistical properties (zero-mean, Gaussian, and spectrally white). However in practice the process and measurement noise models are usually difficult to obtain. Thus we have developed the Adaptive Kalman Filter with Inflatable Noise Variances (AKF with InNoVa), an algorithm that can efficiently identify and reduce the impact of incorrect system modeling and/or erroneous measurements. In stage one, we estimate the static state from raw PMU measurements using the AKF with InNoVa; then in stage two, the estimated static state is fed into an extended Kalman filter to estimate the dynamic state. Simulations demonstrate its robustness to sudden changes of system dynamics and erroneous measurements.

Zhang, Jinghe↗
DOE OSTI2015

Robustness of Meteorological Droughts in Dynamically Downscaled Climate Simulations

Abstract We examine the robustness of a suite of regional climate models (RCMs) in simulating meteorological droughts and associated metrics in present‐day climate (1971‐2003) over the conterminous United States (U.S.). The RCMs that are part of North American Regional Climate Change Assessment Program (NARCCAP) simulations are compared with multiple observations over the climatologically homogeneous regions of the U.S. The seasonal precipitation, climatology, drought attributes, and trends have been assessed. The reanalysis‐based multi‐model median RCM reasonably simulates observed statistical attributes of drought and the regional detail due to topographic forcing. However, models fail to simulate significant drying trend over the Southwest and West. Further, reanalysis‐based NARCCAP runs underestimate the observed drought frequency overall, with the exception of the Southwest; whereas they underestimate persistence in the drought‐affected areas over the Southwest and West‐North Central regions. However, global climate model‐driven NARCCAP ensembles tend to overestimate regional drought frequencies. Models exhibit considerable uncertainties while reproducing meteorological drought statistics, as evidenced by a general lack of agreement in the Hurst exponent, which in turn controls drought persistence. Water resources managers need to be aware of the limitations of current climate models, while regional climate modelers may want to fine‐tune their parameters to address impact‐relevant metrics.

Ganguli, Poulomi↗
DOE OSTI2019

A Selective Overview and Comparison of Robust Mixture Regression Estimators

Summary Mixture regression models have been widely used in business, marketing and social sciences to model mixed regression relationships arising from a clustered and thus heterogeneous population. The unknown mixture regression parameters are usually estimated by maximum likelihood estimators using the expectation–maximisation algorithm based on the normality assumption of component error density. However, it is well known that the normality‐based maximum likelihood estimation is very sensitive to outliers or heavy‐tailed error distributions. This paper aims to give a selective overview of the recently proposed robust mixture regression methods and compare their performance using simulation studies.

Yu, Chun↗
DOE OSTI2016

Response of precipitation extremes to idealized global warming in an aqua-planet climate model: towards a robust projection across different horizontal resolutions

Current climate models produce quite heterogeneous projections for the responses of precipitation extremes to future climate change. To help understand the range of projections from multimodel ensembles, a series of idealized 'aquaplanet' Atmospheric General Circulation Model (AGCM) runs have been performed with the Community Atmosphere Model CAM3. These runs have been analysed to identify the effects of horizontal resolution on precipitation extreme projections under two simple global warming scenarios. We adopt the aquaplanet framework for our simulations to remove any sensitivity to the spatial resolution of external inputs and to focus on the roles of model physics and dynamics. Results show that a uniform increase of sea surface temperature (SST) and an increase of low-to-high latitude SST gradient both lead to increase of precipitation and precipitation extremes for most latitudes. The perturbed SSTs generally have stronger impacts on precipitation extremes than on mean precipitation. Horizontal model resolution strongly affects the global warming signals in the extreme precipitation in tropical and subtropical regions but not in high latitude regions. This study illustrates that the effects of horizontal resolution have to be taken into account to develop more robust projections of precipitation extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Quantitative ROS bioreporters: A robust toolkit for studying biological roles of ROS in response to abiotic and biotic stresses

