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At least 1,063 records · Page 59

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Entropy and Boundary Based Adversarial Learning for Large Scale Unsupervised Domain Adaptation

Supervised semantic segmentation methods provide state-of-the-art performance, but their performance is limited by the amount of quality labeled data they need for training. Scarcity of labeled data and non-transferablity of models, due to cross-domain discrepancy makes it a bigger challenge for remote sensing imagery analysis. In this work, we approach this problem through adversarial learning, driven by entropy and boundary of region-of-interest for unsupervised domain adaptation. This concept helps with better boundary prediction and encourages target domain entropy maps (probability/uncertainty maps) to be similar to source domains. In particular, we showed that deriving informative entropy through the adversarial learning is essential to enable the adaptation. We used a large scale cross country building extraction dataset to validate the framework. The experimental results show the usefulness of considering boundary and entropy driven adversarial learning for adaptation.

Makkar, Nikhil↗

DOE OSTI2023

Convergence Rate of Model Reference Adaptive Control with Application to Building HVAC Systems

Model reference adaptive control (MRAC) has been studied for decades and successfully applied in multiple areas, including heating, ventilation, and air conditioning (HVAC) systems for buildings. MRAC is efficient in capturing the time-varying characteristics of buildings' indoor temperatures and outdoor weather environments. In this paper, the rate of convergence of MRAC is investigated, where a direct adaptive control with temperature set point reference tracking is used to regulate the indoor temperatures for buildings. Numerical results show that by controlling the HVAC systems of residential buildings using MRAC, the indoor temperatures converge Q-sublinearly to the desired temperature set points. In addition, the rate of convergence for MRAC is compared with a baseline adaptive model-free control method.

Wu, Tumin↗

DOE OSTI2024

Verification of Adaptive Protection in Hardware in the Loop for Coordination with Solar Variability

As inverter-based resources continue to be installed at all levels of the electric grid, the fixed protection schemes used at the distribution level will continue to be stressed until they no longer ensure the protection of the grid. Adaptive protection has been proposed as a solution with the ability to update the protection schemes in near real-time to ensure reliability and increase the resilience of the grid. However, weather variability poses a significant challenge to the ability of these methods to keep the selectivity and reliability of these schemes coordinated. If the ramp rates, due to solar variability, of the inverter-based resources change faster than the adaptive protection can issue new settings, the protection system could be uncoordinated, with the wrong device responding to a system fault. The proposed adaptive protection method ensures that due to solar variability, communication, and protection calculation latency, it can issu e coordinated protection settings promptly, in one minute or less. The hardware-in-the-loop results show the protection settings being issued and maintaining system coordination in under a minute.

Summers, Adam↗

DOE OSTI2022

A Multi-Branch Decoder Network Approach to Adaptive Temporal Data Selection and Reconstruction for Big Scientific Simulation Data

A key challenge in scientific simulation is that the simulation outputs often require intensive I/O and storage space to store the results for effective post hoc analysis. This article focuses on a quality-aware adaptive temporal data selection and reconstruction problem where the goal is to adaptively select simulation data samples at certain key timesteps in situ and reconstruct the discarded samples with quality assurance during post hoc analysis. This problem is motivated by the limitation of current solutions that a significant amount of simulation data samples are either discarded or aggregated during the sampling process, leading to inaccurate modeling of the simulated phenomena. Two unique challenges exist: 1) the sampling decisions have to be made in situ and adapted to the dynamics of the complex scientific simulation data; 2) the reconstruction error must be strictly bounded to meet the application requirement. To address the above challenges, we develop DeepSample , an error-controlled convolutional neural network framework, that jointly integrates a set of coherent multi-branch deep decoders to effectively reconstruct the simulation data with rigorous quality assurance. The results on two real-world scientific simulation applications show that DeepSample significantly outperforms other state-of-the-art methods on both sampling efficiency and reconstructed simulation data quality.

