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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

malbacR: A Package for Standardized Implementation of Batch Correction Methods for Omics Data

Mass spectrometry is a powerful tool for identifying and analyzing small molecules, such as metabolites and lipids, in com-plex biological samples. Liquid chromatography and gas chromatography mass spectrometry studies quite commonly in-volve large numbers of samples, which can require significant time for sample preparation and analyses. To accommodate such studies, the samples are commonly split into batches. Inevitably, variations in sample handling, temperature fluctua-tion, imprecise timing, column degradation and other factors result in systematic errors or biases of the measured abundances between the batches. Numerous methods are available via R packages to assist with batch correction for small molecule om-ics data; however, since these methods were developed by different research teams, the algorithms are available in separate R packages, each with different data input and output formats. We introduce the malbacR package which consolidates eleven common batch effect correction methods for small molecule omics data into one place so users can easily implement and compare: pareto scaling, power scaling, range scaling, ComBat, EigenMS, NOMIS, RUV-random, QC-RLSC, WaveI-CA2.0, TIGER, and SERRF. The malbacR package standardizes data input and output formats across these batch correction methods. The package works in conjunction with the pmartR package, allowing users to seamlessly include batch effect cor-rection in a pmartR workflow without needing any additional data manipulation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Out of time order correlation of the Hubbard model with random local disorder

The out-of-time-order correlator (OTOC) serves as a powerful tool for investigating quantum information spreading and chaos in complex systems. We present a method employing non-equilibrium dynamical mean-field theory and coherent potential approximation combined with diagrammatic perturbation on the Schwinger–Keldysh contour to calculate the OTOC for correlated fermionic systems subjected to both random disorder and electron interaction. Furthermore, our key finding is that random disorder enhances the OTOC decay in the Hubbard model for the metallic phase in the weakly interacting limit. However, the current limitation of our perturbative solver restricts the applicability to weak interaction regimes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Online Learning for Commercial Buildings

There has been an increased interest in designing optimization based techniques for the control of building heating, ventilation, and air-conditioning (HVAC) systems for either improving the energy efficiency of buildings or providing ancillary services to the electric grid. The performance of such prediction-based control techniques heavily rely on the model of a building’s thermal dynamics. However, the development of high fidelity building thermal dynamic models is challenging given the presence of large uncertainties that affect thermal loads in buildings such as building envelope performance, thermal mass, internal heat gains as well as occupants' behavior. In this paper, we propose a method to identify both a resistive-capacitive parametric model, and non -parametric load uncertainties using measured input-output data. The parametric model is obtained using semi-parametric regression, whereas the non-parametric part is based on Random Forest, where regression trees are used to derive the dependency of non-parametric terms on both building operation parameters and ambient temperature. The effectiveness of the method is evaluated using experimental data collected from an office building at the Pacific Northwest National Laboratory (PNNL) campus. The proposed methodology was observed to provide improved accuracy over appropriate baseline strategies, when predicting indoor air temperatures.

Dong, Jin↗
DOE OSTI2026

scPlantAnnotate: an accurate and robust transformer-based model for plant cell type annotation

Accurate cell type annotation remains a major bottleneck in plant single-cell RNA sequencing (scRNA-seq), where existing tools are often adapted from animal studies and perform sub-optimally on plant data. The lack of plant-specific computational frameworks limits the construction of plant cell atlases and downstream biological discovery. We develop and evaluate scPlantAnnotate, a Transformer-based reference annotation framework tailored for plant scRNA-seq data, and benchmark it against state-of-the-art deep learning and conventional methods across multiple plant species. Species-specific scPlantAnnotate models were trained using curated datasets from Arabidopsis thaliana, Zea mays, Oryza sativa, and Glycine max. We compared scPlantAnnotate with leading baselines under both standard random-split evaluation and a more stringent leave-one-dataset-out setting, which tests robustness to completely unseen datasets and tissue types. scPlantAnnotate consistently outperforms existing approaches across all four species under random-split evaluation. In the leave-one-dataset-out setting for A. thaliana, where performance drops markedly for all methods due to strong batch effects and dataset heterogeneity, scPlantAnnotate nonetheless achieves the highest Accuracy, Macro-F1, Balanced Accuracy, and Macro-AUROC on average and ranks first on most held-out datasets. These results demonstrate improved robustness to dataset shifts, a critical yet underexplored challenge in plant scRNA-seq analysis. A freely accessible web server enables users to annotate their own datasets using pretrained models. scPlantAnnotate provides a plant-specific, Transformer-based framework for single-cell annotation that delivers state-of-the-art performance and enhanced robustness to unseen datasets. By addressing limitations of existing tools and enabling scalable reference-based annotation, scPlantAnnotate supports the development of comprehensive plant cell atlases and facilitates broader use of single-cell genomics in plant biology.