While the accumulation of reactive oxygen species (ROS) through spontaneous generation or as the by-products of aerobic metabolism can be toxic to plants, recent findings demonstrate that ROS act as signaling molecules that play a critical role in adapting to various stress conditions. Tight regulation of ROS homeostasis is required to adapt to stress and survive, yet in vivo spatiotemporal information of ROS dynamics are still largely undefined. In order to understand the dynamics of ROS changes and their biological function in adapting to stresses, two quantitative ROS transcription-based bioreporters were developed. These reporters use ROS-responsive promoters from RBOHD or ZAT12 to drive green fluorescent protein (GFP) expression. The resulting GFP expression is compared to a constitutively expressed mCherry that is contained on the same cassette with the ROS-responsive promoter: This allows for the generation of ratiometric images comparing ROS changes (GFP) to the constitutively expressed mCherry. Both reporters were used to assess ROS levels to oxidative stress, salt stress, and the pathogen defense elicitor flg22. These bioreporters showed increases in the ratio values of GFP to mCherry signals between 10 and 30 min poststress application. Such stress-associated ROS signals correlated with the induction of abiotic/biotic stress responsive markers such as RbohD, ZAT12, SOS2 and PR5 suggesting these ROS bioreporters provide a robust indicator of increased ROS related to stress responses. Based upon the spatiotemporal response patterns of signal increase, ZAT12 promoter-dependent ROS (Zat12p-ROS) bioreporter appears to be suitable for cellular mapping of ROS changes in response to abiotic and biotic stresses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Evaluating Model Robustness for Defect Identification and Classification in a Composite Aerostructure Material

Aircraft structures are required to have a high level of quality to satisfy their need for light weight, efficient flight, and withstanding high loads over their lifespan. These aerostructures are typically made from a composite material due to their good tensile strength and resistance to compression. To ensure their structural integrity, the composite material requires inspection for common flaws such as porosity, delaminations, voids, foreign object debris, and other defects. Ultrasonic testing (UT) is a popular non-destructive inspection (NDI) technique used for effectively evaluating the composite material. Current inspection methods rely heavily on human experience and are extremely time consuming. Therefore, there is a need for the development of techniques to reduce the manual inspection time. This work compares the performance of different deep learning-based methods in the identification and classification of defects. Deep learning has shown great promise in numerous fields, and we show its effectiveness in the evaluation of the composite aerostructure material. The methods developed here are both highly reliable with a top recall value of 98.64% as well as extremely efficient requiring an average of 4 s during the inferencing stage to evaluate new composites. Lastly, we investigate model robustness to concept drift by measuring its performance over time.

36 MATERIALS SCIENCE↗
DOE OSTI2013

Tailoring of the care concept for practicality, safety and robustness

The Cavern Retrievable (CARE) concept was originally developed to provide flexibility in tailoring disposal of vitrified HLW to small volunteer host sites, where high emplacement density leads to significant challenges in terms of the management of radiogenic heat. Including an extended storage period in underground caverns prior to sealing solved the heat issue, but also provided benefits in terms of a slow decision-making process that provided more opportunities to engage stakeholders. Recently, direct disposal of spent fuel is being considered in Japan and this gives more severe challenges for heat management, even for conventional disposal concepts. Due to great uncertainties in the future of nuclear power in Japan, this may also be associated with a desire to preserve the option of retrieval for extended periods. The basic CARE concept has thus been re-assessed to consider these aspects - and also the need to be able to develop a robust safety case that can be readily communicated to stakeholders, who are more sensitive to nuclear issues than they were in the past. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2013

Evaluation of the robustness of the preprocessing technique improving reversible compressibility of CT images: Tested on various CT examinations