Zhang, Yang↗

DOE OSTI2022

Learning and Fast Adaptation for Grid Emergency Control via Deep Meta Reinforcement Learning

As power systems are undergoing a significant transformation with more uncertainties, less inertia and closer to operation limits, there is increasing risk of large outages. Thus, there is an imperative need to enhance grid emergency control to maintain system reliability and security. Towards this end, great progress has been made in developing deep reinforcement learning (DRL) based grid control solutions in recent years. However, existing DRL-based solutions have two main limitations: 1) they cannot handle well with a wide range of grid operation conditions, system parameters, and contingencies; 2) they generally lack the ability to fast adapt to new grid operation conditions, system parameters, and contingencies, limiting their applicability for real-world applications. Here, in this paper, we mitigate these limitations by developing a novel deep meta-reinforcement learning (DMRL) algorithm. The DMRL combines the meta strategy optimization together with DRL, and trains policies modulated by a latent space that can quickly adapt to new scenarios. We test the developed DMRL algorithm on the IEEE 300-bus system. We demonstrate fast adaptation of the meta-trained DRL polices with latent variables to new operating conditions and scenarios using the proposed method, which achieves superior performance compared to the state-of-the-art DRL and model predictive control (MPC) methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2015

ARMAX-Based Transfer Function Model Identification Using Wide-Area Measurement for Adaptive and Coordinated Damping Control

One of the main drawbacks of the existing oscillation damping controllers that are designed based on offline dynamic models is adaptivity to the power system operating condition. With the increasing availability of wide-area measurements and the rapid development of system identification techniques, it is possible to identify a measurement-based transfer function model online that can be used to tune the oscillation damping controller. Such a model could capture all dominant oscillation modes for adaptive and coordinated oscillation damping control. our paper describes a comprehensive approach to identify a low-order transfer function model of a power system using a multi-input multi-output (MIMO) autoregressive moving average exogenous (ARMAX) model. This methodology consists of five steps: 1) input selection; 2) output selection; 3) identification trigger; 4) model estimation; and 5) model validation. The proposed method is validated by using ambient data and ring-down data in the 16-machine 68-bus Northeast Power Coordinating Council system. Our results demonstrate that the measurement-based model using MIMO ARMAX can capture all the dominant oscillation modes. Compared with the MIMO subspace state space model, the MIMO ARMAX model has equivalent accuracy but lower order and improved computational efficiency. The proposed model can be applied for adaptive and coordinated oscillation damping control.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

CPU Isosurface Ray Tracing of Adaptive Mesh Refinement Data

Adaptive mesh refinement (AMR) is a key technology for large-scale simulations that allows for adaptively changing the simulation mesh resolution, resulting in significant computational and storage savings. However, visualizing such AMR data poses a significant challenge due to the difficulties introduced by the hierarchical representation when reconstructing continuous field values. Here, we detail a comprehensive solution for interactive isosurface rendering of block-structured AMR data. We contribute a novel reconstruction strategy—the octant method—which is continuous, adaptive and simple to implement. Furthermore, we present a generally applicable hybrid implicit isosurface ray-tracing method, which provides better rendering quality and performance than the built-in sampling-based approach in OSPRay. Finally, we integrate our octant method and hybrid isosurface geometry into OSPRay as a module, providing the ability to create high-quality interactive visualizations combining volume and isosurface representations of BS-AMR data. Lastly, we evaluate the rendering performance, memory consumption and quality of our method on two gigascale block-structured AMR datasets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Differential early–life survival underlies the adaptive significance of temperature–dependent sex determination in a long–lived reptile

1. Many ectotherms rely on temperature cues experienced during development to determine offspring sex. The first descriptions of temperature-dependent sex determination (TSD) were made over 50 years ago, yet an understanding of its adaptive significance remains elusive, especially in long-lived taxa. 2. One novel hypothesis predicts that TSD should be evolutionarily favoured when two criteria are met—(a) incubation temperature influences annual juvenile survival and (b) sexes mature at different ages. Under these conditions, a sex-dependent effect of incubation temperature on offspring fitness arises through differences in age at sexual maturity, with the sex that matures later benefiting disproportionately from temperatures that promote juvenile survival. 3. The American alligator (Alligator mississippiensis) serves as an insightful model in which to test this hypothesis, as males begin reproducing nearly a decade after females. Here, through a combination of artificial incubation experiments and mark-recapture approaches, we test the specific predictions of the survival-to-maturity hypothesis for the adaptive value of TSD by disentangling the effects of incubation temperature and sex on annual survival of alligator hatchlings across two geographically distinct sites. 4. Hatchlings incubated at male-promoting temperatures (MPTs) consistently exhibited higher survival compared to those incubated at female-promoting temperatures. This pattern appears independent of hatchling sex, as females produced from hormone manipulation at MPT exhibit similar survival to their male counterparts. 5. Additional experiments show that incubation temperature may affect early-life survival primarily by affecting the efficiency with which maternally transferred energy resources are used during development. 6. Results from this study provide the first explicit empirical support for the adaptive value of TSD in a crocodilian and point to developmental energetics as a potential unifying mechanism underlying persistent survival consequences of incubation temperature.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Which practices co-deliver food security, climate change mitigation and adaptation, and combat land degradation and desertification?

