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DOE OSTI2015

SU-E-T-250: Determining VMAT Machine Limitations of An Elekta Linear Accelerator with Agility MLC for Accurate Modeling in RayStation and Robust Delivery

Purpose: To implement VMAT in RayStation with the Elekta Synergy linac with the new Agility MLC, and to utilize the same vendor softwares to determine the optimum Elekta VMAT machine parameters in RayStation for accurate modeling and robust delivery. Methods: iCOMCat is utilized to create various beam patterns with user defined dose rate, gantry, MLC and jaw speed for each control point. The accuracy and stability of the output and beam profile are qualified for each isolated functional component of VMAT delivery using ion chamber and Profiler2 with isocentric mounting fixture. Service graphing on linac console is used to verify the mechanical motion accuracy. The determined optimum Elekta VMAT machine parameters were configured in RayStation v4.5.1. To evaluate the system overall performance, TG-119 test cases and nine retrospective VMAT patients were planned on RayStation, and validated using both ArcCHECK (with plug and ion chamber) and MapCHECK2. Results: Machine output and profile varies <0.3% when only variable is dose rate (35MU/min-600MU/min). <0.9% output and <0.3% profile variation are observed with additional gantry motion (0.53deg/s–5.8deg/s both directions). The output and profile variation are still <1% with additional slow leaf motion (<1.5cm/s both direction). However, the profile becomes less symmetric, and >1.5% output and 7% profile deviation is seen with >2.5cm/s leaf motion. All clinical cases achieved comparable plan quality as treated IMRT plans. The gamma passing rate is 99.5±0.5% on ArcCheck (<3% iso center dose deviation) and 99.1±0.8% on MapCheck2 using 3%/3mm gamma (10% lower threshold). Mechanical motion accuracy in all VMAT deliveries is <1°/1mm. Conclusion: Accurate RayStation modeling and robust VMAT delivery is achievable on Elekta Agility for <2.5cm/s leaf motion and full range of dose rate and gantry speed determined by the same vendor softwares. Our TG-119 and patient results have provided us with the confidence to use VMAT clinically.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-417: Experimental Verification of Robustness Optimized Intensity Modulated Proton Therapy Plans for Craniospinal Irradiation

Purpose: The robustness-optimized intensity modulated proton therapy (RO-IMPT) plans takes into account the setup errors to eliminate the hot/cold spots caused by setup errors at the junction for craniospinal (CSI) irradiation fields. The RO-IMPT requires a large area for the junction of two fields. This work is to verify the treatment planning system calculated dose with introduction of setup errors by two-dimensional (2D) dose measurement. Two fields of total lengths of 45 cm with junctions of 21 cm were measured. Methods: 2D dose distributions in the coronal plane were measured by a commercial 2D ion-chamber-array detector in a cubic solid water phantom. A total spine field consisting of an upper and a lower spine field was measured at the depths of 5.4 and 6.9 cm. The active area for the measurement was 24×24 cm{sup 2}2. The junction and the area outside the junction of the spine fields were measured separately to complete the verification for the total field. Six different amounts of set-up errors along the superior-inferior direction up to 1.4 cm were intentionally applied. The measured dose with and without set-up errors were compared with the calculated dose with and without set-up errors using Gamma-index analysis. 3%/3mm and 2%/2mm dose/distance agreement criteria were used. The point dose at the center of the junction was also compared. Results: For all fields with various introduced setup errors, 100 % of pixels passed the 3%/3mm criteria and on average 96% of pixels passed 2%/2mm criteria. Differences in the measured and calculated doses were within 1.1%. Conclusion: The efficacy of the robustness for the CSI RO-IMPT plans were verified by measurement. The measurement method can be used for patient treatment field specific QA for CSI RO-IMPT plans in our institution in the near future.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-G-303-02: Robust Radiomics Methods for PET and CT Imaging