Bioinformatics↗
DOE OSTI2020

Methods for the robust computation of the long-period seismic spectrum of broad-band arrays

SUMMARY We describe array methods to search for low signal-to-noise ratio (SNR) signals in long-period seismic data using Fourier analysis. This is motivated by published results that find evidence of solar free oscillations in the Earth's seismic hum. Previous work used data from only one station. In this paper, we describe methods for computing spectra from array data. Arrays reduce noise level through averaging and provide redundancy that we use to distinguish coherent signal from a random background. We describe two algorithms for calculating a robust spectrum from seismic arrays, an algorithm that automatically removes impulsive transient signals from data, a jackknife method for estimating the variance of the spectrum, and a method for assessing the significance of an entire spectral band. We show examples of their application to data recorded by the Homestake Mine 3-D array in Lead, SD and the Piñon Flats PY array. These are two of the quietest small aperture arrays ever deployed in North America. The underground Homestake data has exceptionally low noise, and the borehole sensors of the PY array also have very low noise, making these arrays well suited to finding very weak signals. We find that our methods remove transient signals effectively from the data so that even low-SNR signals in the seismic background can be found and tested. Additionally, we find that the jackknife variance estimate is comparable to the noise floor, and we present initial evidence for solar g-modes in our data through the T2 test, a multivariate generalization of Student's t-test.

Caton, Ross C.↗
DOE OSTI2024

A finite difference informed random walker (FDiRW) solver for strongly inhomogeneous diffusion problems

In nature, many complex multi-physics coupling problems exhibit strong diffusivity inhomogeneity. For instance, in the context of radionuclide absorption by porous wasteform materials within a flowing waste stream, the difference of species’ diffusivity in solid and liquid phases spans by 3~8 orders of magnitude. To solve the diffusion equations with strongly inhomogeneous diffusivity, traditional discretization-based methods, such as the Finite Difference Method (FDM), require infinitesimally small time steps (<10 -10 ) as high spatial resolutions are employed in most microstructure evolution processes, leading to prohibitively high computational costs. Here, this work developed an integrated numerical approach (FDiRW: Finite Difference informed Random Walk) to tackle this challenge. The idea is that utilizing the Random Walk concept, the fast diffusion is modeled as a superposition of point source’s solution for a concentration distribution while FDM is used to obtain the point source’s solution at each node. A mesh-coarsening algorithm is developed to generate an exclusive coarse mesh for FDiRW approach to maximize its efficiency. The effectiveness of the coarse mesh-based FDiRW approach is validated by benchmarking Finite Difference solutions. Numerical results demonstrated that FDiRW achieves a remarkable 1000x computational efficiency improvement over FDM while preserving desired accuracy for a medium-sized model of 192 × 192 × 192 grids. Finally, as models scale up, a floating-point operations (PLOPs) analysis of the FDiRW algorithm reveals that its computational complexity grows quadratically in terms of the number of nodes employed in computation.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Long-Term Results of a Randomized Trial Comparing Iridium Implant Plus External Beam Radiation Therapy With External Beam Radiation Therapy Alone in Node-Negative Locally Advanced Cancer of the Prostate

Purpose: To determine the impact on long-term survival from the addition of brachytherapy to external beam radiation therapy (EBRT) in patients with prostate cancer. Methods and Materials: Between 1992 and 1997, 104 men with cT2-3, surgically staged node-negative prostate cancer were randomized to receive either EBRT (40 Gy/20 fractions) with iridium implant (35 Gy/48 hours) or EBRT alone (66 Gy/33 fractions) to the prostate. According to T stage, Gleason score, and prostate-specific antigen level, 60% of patients had high-risk disease. Substantial improvements in biochemical control at 8 years have previously been reported. Additional follow-up was collected on deaths and metastases. Results: Median follow-up was 14 years. Five patients were lost to follow-up. All other patients have been followed a minimum of 13 years. There have been 75 deaths, including 21 from prostate cancer and 25 from second cancers. No patients developing a second cancer have died from prostate cancer. There was no difference in overall survival between the 2 treatment groups: 34 deaths (67%) in the implant arm and 41 (77%) in the EBRT arm (hazard ratio [HR] 1.00, 95% confidence interval [CI] 0.63-1.59). Similarly, there was no difference in prostate cancer–specific deaths: 9 (18%) patients in the implant arm compared with 12 (23%) in the EBRT arm (HR 0.79, 95% CI 0.34-1.87). There was no statistically significant difference in the number of patients developing metastatic disease: 10 (20%) in the implant arm and 15 (28%) in the EBRT arm (HR 0.70, 95% CI 0.32-1.57). Improvements in biochemical control were maintained (HR 0.53, 95% CI 0.31-0.88). Conclusions: Despite a dramatic reduction of biochemical recurrence rates, the addition of iridium implant to EBRT did not translate into improved overall survival or prostate cancer–specific survival.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Adaptive sampling quasi-Newton methods for zeroth-order stochastic optimization