There is a clear need for transformative change in the land management and food production sectors to address the global land challenges of climate change mitigation, climate change adaptation, combatting land degradation and desertification, and delivering food security (referred to hereafter as “land challenges”). We assess the potential for 40 practices to address these land challenges and find that: Nine options deliver medium to large benefits for all four land challenges. A further two options have no global estimates for adaptation, but have medium to large benefits for all other land challenges. Five options have large mitigation potential (>3 Gt CO 2 eq/year) without adverse impacts on the other land challenges. Five options have moderate mitigation potential, with no adverse impacts on the other land challenges. Sixteen practices have large adaptation potential (>25 million people benefit), without adverse side effects on other land challenges. Most practices can be applied without competing for available land. However, seven options could result in competition for land. A large number of practices do not require dedicated land, including several land management options, all value chain options, and all risk management options. Four options could greatly increase competition for land if applied at a large scale, though the impact is scale and context specific, highlighting the need for safeguards to ensure that expansion of land for mitigation does not impact natural systems and food security. A number of practices, such as increased food productivity, dietary change and reduced food loss and waste, can reduce demand for land conversion, thereby potentially freeing-up land and creating opportunities for enhanced implementation of other practices, making them important components of portfolios of practices to address the combined land challenges.

42 ENGINEERING↗

DOE OSTI2020

Predicting soil carbon changes in switchgrass grown on marginal lands under climate change and adaptation strategies

Abstract The United States Great Lakes Region (USGLR) is a critical geographic area for future bioenergy production. Switchgrass ( Panicum virgatum ) is widely considered a carbon (C)‐neutral or C‐negative bioenergy production system, but projected increases in air temperature and precipitation due to climate change might substantially alter soil organic C (SOC) dynamics and storage in soils. This study examined long‐term SOC changes in switchgrass grown on marginal land in the USGLR under current and projected climate, predicted using a process‐based model (Systems Approach to Land‐Use Sustainability) extensively calibrated with a wealth of plant and soil measurements at nine experimental sites. Simulations indicate that these soils are likely a net C sink under switchgrass (average gain 0.87 Mg C ha −1 year −1 ), although substantial variation in the rate of SOC accumulation was predicted (range: 0.2–1.3 Mg C ha −1 year −1 ). Principal component analysis revealed that the predicted intersite variability in SOC sequestration was related in part to differences in climatic characteristics, and to a lesser extent, to heterogeneous soils. Although climate change impacts on switchgrass plant growth were predicted to be small (4%–6% decrease on average), the increased soil respiration was predicted to partially negate SOC accumulations down to 70% below historical rates in the most extreme scenarios. Increasing N fertilizer rate and decreasing harvest intensity both had modest SOC sequestration benefits under projected climate, whereas introducing genotypes better adapted to the longer growing seasons was a much more effective strategy. Best‐performing adaptation scenarios were able to offset >60% of the climate change impacts, leading to SOC sequestration 0.7 Mg C ha −1 year −1 under projected climate. On average, this was 0.3 Mg C ha −1 year −1 more C sequestered than the no adaptation baseline. These findings provide crucial knowledge needed to guide policy and operational management for maximizing SOC sequestration of future bioenergy production on marginal lands in the USGLR.