‘Radiomics’ refers to studies that extract a large amount of quantitative information from medical imaging studies as a basis for characterizing a specific aspect of patient health. Radiomics models can be built to address a wide range of outcome predictions, clinical decisions, basic cancer biology, etc. For example, radiomics models can be built to predict the aggressiveness of an imaged cancer, cancer gene expression characteristics (radiogenomics), radiation therapy treatment response, etc. Technically, radiomics brings together quantitative imaging, computer vision/image processing, and machine learning. In this symposium, speakers will discuss approaches to radiomics investigations, including: longitudinal radiomics, radiomics combined with other biomarkers (‘pan-omics’), radiomics for various imaging modalities (CT, MRI, and PET), and the use of registered multi-modality imaging datasets as a basis for radiomics. There are many challenges to the eventual use of radiomics-derived methods in clinical practice, including: standardization and robustness of selected metrics, accruing the data required, building and validating the resulting models, registering longitudinal data that often involve significant patient changes, reliable automated cancer segmentation tools, etc. Despite the hurdles, results achieved so far indicate the tremendous potential of this general approach to quantifying and using data from medical images. Specific applications of radiomics to be presented in this symposium will include: the longitudinal analysis of patients with low-grade gliomas; automatic detection and assessment of patients with metastatic bone lesions; image-based monitoring of patients with growing lymph nodes; predicting radiotherapy outcomes using multi-modality radiomics; and studies relating radiomics with genomics in lung cancer and glioblastoma. Learning Objectives: Understanding the basic image features that are often used in radiomic models. Understanding requirements for reliable radiomic models, including robustness of metrics, adequate predictive accuracy, and generalizability. Understanding the methodology behind radiomic-genomic (’radiogenomics’) correlations. Research supported by NIH (US), CIHR (Canada), and NSERC (Canada)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Generic and robust method for automatic segmentation of PET images using an active contour model

Purpose: Although positron emission tomography (PET) images have shown potential to improve the accuracy of targeting in radiation therapy planning and assessment of response to treatment, the boundaries of tumors are not easily distinguishable from surrounding normal tissue owing to the low spatial resolution and inherent noisy characteristics of PET images. The objective of this study is to develop a generic and robust method for automatic delineation of tumor volumes using an active contour model and to evaluate its performance using phantom and clinical studies. Methods: MASAC, a method for automatic segmentation using an active contour model, incorporates the histogram fuzzy C-means clustering, and localized and textural information to constrain the active contour to detect boundaries in an accurate and robust manner. Moreover, the lattice Boltzmann method is used as an alternative approach for solving the level set equation to make it faster and suitable for parallel programming. Twenty simulated phantom studies and 16 clinical studies, including six cases of pharyngolaryngeal squamous cell carcinoma and ten cases of nonsmall cell lung cancer, were included to evaluate its performance. Besides, the proposed method was also compared with the contourlet-based active contour algorithm (CAC) and Schaefer’s thresholding method (ST). The relative volume error (RE), Dice similarity coefficient (DSC), and classification error (CE) metrics were used to analyze the results quantitatively. Results: For the simulated phantom studies (PSs), MASAC and CAC provide similar segmentations of the different lesions, while ST fails to achieve reliable results. For the clinical datasets (2 cases with connected high-uptake regions excluded) (CSs), CAC provides for the lowest mean RE (−8.38% ± 27.49%), while MASAC achieves the best mean DSC (0.71 ± 0.09) and mean CE (53.92% ± 12.65%), respectively. MASAC could reliably quantify different types of lesions assessed in this work with good accuracy, resulting in a mean RE of −13.35% ± 11.87% and −11.15% ± 23.66%, a mean DSC of 0.89 ± 0.05 and 0.71 ± 0.09, and a mean CE of 19.19% ± 7.89% and 53.92% ± 12.65%, for PSs and CSs, respectively. Conclusions: The authors’ results demonstrate that the developed novel PET segmentation algorithm is applicable to various types of lesions in the authors’ study and is capable of producing accurate and consistent target volume delineations, potentially resulting in reduced intraobserver and interobserver variabilities observed when using manual delineation and improved accuracy in treatment planning and outcome evaluation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-D-207B-05: Robust Intra-Tumor Partitioning to Identify High-Risk Subregions for Prognosis in Lung Cancer