Purpose: To modify the preprocessing technique, which was previously proposed, improving compressibility of computed tomography (CT) images to cover the diversity of three dimensional configurations of different body parts and to evaluate the robustness of the technique in terms of segmentation correctness and increase in reversible compression ratio (CR) for various CT examinations.Methods: This study had institutional review board approval with waiver of informed patient consent. A preprocessing technique was previously proposed to improve the compressibility of CT images by replacing pixel values outside the body region with a constant value resulting in maximizing data redundancy. Since the technique was developed aiming at only chest CT images, the authors modified the segmentation method to cover the diversity of three dimensional configurations of different body parts. The modified version was evaluated as follows. In randomly selected 368 CT examinations (352 787 images), each image was preprocessed by using the modified preprocessing technique. Radiologists visually confirmed whether the segmented region covers the body region or not. The images with and without the preprocessing were reversibly compressed using Joint Photographic Experts Group (JPEG), JPEG2000 two-dimensional (2D), and JPEG2000 three-dimensional (3D) compressions. The percentage increase in CR per examination (CR{sub I}) was measured.Results: The rate of correct segmentation was 100.0% (95% CI: 99.9%, 100.0%) for all the examinations. The median of CR{sub I} were 26.1% (95% CI: 24.9%, 27.1%), 40.2% (38.5%, 41.1%), and 34.5% (32.7%, 36.2%) in JPEG, JPEG2000 2D, and JPEG2000 3D, respectively.Conclusions: In various CT examinations, the modified preprocessing technique can increase in the CR by 25% or more without concerning about degradation of diagnostic information.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Poster — Thur Eve — 44: Linearization of Compartmental Models for More Robust Estimates of Regional Hemodynamic, Metabolic and Functional Parameters using DCE-CT/PET Imaging

Quantitative analysis of dynamic positron emission tomography (PET) data usually involves minimizing a cost function with nonlinear regression, wherein the choice of starting parameter values and the presence of local minima affect the bias and variability of the estimated kinetic parameters. These nonlinear methods can also require lengthy computation time, making them unsuitable for use in clinical settings. Kinetic modeling of PET aims to estimate the rate parameter k{sub 3}, which is the binding affinity of the tracer to a biological process of interest and is highly susceptible to noise inherent in PET image acquisition. We have developed linearized kinetic models for kinetic analysis of dynamic contrast enhanced computed tomography (DCE-CT)/PET imaging, including a 2-compartment model for DCE-CT and a 3-compartment model for PET. Use of kinetic parameters estimated from DCE-CT can stabilize the kinetic analysis of dynamic PET data, allowing for more robust estimation of k{sub 3}. Furthermore, these linearized models are solved with a non-negative least squares algorithm and together they provide other advantages including: 1) only one possible solution and they do not require a choice of starting parameter values, 2) parameter estimates are comparable in accuracy to those from nonlinear models, 3) significantly reduced computational time. Our simulated data show that when blood volume and permeability are estimated with DCE-CT, the bias of k{sub 3} estimation with our linearized model is 1.97 ± 38.5% for 1,000 runs with a signal-to-noise ratio of 10. In summary, we have developed a computationally efficient technique for accurate estimation of k{sub 3} from noisy dynamic PET data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Poster — Thur Eve — 66: Robustness Assessment of a Novel IMRT Planning Method for Lung Radiotherapy

Conventional radiotherapy treatment planning for lung cancer accounts for tumour motion by increasing the beam apertures. We recently developed an IMRT planning strategy which uses reduced beam apertures in combination with an edge enhancing boost of 110% of the prescription dose to the volume that corresponds to the portion of the CTV that moves outside of the reduced beam. Previous results showed that this approach ensures target coverage while reducing lung dose. In the current study, we evaluate the robustness of this boost volume approach to changes in respiratory motion, including amplitude and phase weight variations. ITV and boost volume plans were generated for 5 NSCLC patients with respiratory motion amplitudes ranging from 1 to 2 cm. A standard 5mm PTV margin was used for all plans. The ORBIT treatment planning tool was used to plan and accumulate dose over 10 respiratory phases defined by the 4DCT datasets. For the phase weight variation study, dose was accumulated for three scenarios: equally-weighted-phases, higher weight assigned to exhale phases and higher weight assigned to inhale phases. For the amplitude variation study, a numerical phantom was used to generate 4DCT datasets corresponding to 7 mm, 10 mm and 14 mm motion amplitudes. Preliminary results found that delivered plans for all phase weight scenarios were clinically acceptable. When normalized to mean lung dose, the boost volume plan delivered 5% more dose to the CTV which indicates the potential for dose escalation using this approach.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-603: PBS Prostate Plan Robustness: A Tool for Patient Specific Setup Tolerance