09 BIOMASS FUELS↗

DOE OSTI2020

Whole genome resequencing reveals genomic regions associated with thermal adaptation in redband trout

Abstract Adaptation to local environments involves evolution of ecologically important traits and underlying physiological processes. Here, we used low coverage whole‐genome resequencing (lcWGR) on individuals to identify genome regions involved in thermal adaptation in wild redband trout Oncorhynchus mykiss gairdneri , a subspecies of rainbow trout that inhabits ecosystems ranging from cold montane forests to high elevation deserts. This study includes allele frequency‐based analyses for selective sweeps among populations, followed by multiple association tests for specific sets of phenotypes measured under thermal stress (acute and chronic survival/mortality; high or low cardiac performance groups). Depending on the groups in each set of analyses, sequencing reads covered 43%–75% of the genome at ≥15× and each analysis included millions of SNPs across the genome. In tests for selective sweeps among populations, a total of six chromosomal regions were significant. The further association tests for specific phenotypes revealed that the region on chromosome 4 was consistently the most significant and contains the cerk gene (ceramide kinase). This study provides insight into a potential genetic mechanism of local thermal adaptation and suggests c erk may be an important candidate gene. However, further validation of this cerk gene is necessary to determine if the association with cardiac performance results in a functional role to influence thermal performance when exposed to high water temperatures and hypoxic conditions.

Chen, Zhongqi↗

DOE OSTI2022

Adaptive management of large-scale ecosystem restoration: increasing certainty of habitat outcomes in the Columbia River Estuary, U.S.A.

Ecological restoration programs in dynamic coastal environments can benefit from adaptive management, including an iterative process for identifying and addressing critical uncertainties. We highlight key developments under the three pillars that have increased the rate of restoration by the Columbia Estuary Ecosystem Restoration Program (CEERP) over 20 years: science, coordination, and management. We show how such programs can be institutionalized to ensure that estuary ecosystems are better understood, conserved, and restored. The principal conservation effort under CEERP is to reconnect historical floodplain wetlands to the mainstem. The program also supports other restoration actions that demonstrate a high potential to benefit ecosystem function and endangered salmon populations, however, there is greater uncertainty regarding these less-utilized techniques. Through adaptive management, we address both technical uncertainty regarding benefits to the environmental resource and programmatic uncertainty pertaining to decision-making. Here, we examine three periods of CEERP growth to establish how complementary research and restoration actions have improved program outcomes over time. We highlight the tools and processes that were developed and integrated into the program to refine program strategy, improve project design, and maximize ecological benefits. CEERP supported 77 restoration projects and reconnected over 7,000 acres of floodplain habitat to the lower Columbia River between 2004 and 2021. Building on these successes, we outline current plans to better engage landowners and local communities, solicit new project types, and maintain enough flexibility within the program to adapt to new priorities.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

General-Surrogate Adaptive Sampling Using Interquartile Range for Design Space Exploration

Abstract A surrogate model is a common tool to approximate system response at untested points for design space exploration. Adaptive sampling has been studied for improving the accuracy of surrogates iteratively by introducing additional samples (simulations and experiments). New samples are often selected based on the estimated uncertainty in the design space. While some surrogates such as kriging have readily available uncertainty models for their predictions, other surrogates do not. Consequently, there have been studies of using the process of leaving-samples-out (LSO) used in cross-validation tools to estimate prediction uncertainty, such as the universal prediction distribution (UPD). In this paper, an adaptive sampling scheme for general surrogates is proposed based on LSO, similar to cross-validation and interquartile range (IQR). Multiple submodels are first developed from LSO. The uncertainty is then estimated from the IQR of these surrogates at a given point. New samples are added iteratively at the point with maximum IQR for design space exploration. The proposed scheme is illustrated using kriging, radial basis function, and neural network surrogates. The proposed scheme is evaluated using four algebraic test functions. Multiple sets of initial samples were produced to account for randomness. For these test functions, the proposed scheme was found to be more accurate and robust than kriging with its own uncertainty model. The proposed scheme was more accurate than the UPD for three out of the four test functions. For a fixed number of samples, the IQR-based adaptive sampling also proved to be more accurate than all-at-once sampling in most cases even when the estimated uncertainty was only mildly correlated with prediction errors.