Here, we consider unconstrained stochastic optimization problems with no available gradient information. Such problems arise in settings from derivative-free simulation optimization to reinforcement learning. We propose an adaptive sampling quasi-Newton method where we estimate the gradients using finite differences of stochastic function evaluations within a common random number framework. We develop modified versions of a norm test and an inner product quasi-Newton test to control the sample sizes used in the stochastic approximations and provide global convergence results to the neighborhood of a locally optimal solution. We present numerical experiments on simulation optimization problems to illustrate the performance of the proposed algorithm. When compared with classical zeroth-order stochastic gradient methods, we observe that our strategies of adapting the sample sizes significantly improve performance in terms of the number of stochastic function evaluations required.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Augmenting subspace optimization methods with linear bandits

In this work, we consider the framework of methods for unconstrained minimization that are, in each iteration, restricted to a model that is only a valid approximation to the objective function on some affine subspace containing an incumbent point. These methods are of practical interest in computational settings where derivative information is either expensive or impossible to obtain. Recent attention has been paid in the literature to employing randomized matrix sketching for generating the affine subspaces within this framework. We consider a relatively straightforward, deterministic augmentation of such a generic subspace optimization method. In particular, we consider a sequential optimization framework where actions consist of one-dimensional linear subspaces and rewards consist of (approximations to) the magnitudes of directional derivatives computed in the direction of the action subspace. Reward maximization in this context is consistent with maximizing lower bounds on descent guaranteed by first-order Taylor models. This sequential optimization problem can be analysed through the lens of dynamic regret. We modify an existing linear upper confidence bound (UCB) bandit method and prove sublinear dynamic regret in the subspace optimization setting. We demonstrate the efficacy of employing this linear UCB method in a setting where forward-mode algorithmic differentiation can provide directional derivatives in arbitrary directions and in a derivative-free setting. For the derivative-free setting, we propose SS-POUNDers, an extension of the derivative-free optimization method POUNDers that employs the linear UCB mechanism to identify promising subspaces. Our numerical experiments suggest a preference, in either computational setting, for employing a linear UCB mechanism within a subspace optimization method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Dynamic PET simulator via tomographic emission projection for kinetic modeling and parametric image studies

Purpose: To develop and evaluate a fast and simple tool called dPETSTEP (Dynamic PET Simulator of Tracers via Emission Projection), for dynamic PET simulations as an alternative to Monte Carlo (MC), useful for educational purposes and evaluation of the effects of the clinical environment, postprocessing choices, etc., on dynamic and parametric images. Methods: The tool was developed in MATLAB using both new and previously reported modules of PETSTEP (PET Simulator of Tracers via Emission Projection). Time activity curves are generated for each voxel of the input parametric image, whereby effects of imaging system blurring, counting noise, scatters, randoms, and attenuation are simulated for each frame. Each frame is then reconstructed into images according to the user specified method, settings, and corrections. Reconstructed images were compared to MC data, and simple Gaussian noised time activity curves (GAUSS). Results: dPETSTEP was 8000 times faster than MC. Dynamic images from dPETSTEP had a root mean square error that was within 4% on average of that of MC images, whereas the GAUSS images were within 11%. The average bias in dPETSTEP and MC images was the same, while GAUSS differed by 3% points. Noise profiles in dPETSTEP images conformed well to MC images, confirmed visually by scatter plot histograms, and statistically by tumor region of interest histogram comparisons that showed no significant differences (p < 0.01). Compared to GAUSS, dPETSTEP images and noise properties agreed better with MC. Conclusions: The authors have developed a fast and easy one-stop solution for simulations of dynamic PET and parametric images, and demonstrated that it generates both images and subsequent parametric images with very similar noise properties to those of MC images, in a fraction of the time. They believe dPETSTEP to be very useful for generating fast, simple, and realistic results, however since it uses simple scatter and random models it may not be suitable for studies investigating these phenomena. dPETSTEP can be downloaded free of cost from https://github.com/CRossSchmidtlein/dPETSTEP.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Evaluating the Interplay between Trajectory Segmentation and Mode Inference Error

