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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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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Raman chemical imaging of the rhizosphere bacterium Pantoea sp. YR343 and its co-culture with Arabidopsis thaliana

Chemical imaging of plant-bacteria co-cultures renders it possible to characterize bacterial populations and behaviors and their interactions with proximal organisms, under conditions closest to the environment in the rhizosphere. Here Raman micro-spectroscopy and confocal Raman imaging are used as minimally invasive probes to study the rhizosphere bacterial isolate, Pantoea sp. YR343, and its co-culture with model plant Arabidopsis thaliana by combining enhanced Raman spectroscopies with electron microscopy and principal component analysis (PCA). In this work, the presence of carotenoid pigments in the wild type Pantoea sp. YR343 was characterized using resonance Raman scattering, which was also used to confirm successful disruption of the crtB gene in an engineered carotenoid mutant strain. Other components of the Pantoea sp. YR343 cells were imaged in the presence of resonantly enhanced pigments using a combination of surface enhanced Raman imaging and PCA. Pantoea sp. YR343 cells decorated with Ag colloid synthesized ex situ gave spectra dominated by carotenoid scattering, whereas colloids synthesized in situ produced spectral signatures characteristic of flavins in the cell membrane. Scanning electron microscopy (SEM) of whole cells and transmission electron microscopy (TEM) images of thinly sliced cross-sections were used to assess structural integrity of the coated cells and to establish the origin of spectral signatures based on the position of Ag nanoparticles in the cells. Finally, raman imaging was also used to characterize senescent green Arabidopsis thaliana plant roots inoculated with Pantoea sp. YR343, and PCA was used to distinguish spectral contributions from plant and bacterial cells, thereby establishing the potential of Raman imaging to visualize the distribution of rhizobacteria on plant roots.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Reduction of a collisional-radiative mechanism for argon plasma based on principal component analysis

This article considers the development of reduced chemistry models for argon plasmas using Principal Component Analysis (PCA) based methods. Starting from an electronic specific Collisional-Radiative model, a reduction of the variable set (i.e., mass fractions and temperatures) is proposed by projecting the full set on a reduced basis made up of its principal components. Thus, the flow governing equations are only solved for the principal components. The proposed approach originates from the combustion community, where Manifold Generated Principal Component Analysis (MG-PCA) has been developed as a successful reduction technique. Applications consider ionizing shock waves in argon. The results obtained show that the use of the MG-PCA technique enables for a substantial reduction of the computational time.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2012

Watching a signaling protein function in real time via 100-ps time-resolved Laue crystallography

To understand how signaling proteins function, it is necessary to know the time-ordered sequence of events that lead to the signaling state. We recently developed on the BioCARS 14-IDB beamline at the Advanced Photon Source the infrastructure required to characterize structural changes in protein crystals with near-atomic spatial resolution and 150-ps time resolution, and have used this capability to track the reversible photocycle of photoactive yellow protein (PYP) following trans-to-cis photoisomerization of its p-coumaric acid (pCA) chromophore over 10 decades of time. The first of four major intermediates characterized in this study is highly contorted, with the pCA carbonyl rotated nearly 90° out of the plane of the phenolate. A hydrogen bond between the pCA carbonyl and the Cys69 backbone constrains the chromophore in this unusual twisted conformation. Density functional theory calculations confirm that this structure is chemically plausible and corresponds to a strained cis intermediate. This unique structure is short-lived (~600 ps), has not been observed in prior cryocrystallography experiments, and is the progenitor of intermediates characterized in previous nanosecond time-resolved Laue crystallography studies. The structural transitions unveiled during the PYP photocycle include trans/cis isomerization, the breaking and making of hydrogen bonds, formation/relaxation of strain, and gated water penetration into the interior of the protein. This mechanistically detailed, near-atomic resolution description of the complete PYP photocycle provides a framework for understanding signal transduction in proteins, and for assessing and validating theoretical/computational approaches in protein biophysics.