Engineering↗

DOE OSTI2013

A method for online verification of adapted fields using an independent dose monitor

Purpose: Clinical implementation of online adaptive radiotherapy requires generation of modified fields and a method of dosimetric verification in a short time. We present a method of treatment field modification to account for patient setup error, and an online method of verification using an independent monitoring system.Methods: The fields are modified by translating each multileaf collimator (MLC) defined aperture in the direction of the patient setup error, and magnifying to account for distance variation to the marked isocentre. A modified version of a previously reported online beam monitoring system, the integral quality monitoring (IQM) system, was investigated for validation of adapted fields. The system consists of a large area ion-chamber with a spatial gradient in electrode separation to provide a spatially sensitive signal for each beam segment, mounted below the MLC, and a calculation algorithm to predict the signal. IMRT plans of ten prostate patients have been modified in response to six randomly chosen setup errors in three orthogonal directions.Results: A total of approximately 49 beams for the modified fields were verified by the IQM system, of which 97% of measured IQM signal agree with the predicted value to within 2%.Conclusions: The modified IQM system was found to be suitable for online verification of adapted treatment fields.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2014

Dosimetric adaptive IMRT driven by fiducial points

Purpose: Intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy have become standard treatments but are more sensitive to anatomical variations than 3D conformal techniques. To correct for inter- and intrafraction anatomical variations, fast and easy to implement methods are needed. Here, the authors propose a full dosimetric IMRT correction that finds a compromise in-between basic repositioning (the current clinical practice) and full replanning. It simplifies replanning by avoiding a recontouring step and a full dose calculation. It surpasses repositioning by updating the preoptimized fluence and monitor units (MU) using a limited number of fiducial points and a pretreatment (CB)CT. To adapt the fluence the fiducial points were projected in the beam's eye view (BEV). To adapt the MUs, point dose calculation towards the same fiducial points were performed. The proposed method is intrinsically fast and robust, and simple to understand for operators, because of the use of only four fiducial points and the beam data based point dose calculations. Methods: To perform our dosimetric adaptation, two fluence corrections in the BEV are combined with two MU correction steps along the beam's path. (1) A transformation of the fluence map such that it is realigned with the current target geometry. (2) A correction for an unintended scaling of the penumbra margin when the treatment beams scale to the current target size. (3) A correction for the target depth relative to the body contour and (4) a correction for the target distance to the source. The impact of the correction strategy and its individual components was evaluated by simulations on a virtual prostate phantom. This heterogeneous reference phantom was systematically subjected to population based prostate transformations to simulate interfraction variations. Additionally, a patient example illustrated the clinical practice. The correction strategy was evaluated using both dosimetric (CTV mean dose, conformity index) and clinical (tumor control probability, and normal tissue complication probability) measures. Results: Based on the current experiments, the intended target dose and tumor control probability could be assured by the proposed method (TCP ≥ TCP{sub intended}). Additionally, the conformity index error was more than halved compared to the current clinical practice (ΔCI{sub 95%} from 40% to 16%) resulting in improved organ at risk protection. All the individual correction steps had an added value to the full correction. Conclusions: A limited number of fiducial points (no organ contours required) and an in-room (CB)CT are sufficient to perform a full dosimetric correction for IMRT plans. In the presence of interfraction variation, the corrected plans show superior dose distributions compared to our current clinical practice.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

SU-E-J-52: Dosimetric Benefit of Adaptive Re-Planning in Lung Cancer Stereotactic Body Radiotherapy (SBRT)

Purpose: To investigate the dosimetric benefit of adaptive re-planning for lung stereotactic body radiotherapy(SBRT). Methods: Five lung cancer patients with SBRT treatment were retrospectively investigated. Our in-house supercomputing online re-planning environment (SCORE) was used to realize the re-planning process. First a deformable image registration was carried out to transfer contours from treatment planning CT to each treatment CBCT. Then an automatic re-planning using original plan DVH guided fluence-map optimization is performed to get a new plan for the up-to-date patient geometry. We compared the re-optimized plan to the original plan projected on the up-to-date patient geometry in critical dosimetric parameters, such as PTV coverage, spinal cord maximum and volumetric constraint dose, esophagus maximum and volumetric constraint dose. Results: The average volume of PTV covered by prescription dose for all patients was improved by 7.56% after the adaptive re-planning. The volume of the spinal cord receiving 14.5Gy and 23Gy (V14.5, V23) decreased by 1.48% and 0.68%, respectively. For the esophagus, the volume receiving 19.5Gy (V19.5) reduced by 1.37%. Meanwhile, the maximum dose dropped off by 2.87% for spinal cord and 4.80% for esophagus. Conclusion: Our experimental results demonstrate that adaptive re-planning for lung SBRT has the potential to minimize the dosimetric effect of inter-fraction deformation and thus improve target coverage while reducing the risk of toxicity to nearby normal tissues.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