Travel behavior changes are essential to transportation decarbonization. Travel diaries, consisting of sequences of trips between places, are typically used to instrument human travel behavior. However, these diaries are only as accurate as the underlying methods used to construct them. Travel diary algorithms have been a popular research topic since the advent of Global Positioning System tracking surveys. These algorithms have typically been validated using prompted recall of presegmented trips, thus disregarding the continuity of mode inference. Phone operating systems have adopted battery-conserving techniques, but the resulting data collection errors have not been studied extensively. We introduce a framework to evaluate the accuracy of trip length computations and mode inference by analyzing continuous mode-segmented trajectories for groups of trips. We then use the framework to identify the input data quality and the impact of postprocessing. Our primary inputs to this evaluation are MobilityNet, a public dataset containing information from three artificial timelines covering 15 different travel modes, and sample open-source travel diary creation algorithms from the OpenPATH project. Our framework concretely shows that the variance of the distance error drops from (0.217, 0.0848) to (o.011, 0.0407) (Android, iOS) after postprocessing. Similarly, the weighted F-scores for mode inference increase from (0.25, 0.29) to (0.60, 0.74) (iOS, Android) between random forest and geographic information system-based models. We hope that this standardized method will be adapted to evaluate other, potentially proprietary, travel diary algorithms. Finally, the results can be used to understand and improve the state of the art in the travel diary creation field.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

PET/CT in brachytherapy early response evaluation of pancreatic ductal adenocarcinoma xenografts: comparison with apparent diffusion coefficient from diffusion-weighted MR imaging

Objective: To evaluate the feasibility of using PET/CT and diffusion-weighted magnetic resonance imaging (DW-MRI) to monitor the early response of pancreatic ductal adenocarcinoma (PDAC) xenografts to brachytherapy, and to determine whether maximum standardized uptake value (SUVmax) correlate with apparent diffusion coefficient (ADC). Materials and Methods: SW1990 human PDAC were subcutaneously implanted in 20 nude mice. They were randomly divided into 125-Iodine ({sup 125}I) seeds and blank seeds group. PET/CT and DW-MRI were performed at pretreatment and 5 days after therapy. SUVmax and ADC values were calculated, respectively. The correlation between SUVmax and ADC values was analyzed by the Pearson correlation test. Results: The SUVmax were significantly decreased between pretreatment and 5 days after {sup 125}I seeds treatment (p < 0.001) and between two groups (p < 0.001). And the ADC values were significantly increased between pretreatment and 5 days after {sup 125}I seeds treatment (p < 0.001) and between two groups (p < 0.001). While in the bank seeds group, there were no significantly difference between pretreatment and after treatment in SUVmax and ADC values (p = 0.057; p = 0.397). SUVmax and ADC correlated significantly and negatively before treatment in both groups (r = − 0.964, R{sup 2} = 0.929, p < 0.001; r = − 0.917, R{sup 2} = 0.841, p < 0.001) and after treatment in the blank seeds group (r = − 0.944, R{sup 2} = 0.891, p < 0.001). But after {sup 125}I seeds treatment there was no significant correlation between SUVmax and ADC (r = − 0.388, R{sup 2} = 0.151, p = 0.268). Conclusion: The PET/CT and DW-MRI are capable of monitoring the early response of PDAC xenografts to brachytherapy. The significantly inverse correlation between pretreatment SUVmax and ADC suggests that PET/CT and DW-MRI might play complementary roles for therapy assessment.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

PARIS: Predicting application resilience using machine learning

The traditional method to study application resilience to errors in HPC applications uses fault injection (FI), a time-consuming approach. Furthermore, while analytical models have been built to overcome the inefficiencies of FI, they lack accuracy. In this paper, we present PARIS, a machine-learning method to predict application resilience that avoids the time-consuming process of random FI and provides higher prediction accuracy than analytical models. PARIS captures the implicit relationship between application characteristics and application resilience, which is difficult to capture using most analytical models. We overcome many technical challenges for feature construction, extraction, and selection to use machine learning in our prediction approach. Our evaluation on 16 HPC benchmarks shows that PARIS achieves high prediction accuracy. PARIS is up to 450x faster than random FI (49x on average). Compared to the state-of-the-art analytical model, PARIS is at least 63% better in terms of accuracy and has comparable execution time on average.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