Schotte, Friedrich↗
DOE OSTI2026

Machine Learning–Augmented Laser-Induced Breakdown Spectroscopy for Spectral Discrimination of Iron Oxalates

Enhanced characterization and phase identification of post-PUREX Pu Oxalates (PuOXA) are pivotal for nonproliferation and pre-detonation nuclear forensics. Despite significant advances in the characterization of PuO 2 samples, little is known about the impact of both the chemical structure and oxidation states of PuOXA (i.e., Pu(III) and Pu(IV)) have on optical emission signatures. Here, we demonstrate the analytical capabilities of laser-induced breakdown spectroscopy (LIBS) applied to Fe(II) and Fe(III) oxalate samples as surrogates for PuOXA, highlighting the discriminating features in the LIBS emission spectra arising from differences in the oxidation states within mixed FeOXA samples. We report the enhancement of spectral feature selection using Principal Component Analysis (PCA), which enables the analytical superiority of machine learning algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR) over conventional univariate techniques for phase discrimination and chemometric analysis. Cluster analysis revealed how both matrix effects and laser ablation influence cluster separability by introducing spectral artifacts that misdirect the maximization of variance. PCA-selected emission lines were used in the regression models, demonstrating that both univariate and multivariate linear regression models (i.e., PLSR and SVR) can achieve acceptable performance, with machine learning models outperforming conventional calibration regressions. Furthermore, the application of non-linearly activated PCA-selected emission lines illustrates how simplifying the data while retaining captured variance enables the use of less complex and more computationally efficient models. Furthermore, this is particularly evident in the underperformance of RFR, which suffers from increased computational costs and overfitting owing to its high complexity.

Oxalates↗
DOE OSTI2022

p -Coumaroylation of lignin occurs outside of commelinid monocots in the eudicot genus Morus (mulberry)

Abstract The presence of p-coumarate (pCA) in plant cell walls is generally considered to be a trait present only in commelinid monocots. Here, we show that this long-held overgeneralizing assumption is incorrect and that mulberry trees (Morus) are eudicot plants that have lignins derived in part from monolignol pCA esters. As in commelinid monocots, the lignin-bound pCA acylates the sidechain γ-hydroxyl of both coniferyl and syringyl units. This discovery expands mulberry’s potential applications to include being a source of p-coumaric acid, a supplier of nutritious berries, a forage crop, a decorative plant, and the main food source for silkworms.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Fault Detection via Occupation Kernel Principal Component Analysis

Reliable operation of automatic systems is heavily dependent on the ability to detect faults in the underlying dynamics. While traditional model-based methods have been widely used for fault detection, data-driven approaches have garnered increasing attention due to their ease of deployment and minimal need for expert knowledge. In this letter, we present a novel principal component analysis (PCA) method that uses occupation kernels. Occupation kernels result in feature maps that are tailored to the measured data, have inherent noise-robustness due to the use of integration, and can utilize irregularly sampled system trajectories of variable lengths for PCA. The occupation kernel PCA method is used to develop a reconstruction error approach to fault detection and its efficacy is validated using numerical simulations.

42 ENGINEERING↗
DOE OSTI2018

An Investigation of Potential Intermittency Induced Outage Modes for Wind Generation

In a deterministic contingency analysis (DCA), renewable generation such as wind is modeled similar to a conventional generator. In the current practices of a probabilistic contingency analysis (PCA), the outage parameters for a renewable generator reflect only failures of its parts. Intermittency due to wind speed changes or cloud cover may cause renewable generation to ramp up or down within a short time period and, after the transient is finished, stay at a certain generation level before a new ramp starts. A fast ramp-down transient can last for some time and thus, have an effect similar to a failure or de-rated generation of a conventional generator. This paper investigates potential outage modes that are induced by the intermittency of renewable generation, as well as the modeling and parameterization of such outage modes in the PCA framework. Historical time-series wind generation data are used in this study to extract different types of transients and perform statistical analyses to obtain input data such that the intermittency induced outages (IIOs) can be included in PCA.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Design of an Ara h 2 hypoallergen from conformational epitopes