Purpose: To develop an intra-tumor partitioning framework for identifying high-risk subregions from 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) and CT imaging, and to test whether tumor burden associated with the high-risk subregions is prognostic of outcomes in lung cancer. Methods: In this institutional review board-approved retrospective study, we analyzed the pre-treatment FDG-PET and CT scans of 44 lung cancer patients treated with radiotherapy. A novel, intra-tumor partitioning method was developed based on a two-stage clustering process: first at patient-level, each tumor was over-segmented into many superpixels by k-means clustering of integrated PET and CT images; next, tumor subregions were identified by merging previously defined superpixels via population-level hierarchical clustering. The volume associated with each of the subregions was evaluated using Kaplan-Meier analysis regarding its prognostic capability in predicting overall survival (OS) and out-of-field progression (OFP). Results: Three spatially distinct subregions were identified within each tumor, which were highly robust to uncertainty in PET/CT co-registration. Among these, the volume of the most metabolically active and metabolically heterogeneous solid component of the tumor was predictive of OS and OFP on the entire cohort, with a concordance index or CI = 0.66–0.67. When restricting the analysis to patients with stage III disease (n = 32), the same subregion achieved an even higher CI = 0.75 (HR = 3.93, logrank p = 0.002) for predicting OS, and a CI = 0.76 (HR = 4.84, logrank p = 0.002) for predicting OFP. In comparison, conventional imaging markers including tumor volume, SUVmax and MTV50 were not predictive of OS or OFP, with CI mostly below 0.60 (p < 0.001). Conclusion: We propose a robust intra-tumor partitioning method to identify clinically relevant, high-risk subregions in lung cancer. We envision that this approach will be applicable to identifying useful imaging biomarkers in many cancer types.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-38: Impact of Smoothing and Noise On Robustness of CBCT Textural Features for Prediction of Response to Radiotherapy Treatment of Head and Neck Cancers

Purpose: To examine the impact of image smoothing and noise on the robustness of textural information extracted from CBCT images for prediction of radiotherapy response for patients with head/neck (H/N) cancers. Methods: CBCT image datasets for 14 patients with H/N cancer treated with radiation (70 Gy in 35 fractions) were investigated. A deformable registration algorithm was used to fuse planning CT’s to CBCT’s. Tumor volume was automatically segmented on each CBCT image dataset. Local control at 1-year was used to classify 8 patients as responders (R), and 6 as non-responders (NR). A smoothing filter [2D Adaptive Weiner (2DAW) with 3 different windows (ψ=3, 5, and 7)], and two noise models (Poisson and Gaussian, SNR=25) were implemented, and independently applied to CBCT images. Twenty-two textural features, describing the spatial arrangement of voxel intensities calculated from gray-level co-occurrence matrices, were extracted for all tumor volumes. Results: Relative to CBCT images without smoothing, none of 22 textural features extracted showed any significant differences when smoothing was applied (using the 2DAW with filtering parameters of ψ=3 and 5), in the responder and non-responder groups. When smoothing, 2DAW with ψ=7 was applied, one textural feature, Information Measure of Correlation, was significantly different relative to no smoothing. Only 4 features (Energy, Entropy, Homogeneity, and Maximum-Probability) were found to be statistically different between the R and NR groups (Table 1). These features remained statistically significant discriminators for R and NR groups in presence of noise and smoothing. Conclusion: This preliminary work suggests that textural classifiers for response prediction, extracted from H&N CBCT images, are robust to low-power noise and low-pass filtering. While other types of filters will alter the spatial frequencies differently, these results are promising. The current study is subject to Type II errors. A much larger cohort of patients is needed to confirm these results. This work was supported in part by a grant from Varian Medical Systems (Palo Alto, CA)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-384: Step and Shoot IMRT, VMAT and Autoplan VMAT Nasopharnyx Plan Robustness to Linear Accelerator Delivery Errors