High-Resolution Comonomer Sequencing of Blocky Brominated Syndiotactic Polystyrene Copolymers Using 13C NMR Spectroscopy and Computer Simulations

This work demonstrates the first high-resolution comonomer sequencing of Blocky brominated syndiotactic polystyrene (sPS-co-sPS-Br) copolymers based on pentad assign- ments of the quaternary carbon region of the nuclear magnetic resonance spectrum. Copolymers containing p-bromostyrene (Br-Sty) units were prepared in matched sets using postpolymerization bromination methods carried out in the heterogeneous gel state (Blocky) and homogeneous solution state (Random). Quantitative information from the quaternary carbon spectra, heteronuclear multiple bond correlation spectroscopy, electronic structure calculations, and simulated statistically random copolymers was correlated to confirm the carbon resonance assignments for all 20 possible pentad comonomer sequences. Using the experimental pentad sequence prevalences, a computer code was developed to simulate chains with microstructures typical of each sample as a means to visually represent the copolymer blockiness with quantitative precision. Based on the microstructure and distribution of run lengths in these chains, the simulations revealed that the Blocky copolymers contain a high degree of blockiness. By comparing the run lengths in the simulated chains to the average number of styrene units in a crystalline segment of sPS (found by small-angle X-ray scattering), copolymer crystallizability was predicted. For the simulated Blocky B-21% (21 mol % Br-Sty) chain, the probability of randomly selecting a styrene unit in a crystallizable block was 25.8%, while that in the simulated Random R-18% was zero, in excellent agreement with the experimental crystallization behavior measured by differential scanning calorimetry. Additionally, these predictions confirmed that the simulated chains accurately represent the ensemble of chains in their respective copolymer samples. Furthermore, each simulated Blocky chain contained one or more long sPS blocks that paralleled the measured 38-40 styrene units spanning a crystalline segment within the sPS/CCl4 gel. This finding affirmed that the long sPS segments originated from the precise lamellar structure within the heterogeneous gel morphology (i.e., block length is correlated with lamellar thickness). Overall, the ability to tailor the copolymer microstructure through control of the semicrystalline gel morphology opens the door to synthesizing ordered copolymers by postpolymerization functionalization processes with unprecedented levels of compositional control.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A Differentiable Hydrology Approach for Modeling With Time-Varying Parameters

In the analysis of observational data with mathematical models, it is often desirable to let one or more model parameters vary with time to account for changing environmental conditions or to offer more flexibility. A standard approach for dynamic models is to allow for the number of parameters to grow as a function of time, leading to a high-dimensional inference problem for long data records. We propose using Hamiltonian Monte Carlo, a gradient-based Markov chain Monte Carlo (MCMC) method, to perform Bayesian inference for time-varying parameters (TVP) in hydrology models. As derivatives of model error functions with regard to parameters are not available in closed form, we implemented the GR4J rainfall-runoff models in Theano/PyMC3, and Jax/NumPyro, allowing for the application of automatic differentiation techniques. A simulation experiment assessing the viability of these methods for recovering underlying temporal variation in parameters indicates that a discrete Gaussian random walk-based prior appears to be best suited across a range of scenarios while the continuous-time Gaussian process compares relatively poorly. In an analysis of 20 years of daily real-world streamflow records from the Model Parameter Estimation Experiment (MOPEX) hydrology data set with increasing data sparsity and at multiple temporal resolutions, we find that TVP inference with gradient-based MCMC is a flexible and reliable approach for analyzing hydrology models with dynamic parameter sets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Spherulite-enhanced macroscopic polarization in molecular ferroelectric films from vacuum deposition