Abstract Introduction Adverse reactions are relatively common during peanut oral immunotherapy. To reduce the risk to the patient, some researchers have proposed modifying the allergen to reduce IgE reactivity, creating a putative hypoallergen. Analysis of recently cloned human IgG from patients treated with peanut immunotherapy suggested that there are three common conformational epitopes for the major peanut allergen Ara h 2. We sought to test if structural information on these epitopes could indicate mutagenesis targets for designing a hypoallergen and evaluated the reduction in IgE binding via immunochemistry and a mouse model of passive cutaneous anaphylaxis (PCA). Methods X‐ray crystallography characterized the conformational epitopes in detail, followed by mutational analysis of key residues to modify monoclonal antibody (mAb) and serum IgE binding, assessed by ELISA and biolayer interferometry. A designed Ara h 2 hypoallergen was tested for reduced vascularization in mouse PCA experiments using pooled peanut allergic patient serum. Results A ternary crystal structure of Ara h 2 in complex with patient antibodies 13T1 and 13T5 was determined. Site‐specific mutants were designed that reduced 13T1, 13T5, and 22S1 mAbs binding by orders of magnitude. By combining designed mutations from the three major conformational bins, a hexamutant (Ara h 2 E46R, E89R, E97R, E114R, Q146A, R147E) was created that reduced IgE binding in serum from allergic patients. Further, in the PCA model where mice were primed with peanut allergic patient serum, reactivity upon allergen challenge was significantly decreased using the hexamutant. Conclusion These studies demonstrate that prior knowledge of common conformational epitopes can be used to engineer reduced IgE reactivity, an important first step in hypoallergen design.

Allergy↗
DOE OSTI2025

Modeling MTS pyrolysis and SiC deposition kinetics using principal component analysis and neural networks

Accurate chemical kinetics modeling is crucial for improving the efficiency of chemical processing and synthesis of ceramic matrix composites. Detailed kinetic models are computationally expensive due to the large number of transported chemical species, while the simplified physics-based models, such as single-step global mechanisms, are efficient but often overlook key chemical intermediates and pathways. Recent deep learning approaches promise accurate and cost-effective models. Yet, they require additional closures for the transported nonlinear latent variables, complicating integration with existing solvers. In this work, we develop a hybrid linear—nonlinear reduced model for silicon carbide deposition from methyltrichlorosilane precursor by combining principal component analysis (PCA) and autoencoder (AE) neural network (NN) approaches. PCA is used to identify a smaller set of linear transport variables, enabling direct reuse of conventional transport solvers. NNs then reconstruct the full chemical state from these reduced variables. We demonstrate the method on a chemical vapor deposition reactor—comprising a gas-phase pyrolysis plug flow reactor and a heterogeneous surface reactor—over a wide range of temperatures, pressures, and residence times. Our PCA–AE model achieves high accuracy with only five transported scalars, achieving an eightfold cost reduction compared to detailed mechanisms, in both a priori (using data from the test set only) and a posteriori (coupled with a differential equation solver). In conclusion, notable errors arise primarily near training domain boundaries and for long residence times, indicating the need for domain shift indicators and better long-horizon predictions in future reduced chemistry model development.

autoencoder neural networks↗
DOE OSTI2013

The effects of Doppler broadening and detector resolution on the performance of three-stage Compton cameras