Purpose: To identify the robustness of different treatment techniques in respect to simulated linac errors on the dose distribution to the target volume and organs at risk for step and shoot IMRT (ssIMRT), VMAT and Autoplan generated VMAT nasopharynx plans. Methods: A nasopharynx patient dataset was retrospectively replanned with three different techniques: 7 beam ssIMRT, one arc manual generated VMAT and one arc automatically generated VMAT. Treatment simulated uncertainties: gantry, collimator, MLC field size and MLC shifts, were introduced into these plans at increments of 5,2,1,−1,−2 and −5 (degrees or mm) and recalculated in Pinnacle. The mean and maximum doses were calculated for the high dose PTV, parotids, brainstem, and spinal cord and then compared to the original baseline plan. Results: Simulated gantry angle errors have <1% effect on the PTV, ssIMRT is most sensitive. The small collimator errors (±1 and ±2 degrees) impacted the mean PTV dose by <2% for all techniques, however for the ±5 degree errors mean target varied by up to 7% for the Autoplan VMAT and 10% for the max dose to the spinal cord and brain stem, seen in all techniques. The simulated MLC shifts introduced the largest errors for the Autoplan VMAT, with the larger MLC modulation presumably being the cause. The most critical error observed, was the MLC field size error, where even small errors of 1 mm, caused significant changes to both the PTV and the OAR. The ssIMRT is the least sensitive and the Autoplan the most sensitive, with target errors of up to 20% over and under dosages observed. Conclusion: For a nasopharynx patient the plan robustness observed is highest for the ssIMRT plan and lowest for the Autoplan generated VMAT plan. This could be caused by the more complex MLC modulation seen for the VMAT plans. This project is supported by a grant from NSW Cancer Council.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Supramolecular architectures of molecularly thin yet robust free-standing layers

Stable, single-nanometer thin, and free-standing two-dimensional layers with controlled molecular architectures are desired for several applications ranging from (opto-)electronic devices to nanoparticle and single-biomolecule characterization. It is, however, challenging to construct these stable single molecular layers via self-assembly, as the cohesion of those systems is ensured only by in-plane bonds. We herein demonstrate that relatively weak noncovalent bonds of limited directionality such as dipole-dipole (–CN … NC–) interactions act in a synergistic fashion to stabilize crystalline monomolecular layers of tetrafunctional calixarenes. The monolayers produced, demonstrated to be free-standing, display a well-defined atomic structure on the single-nanometer scale and are robust under a wide range of conditions including photon and electron radiation. This work opens up new avenues for the fabrication of robust, single-component, and free-standing layers via bottom-up self-assembly.

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

Strain-induced antipolar phase in hafnia stabilizes robust thin-film ferroelectricity

Hafnia (HfO 2 ) is a promising candidate for next-generation ferroelectric devices due to its robust ferroelectricity at reduced dimensions and its compatibility with silicon technology. Unfortunately, the origin of robust ferroelectricity and the underlying phase transition mechanism in HfO 2 remain elusive. Here, we show that its ferroelectricity arises from two phase transitions, where the primary phase transition to antipolar phase is activated by tensile strain. Above a threshold antipolar mode amplitude, a strong cooperative polar-antipolar coupling enables a second ferroelectric phase transition superimposed on the antipolar phase. Because the antipolar mode is not susceptible to depolarization, this polar-antipolar coupling stabilizes the polarization against depolarization effects. Our results demonstrate that tensile strain and polar-antipolar coupling are the origins of ferroelectricity in HfO 2 and provide a previously unknown mechanism against depolarization other than conventional improper ferroelectricity.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Draft Genome Assemblies of Five Robust Yarrowia lipolytica Strains Exhibiting High Lipid Production, Pentose Sugar Utilization, and Sugar Alcohol Secretion from Undetoxified Lignocellulosic Biomass Hydrolysates