TH-A-BRF-02: BEST IN PHYSICS (JOINT IMAGING-THERAPY) - Modeling Tumor Evolution for Adaptive Radiation Therapy

Purpose: To develop mathematical models of tumor geometry changes under radiotherapy that may support future adaptive paradigms. Methods: A total of 29 cervical patients were scanned using MRI, once for planning and weekly thereafter for treatment monitoring. Using the tumor volumes contoured by a radiologist, three mathematical models were investigated based on the assumption of a stochastic process of tumor evolution. The “weekly MRI” model predicts tumor geometry for the following week from the last two consecutive MRI scans, based on the voxel transition probability. The other two models use only the first pair of consecutive MRI scans, and the transition probabilities were estimated via tumor type classified from the entire data set. The classification is based on either measuring the tumor volume (the “weekly volume” model), or implementing an auxiliary “Markov chain” model. These models were compared to a constant volume approach that represents the current clinical practice, using various model parameters; e.g., the threshold probability β converts the probability map into a tumor shape (larger threshold implies smaller tumor). Model performance was measured using volume conformity index (VCI), i.e., the union of the actual target and modeled target volume squared divided by product of these two volumes. Results: The “weekly MRI” model outperforms the constant volume model by 26% on average, and by 103% for the worst 10% of cases in terms of VCI under a wide range of β. The “weekly volume” and “Markov chain” models outperform the constant volume model by 20% and 16% on average, respectively. They also perform better than the “weekly MRI” model when β is large. Conclusion: It has been demonstrated that mathematical models can be developed to predict tumor geometry changes for cervical cancer undergoing radiotherapy. The models can potentially support adaptive radiotherapy paradigm by reducing normal tissue dose. This research was supported in part by the Ontario Consortium for Adaptive Interventions in Radiation Oncology (OCAIRO) funded by the Ontario Research Fund (ORF) and the MITACS Accelerate Internship Program.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2014

TH-E-17A-06: Anatomical-Adaptive Compressed Sensing (AACS) Reconstruction for Thoracic 4-Dimensional Cone-Beam CT

Purpose: The Feldkamp-Davis-Kress (FDK) algorithm currently used for clinical thoracic 4-dimensional (4D) cone-beam CT (CBCT) reconstruction suffers from noise and streaking artifacts due to projection under-sampling. Compressed sensing theory enables reconstruction of under-sampled datasets via total-variation (TV) minimization, but TV-minimization algorithms such as adaptive-steepest-descent-projection-onto-convex-sets (ASD-POCS) often converge slowly and are prone to over-smoothing anatomical details. These disadvantages can be overcome by incorporating general anatomical knowledge via anatomy segmentation. Based on this concept, we have developed an anatomical-adaptive compressed sensing (AACS) algorithm for thoracic 4D-CBCT reconstruction. Methods: AACS is based on the ASD-POCS framework, where each iteration consists of a TV-minimization step and a data fidelity constraint step. Prior to every AACS iteration, four major thoracic anatomical structures - soft tissue, lungs, bony anatomy, and pulmonary details - were segmented from the updated solution image. Based on the segmentation, an anatomical-adaptive weighting was applied to the TV-minimization step, so that TV-minimization was enhanced at noisy/streaky regions and suppressed at anatomical structures of interest. The image quality and convergence speed of AACS was compared to conventional ASD-POCS using an XCAT digital phantom and a patient scan. Results: For the XCAT phantom, the AACS image represented the ground truth better than the ASD-POCS image, giving a higher structural similarity index (0.93 vs. 0.84) and lower absolute difference (1.1*10{sup 4} vs. 1.4*10{sup 4}). For the patient case, while both algorithms resulted in much less noise and streaking than FDK, the AACS image showed considerably better contrast and sharpness of the vessels, tumor, and fiducial marker than the ASD-POCS image. In addition, AACS converged over 50% faster than ASD-POCS in both cases. Conclusions: The proposed AACS algorithm was shown to reconstruct thoracic 4D-CBCT images more accurately and with faster convergence compared to ASD-POCS. The superior image quality and rapid convergence makes AACS promising for future clinical use.

60 APPLIED LIFE SCIENCES↗