Purpose: Fiducial markers are commonly used for setup of prostate patients using orthogonal radiographs. After aligned with the markers, the displacement of the bony anatomy relative to the planned DRR can be up to 10 mm. Such offset can potentially have significant dosimetric effects because it changes the radiological path length of protons in differing amounts of bone. It is imperative to develop a method to evaluate its impact on target coverage and hence establish patient specific setup tolerance for prostate proton PBS treatment. Methods: Prostate patients were planned in RayStation according to the PCG protocol with bi-lateral beams. The primary planning objectives are: (1) 100% of CTV receives full prescription dose; (2) 98% of the prescription dose covers at least 98% of the PTV; (3) OARs meet criteria per protocol. For each patient 108 dose perturbations were automatically generated using an in-house script, which considered the isocenter shifting in S-I and A-P directions (up to ±15 mm with an interval of 6mm) as well as the range uncertainty (±3.5%). The target coverage was evaluated on the contour shifted along with prostate to mimic the daily treatment. Results: The minimum CTV coverage as a function of offsets in S-I and A-P directions is presented in a 2D contour map. The offsets along A-P direction generally have greater impact than along S-I direction. Both the CTV D98%>98% or CTV V98%>98% are achievable for most patients if the offset is <10 mm in either direction despite of range uncertainties. Conclusion: We developed a method to evaluate the plan robustness for proton PBS prostate treatment. It can provide patient specific setup tolerance of bony structure offset. For our current planning approach, a 1 cm displacement is acceptable. This approach can be generalized to other target structures that move relative to bony anatomy.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2015

SU-E-T-657: Quantitative Assessment of Plan Robustness for Helical Tomotherapy for Head and Neck Cancer Radiotherapy

Introduction: Geometric uncertainties in daily patient setup can lead to variations in the planned dose, especially when using highly conformal techniques such as helical Tomotherapy. To account for the potential effect of geometric uncertainty, our clinical practice is to expand critical structures by 3mm expansion into planning risk volumes (PRV). The PRV concept assumes the spatial dose cloud is insensitive to patient positioning. However, no tools currently exist to determine if a Tomotherapy plan is robust to the effects of daily setup variation. We objectively quantified the impact of geometric uncertainties on the 3D doses to critical normal tissues during helical Tomotherapy. Methods: Using a Matlab-based program created and validated by Accuray (Madison, WI), the planned Tomotherapy delivery sinogram recalculated dose on shifted CT datasets. Ten head and neck patients were selected for analysis. To simulate setup uncertainty, the patient anatomy was shifted ±3mm in the longitudinal, lateral and vertical axes. For each potential shift, the recalculated doses to various critical normal tissues were compared to the doses delivered to the PRV in the original plan Results: 18 shifted scenarios created from Tomotherapy plans for three patients with head and neck cancers were analyzed. For all simulated setup errors, the maximum doses to the brainstem, spinal cord, parotids and cochlea were no greater than 0.6Gy of the respective original PRV maximum. Despite 3mm setup shifts, the minimum dose delivered to 95% of the CTVs and PTVs were always within 0.4Gy of the original plan. Conclusions: For head and neck sites treated with Tomotherapy, the use of a 3mm PRV expansion provide a reasonable estimate of the dosimetric effects of 3mm setup uncertainties. Similarly, target coverage appears minimally effected by a 3mm setup uncertainty. Data from a larger number of patients will be presented. Future work will include other anatomical sites.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