Screening the genetic diversity of 45 Yarrowia lipolytica strains identified five candidates with unique metabolic capability and robustness in undetoxified switchgrass hydrolysates, including superior lipid production and efficient pentose sugar utilization. Here, we report the genome sequences of these strains to study their robustness and potential to produce fuels and chemicals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

A Robust and Efficient Implementation of LOBPCG

Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG) is widely used to compute eigenvalues of large sparse symmetric matrices. The algorithm can suffer from numerical instability if it is not implemented with care. This is especially problematic when the number of eigenpairs to be computed is relatively large. In this paper we propose an improved basis selection strategy based on earlier work by Hetmaniuk and Lehoucq as well as a robust convergence criterion which is backward stable to enhance the robustness. Here, we also suggest several algorithmic optimizations that improve performance of practical LOBPCG implementations. Numerical examples confirm that our approach consistently and significantly outperforms previous competing approaches in both stability and speed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Robust Iterative Method for Symmetric Quantum Signal Processing in All Parameter Regimes

Here, this paper addresses the problem of solving nonlinear systems in the context of symmetric quantum signal processing (QSP), a powerful technique for implementing matrix functions on quantum computers. Symmetric QSP focuses on representing target polynomials as products of matrices in SU(2) that possess symmetry properties. We present a novel Newton’s method tailored for efficiently solving the nonlinear system involved in determining the phase factors within the symmetric QSP framework. Our method demonstrates rapid and robust convergence in all parameter regimes, including the challenging scenario with ill-conditioned Jacobian matrices, using standard double precision arithmetic operations. For instance, solving symmetric QSP for a highly oscillatory target function α cos(1000x) (polynomial degree ≈ 1433) takes 6 iterations to converge to machine precision when α = 0.9, and the number of iterations only increases to 18 iterations when α = 1 – 10 -9 with a highly ill-conditioned Jacobian matrix. Leveraging the matrix product state structure of symmetric QSP, the computation of the Jacobian matrix incurs a computational cost comparable to a single function evaluation. Moreover, we introduce a reformulation of symmetric QSP using real-number arithmetics, further enhancing the method’s efficiency. Extensive numerical tests validate the effectiveness and robustness of our approach, which has been implemented in the QSPPACK software package.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Zotero Robust Links Extension

The Zotero Robust Links extension automatically creates an archival copy (memento) of the URI of an item ingested into a Zotero library. The extension can also manually be triggered to create mementos. The software also contains a "translator" that enables the export of "robustified" items with Robust Links-enhanced HTML code.

Jones, Shawn↗
DOE OSTI2023

Robust Machine Learning

UQ4ML is a code repository for a set of tools for the development of robust machine learning methods, uncertainty quantification and explainability of machine learning methods. The goal of these tools is to develop more robust and statistically rigorous machine learning methods for scientific applications. These tools are developed in Python, a high-level programming language that takes advantage of the Python ecosystem of high-quality open-source packages for machine learning.

Oyen, Diane↗
DOE OSTI2024

Adversarial Robustness Limits

This is the official code for the ICML 2024 paper "Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies". This code extends that of Wang et al. (2023) to facilitate state-of-the-art CIFAR-10 adversarial robustness, via training of WideResNet models on various large synthetic datasets. The code also facilitates derivation of the various scaling laws put forth in our ICML paper, which we use to compute efficient training settings.

Bartoldson, BrianR↗
DOE OSTI2022

Robust Anthropogenic Signal Identified in the Seasonal Cycle of Tropospheric Temperature