Proton-transfer type molecular ferroelectrics hold significant promise for practical application due to their large spontaneous polarizations, high Curie temperatures, and small switching fields. However, it remains puzzling that preparation of quasi-2D films exhibiting macroscopic ferroelectric behaviors has been reported in only few molecular ferroelectrics. To address this puzzle, we studied the impact of microstructures on the macroscopic ferroelectric properties of 5,6-dichloro-2-methylbenzimidazole (DC-MBI) films grown using the low-temperature deposition followed by the restrained crystallization (LDRC) method. Our findings revealed a competition between dense spherulites and porous microstructures containing randomly oriented nanograins in the as-grown films. Post-growth annealing at moderate temperature promotes the formation of spherulites, leading to macroscopic ferroelectric polarization switching. These results underscore the critical role of microstructure density in determining macroscopic ferroelectric properties, potentially resolving the puzzle for the absence of such behavior in many molecular ferroelectric films. Here, we anticipate that the approach proposed in this study to enhance microstructure density will significantly advance the fabrication of quasi-2D molecular ferroelectric films and unlock their potential in device applications.

36 MATERIALS SCIENCE↗
DOE OSTI2015

SU-E-T-538: Lung SBRT Dosimetric Comparison of 3D Conformal and RapidArc Planning

Purpose: Dose distributions of RapidArc Plan can be quite different from standard 3D conformal radiation therapy. SBRT plans can be optimized with high conformity or mimic the 3D conformal treatment planning with very high dose in the center of the tumor. This study quantifies the dosimetric differences among 3D conformal plan; flattened beam and FFF beam RapidArc Plans for lung SBRT. Methods: Five lung cancer patients treated with 3D non-coplanar SBRT were randomly selected. All the patients were CT scanned with 4DCT to determine the internal target volume. Abdominal compression was applied to minimize respiratory motion for SBRT patients. The prescription dose was 48 Gy in 4 fractions. The PTV coverage was optimized by two groups of objective function: one with high conformity, another mimicking 3D conformal dose distribution with high dose in the center of PTV. Optimization constraints were set to meet the criteria of the RTOG-0915 protocol. All VMAT plans were optimized with the RapidArc technique using four full arcs in Eclipse treatment planning system. The RapidArc SBRT plans with flattened 6MV beam and 6MV FFF beam were generated and dosimetric results were compared with the previous treated 3D non-coplanar plans. Results: All the RapidArc plans with flattened beam and FFF beam had similar results for the PTV and OARs. For the high conformity optimization group, The DVH of PTV exhibited a steep dose fall-off outside the PTV compared to the 3D non-coplanar plan. However, for the group mimicking the 3D conformal target dose distribution, although the PTV is very similar to the 3D conformal plan, the ITV coverage is better than 3D conformal plan. Conclusion: Due to excellent clinical experiences of 3D conformal SBRT treatment, the Rapid Arc optimization mimicking 3D conformal planning may be suggested for clinical use.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

SU-E-T-642: PTV Is the Voxel-Wise Worst-Case of CTV in Prostate Photon Therapy

Purpose: To examine the adequacy of the planning target volume (PTV) dose distribution as the worst-case representation of clinical target volume (CTV) dose distribution in prostate volumetric-modulated arc therapy (VMAT) plans. Methods: Ten intact prostate cancer cases treated by VMAT at our institution were randomly selected. Isocenter was shifted in the three cardinal directions by a displacement equal to the PTV expansion on the CTV (±3 mm) for a total of six shifted plans per original plan. Rotationally-perturbed plans were generated with a couch rotation of ±1° to simulate patient yaw. The eight perturbed dose distributions were recalculated in the treatment planning system using the same, fixed fluence map as the original plan. The voxel-wise worst-case CTV dose distribution was constructed from the minimum value per voxel from the eight perturbed doses. The resulting dose volume histograms (DVH) were evaluated for statistical correlation between the worst-case CTV and nominal PTV dose distributions based on D95% by Wilcoxon signed-rank test with significance level p ≤ 0.05. Results: Inspection demonstrates the PTV DVH in the nominal dose distribution is bounded by the CTV DVH in the worst-case dose distribution. Comparison of D95% for the two dose distributions by Wilcoxon signed-rank test gives p = 0.131. Therefore the null hypothesis cannot be rejected since the difference in median values is not statistically significant. Conclusion: The assumption that the nominal dose distribution for PTV represents the worst-case dose distribution for CTV appears valid for the ten plans under examination. Although the worst-case dose distribution is unphysical since the dose per voxel is chosen independently, it serves as a lower bound for the possible CTV coverage. Furthermore, this is consistent with the unphysical nature of the PTV. Minor discrepancies between the two dose distributions are expected since the dose cloud is not strictly static. Funding Support: 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.

62 RADIOLOGY AND NUCLEAR MEDICINE↗