SU-F-BRD-06: Robust Dose Calculation in Intensity Modulated Proton Therapy

Purpose: Commissioning data for intensity modulated proton therapy (IMPT) must be post-processed by fits to ad-hoc functions to derive the dose calculation kernel parameters in a treatment planning system (TPS). Whether from experimental measurement or Monte Carlo simulation, the limited and noisy nature of such data makes this task very challenging. We present a method to improve the modeling of the lateral dose distribution of clinical energy proton beams in water to commission an in-house IMPT dose calculation engine. Methods: A linear sum of three Gaussian distribution functions was fitted to the lateral dose data in logarithmic scale. Starting values of fitting solutions were determined from the Generalized Highland Approximation. We exhaustively optimized the combinations of data weights with upper bounds of the fitting solutions to minimize confidence intervals of the fitting solutions while maintaining the coefficient of determination (R{sup 2}). Results: Across all energies, average confidence bounds improved 72.88% [Max: 88.28%, Min: 55.05%] for small angle coulomb scattering, 114.25% [409.13%, 66.72%,] for nuclear scattering, and 68.66% [141.09%, 33.27%] for large angle coulomb scattering, while the coefficients of determination of the fits (R{sup 2}) remained comparable. On average R {sup 2} only changed 0.18% and were very close to 1 (approx. 0.999). Wilcoxon signed rank tests comparing unweighted/unbounded fits with weighted/bounded fits averaged 0.0146 (Max: 0.177, Min: 7.05×10−{sup 7}) for small angle Coulomb, 0.0903 (0.945, 7.05×10−{sup 7}) for nuclear, and 0.254 (0.871, 1.86×10−{sup 6}) for large angle Coulomb scattering. This allows rejection of the null hypothesis for small angle Coulomb scattering at the 0.015 level and nuclear interaction at the 0.1 level. Conclusion: Optimal weights assigned to IMPT lateral dose data minimized fitting to stochastic noise in the tail region. Optimizing the upper bounds of fitting parameters improved the robustness of fitting solutions. Both led to a more physically meaningful dose calculation kernel for the TPS. NIH/NCI K25CA168984, Eagles Cancer Research Career Development, The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, Mayo ASU Seed Grant, and The Kemper Marley Foundation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-F-BRB-07: A Plan Comparison Tool to Ensure Robustness and Deliverability in Online-Adaptive Radiotherapy

Purpose: To develop a plan comparison tool that will ensure robustness and deliverability through analysis of baseline and online-adaptive radiotherapy plans using similarity metrics. Methods: The ViewRay MRIdian treatment planning system allows export of a plan file that contains plan and delivery information. A software tool was developed to read and compare two plans, providing information and metrics to assess their similarity. In addition to performing direct comparisons (e.g. demographics, ROI volumes, number of segments, total beam-on time), the tool computes and presents histograms of derived metrics (e.g. step-and-shoot segment field sizes, segment average leaf gaps). Such metrics were investigated for their ability to predict that an online-adapted plan reasonably similar to a baseline plan where deliverability has already been established. Results: In the realm of online-adaptive planning, comparing ROI volumes offers a sanity check to verify observations found during contouring. Beyond ROI analysis, it has been found that simply editing contours and re-optimizing to adapt treatment can produce a delivery that is substantially different than the baseline plan (e.g. number of segments increased by 31%), with no changes in optimization parameters and only minor changes in anatomy. Currently the tool can quickly identify large omissions or deviations from baseline expectations. As our online-adaptive patient population increases, we will continue to develop and refine quantitative acceptance criteria for adapted plans and relate them historical delivery QA measurements. Conclusion: The plan comparison tool is in clinical use and reports a wide range of comparison metrics, illustrating key differences between two plans. This independent check is accomplished in seconds and can be performed in parallel to other tasks in the online-adaptive workflow. Current use prevents large planning or delivery errors from occurring, and ongoing refinements will lead to increased assurance of plan quality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-383: Robustness for Patient Setup Error in Total Body Irradiation Using Volumetric Modulated Arc Therapy (VMAT)