Previous work identified an anthropogenic fingerprint pattern in T AC (x, t), the amplitude of the seasonal cycle of mid- to upper-tropospheric temperature (TMT), but did not explicitly consider whether fingerprint identification in satellite T AC (x, t) data could have been influenced by real-world multidecadal internal variability (MIV). Here we address this question here using large ensembles (LEs) performed with five climate models. LEs provide many different sequences of internal variability noise superimposed on an underlying forced signal. Despite differences in historical external forcings, climate sensitivity, and MIV properties of the five models, their T AC (x, t) fingerprints are similar and statistically identifiable in 239 of the 240 LE realizations of historical climate change. Comparing simulated and observed variability spectra reveals that consistent fingerprint identification is unlikely to be biased by model underestimates of observed MIV. Even in the presence of large (factor of 3–4) intermodel and inter-realization differences in the amplitude of MIV, the anthropogenic fingerprints of seasonal cycle changes are robustly identifiable in models and satellite data. This is primarily due to the fact that the distinctive, global-scale fingerprint patterns are spatially dissimilar to the smaller-scale patterns of internal T AC (x, t) variability associated with the Atlantic multidecadal oscillation and El Niño–Southern Oscillation. The robustness of the seasonal cycle detection and attribution results shown here, taken together with the evidence from idealized aquaplanet simulations, suggest that basic physical processes are dictating a common pattern of forced T AC (x, t) changes in observations and in the five LEs. The key processes involved include GHG-induced expansion of the tropics, lapse-rate changes, land surface drying, and sea ice decrease.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Genome sequencing of evolved aspergilli populations reveals robust genomes, transversions in A. flavus, and sexual aberrancy in non-homologous end-joining mutants

Abstract Background Aspergillus spp. comprises a very diverse group of lower eukaryotes with a high relevance for industrial applications and clinical implications. These multinucleate species are often cultured for many generations in the laboratory, which can unknowingly propagate hidden genetic mutations. To assess the likelihood of such events, we studied the genome stability of aspergilli by using a combination of mutation accumulation (MA) lines and whole genome sequencing. Results We sequenced the whole genomes of 30 asexual and 10 sexual MA lines of three Aspergillus species ( A. flavus , A. fumigatus and A. nidulans ) and estimated that each MA line accumulated mutations for over 4000 mitoses during asexual cycles. We estimated mutation rates of 4.2 × 10 −11 ( A. flavus ), 1.1 × 10 −11 ( A. fumigatus ) and 4.1 × 10 −11 ( A. nidulans ) per site per mitosis, suggesting that the genomes are very robust. Unexpectedly, we found a very high rate of GC → TA transversions only in A. flavus . In parallel, 30 asexual lines of the non-homologous end-joining (NHEJ) mutants of the three species were also allowed to accumulate mutations for the same number of mitoses. Sequencing of these NHEJ MA lines gave an estimated mutation rate of 5.1 × 10 −11 ( A. flavus ), 2.2 × 10 −11 ( A. fumigatus ) and 4.5 × 10 −11 ( A. nidulans ) per base per mitosis, which is slightly higher than in the wild-type strains and some ~ 5–6 times lower than in the yeasts. Additionally, in A. nidulans , we found a NHEJ-dependent interference of the sexual cycle that is independent of the accumulation of mutations. Conclusions We present for the first time direct counts of the mutation rate of filamentous fungal species and find that Aspergillus genomes are very robust. Deletion of the NHEJ machinery results in a slight increase in the mutation rate, but at a rate we suggest is still safe to use for biotechnology purposes. Unexpectedly, we found GC→TA transversions predominated only in the species A. flavus , which could be generated by the hepatocarcinogen secondary metabolite aflatoxin. Lastly, a strong effect of the NHEJ mutation in self-crossing was observed and an increase in the mutations of the asexual lines was quantified.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample

Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detection approach, and demonstrated performance advantages on in silico data. In this study, we use the first comprehensive and well-characterized somatic reference data sets from the SEQC2 consortium to investigate best practices for using a deep learning framework in cancer mutation detection. Using the high-confidence somatic mutations established for a cancer cell line by the consortium, we identify the best strategy for building robust models on multiple data sets derived from samples representing real scenarios, for example, a model trained on a combination of real and spike-in mutations had the highest average performance. The strategy identified in our study achieved high robustness across multiple sequencing technologies for fresh and FFPE DNA input, varying tumor/normal purities, and different coverages, with significant superiority over conventional detection approaches in general, as well as in challenging situations such as low coverage, low variant allele frequency, DNA damage, and difficult genomic regions.

59 BASIC BIOLOGICAL SCIENCES↗