Purpose: Total body irradiation (TBI) and total marrow irradiation (TMI) using Tomotherapy have been reported. A gantry-based linear accelerator uses one isocenter during one rotational irradiation. Thus, 3–5 isocenter points should be used for a whole plan of TBI-VMAT during smoothing out the junctional dose distribution. IGRT provides accurate and precise patient setup for the multiple junctions, however it is evident that some setup errors should occur and affect accuracy of dose distribution in the area. In this study, we evaluated the robustness for patient’s setup error in VMAT-TBI. Methods: VMAT-TBI Planning was performed in an adult whole-body human phantom using Eclipse. Eight full arcs with four isocenter points using 6MV-X were used to cover the entire whole body. Dose distribution was optimized using two structures of patient’s body as PTV and lung. The two arcs were shared with one isocenter and the two arcs were 5 cm-overlapped with the other two arcs. Point absolute dose using ionization-chamber and planer relative dose distribution using film in the junctional regions were performed using water-equivalent slab phantom. In the measurements, several setup errors of (+5∼−5mm) were added. Results: The result of the chamber measurement shows the deviations were within ±3% when the setup errors were within ±3 mm. In the planer evaluation, the pass ratio of gamma evaluation (3%/2mm) shows more than 90% if the errors within ±3 mm. However, there were hot/cold areas in the edge of the junction even with acceptable gamma pass ratio. 5 mm setup error caused larger hot and cold areas and the dosimetric acceptable areas were decreased in the overlapped areas. Conclusion: It can be clinically acceptable for VMAT-TBI when patient setup error is within ±3mm. Averaging effects from patient random error would be helpful to blur the hot/cold area in the junction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-AB-202-08: A Robust Real-Time Surface Reconstruction Method On Point Clouds Captured From a 3D Surface Photogrammetry System

Purpose: Surface photogrammetry (e.g. VisionRT, C-Rad) provides a noninvasive way to obtain high-frequency measurement for patient motion monitoring in radiotherapy. This work aims to develop a real-time surface reconstruction method on the acquired point clouds, whose acquisitions are subject to noise and missing measurements. In contrast to existing surface reconstruction methods that are usually computationally expensive, the proposed method reconstructs continuous surfaces with comparable accuracy in real-time. Methods: The key idea in our method is to solve and propagate a sparse linear relationship from the point cloud (measurement) manifold to the surface (reconstruction) manifold, taking advantage of the similarity in local geometric topology in both manifolds. With consistent point cloud acquisition, we propose a sparse regression (SR) model to directly approximate the target point cloud as a sparse linear combination from the training set, building the point correspondences by the iterative closest point (ICP) method. To accommodate changing noise levels and/or presence of inconsistent occlusions, we further propose a modified sparse regression (MSR) model to account for the large and sparse error built by ICP, with a Laplacian prior. We evaluated our method on both clinical acquired point clouds under consistent conditions and simulated point clouds with inconsistent occlusions. The reconstruction accuracy was evaluated w.r.t. root-mean-squared-error, by comparing the reconstructed surfaces against those from the variational reconstruction method. Results: On clinical point clouds, both the SR and MSR models achieved sub-millimeter accuracy, with mean reconstruction time reduced from 82.23 seconds to 0.52 seconds and 0.94 seconds, respectively. On simulated point cloud with inconsistent occlusions, the MSR model has demonstrated its advantage in achieving consistent performance despite the introduced occlusions. Conclusion: We have developed a real-time and robust surface reconstruction method on point clouds acquired by photogrammetry systems. It serves an important enabling step for real-time motion tracking in radiotherapy. This work is supported in part by NIH grant R01 CA169102-02.

60 APPLIED LIFE SCIENCES↗