Purpose: The authors investigated how the characteristics of the detectors used in a three-stage Compton camera (CC) affect the CC's ability to accurately measure the emission distribution and energy spectrum of prompt gammas (PG) emitted by nuclear de-excitations during proton therapy. The detector characteristics they studied included the material (high-purity germanium [HPGe] and cadmium zinc telluride [CZT]), Doppler broadening (DB), and resolution (lateral, depth, and energy). Methods: The authors simulated three-stage HPGe and CZT CCs of various configurations, detecting gammas from point sources with energies ranging from 0.511 to 7.12 MeV. They also simulated a proton pencil beam irradiating a tissue target to study how the detector characteristics affect the PG data measured by CCs in a clinical proton therapy setting. They used three figures of merit: the distance of closest approach (DCA) and the point of closest approach (PCA) between the measured and actual position of the PG emission origin, and the calculated energy resolution. Results: For CCs with HPGe detectors, DB caused the DCA to be greater than 3 mm for 14% of the 6.13 MeV gammas and 20% of the 0.511 MeV gammas. For CCs with CZT detectors, DB caused the DCA to be greater than 3 mm for 18% of the 6.13 MeV gammas and 25% of the 0.511 MeV gammas. The full width at half maximum (FWHM) of the PCA in the z-caret direction for HPGe and CZT detectors ranged from 1.3 to 0.4 mm for gammas with incident energy ranging from 0.511 to 7.12 MeV. For CCs composed of HPGe detectors, the resolution of incident gamma energy calculated by the CC ranged from 6% to 1% for gammas with true incident energies from 0.511 to 7.12 MeV. For CCs composed of CZT detectors, the resolution of gamma energy calculated by the CC ranged from 10% to 1% for gammas with true incident energies from 0.511 to 7.12 MeV. For HPGe and CZT CCs in which all detector effect were included, the DCA was less than 3 mm for 75% and 68% of the detected gammas, respectively, and restricting gammas to those having energy greater than 2.0 MeV increased these percentages to 83% and 77% for HPGe and CZT, respectively. Distributions of the true gamma origins and the PCA after detector characteristics had been included showed good agreement on beam range and some loss of resolution for the lateral profile of the PG emission. Characteristic energy lines were evident in the calculated gamma energy spectrum. Conclusions: The authors found the following: (1) DB is the dominant source of spatial and energy resolution loss in the CCs at all energy levels; (2) the largest difference in the spatial resolution of HPGe and CZT CCs is that the spatial resolution distributions of CZT have broader tails. The differences in the FWHM of these distributions are small; (3) the energy resolution of both HPGe and CZT three-stage CCs is adequate for PG spectroscopy; and (4) restricting the gammas to those having energy greater than 2.0 MeV can improve the achievable image resolution.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2014

SU-E-J-98: Radiogenomics: Correspondence Between Imaging and Genetic Features Based On Clustering Analysis

Purpose: Radiogenomics aims to establish relationships between patient genotypes and imaging phenotypes. An open question remains on how best to integrate information from these distinct datasets. This work investigates if similarities in genetic features across patients correspond to similarities in PET-imaging features, assessed with various clustering algorithms. Methods: [{sup 18}F]FDG PET data was obtained for 26 NSCLC patients from a public database (TCIA). Tumors were contoured using an in-house segmentation algorithm combining gradient and region-growing techniques; resulting ROIs were used to extract 54 PET-based features. Corresponding genetic microarray data containing 48,778 elements were also obtained for each tumor. Given mismatch in feature sizes, two dimension reduction techniques were also applied to the genetic data: principle component analysis (PCA) and selective filtering of 25 NSCLC-associated genes-ofinterest (GOI). Gene datasets (full, PCA, and GOI) and PET feature datasets were independently clustered using K-means and hierarchical clustering using variable number of clusters (K). Jaccard Index (JI) was used to score similarity of cluster assignments across different datasets. Results: Patient clusters from imaging data showed poor similarity to clusters from gene datasets, regardless of clustering algorithms or number of clusters (JI{sub mean}= 0.3429±0.1623). Notably, we found clustering algorithms had different sensitivities to data reduction techniques. Using hierarchical clustering, the PCA dataset showed perfect cluster agreement to the full-gene set (JI =1) for all values of K, and the agreement between the GOI set and the full-gene set decreased as number of clusters increased (JI=0.9231 and 0.5769 for K=2 and 5, respectively). K-means clustering assignments were highly sensitive to data reduction and showed poor stability for different values of K (JI{sub range}: 0.2301–1). Conclusion: Using commonly-used clustering algorithms, we found poor agreement in patient characterization from genomic versus imaging data. Interpretation of radiogenomic associations was found to be dependent on clustering algorithms and sensitive to data reduction techniques.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

An initial study on the estimation of time-varying volumetric treatment images and 3D tumor localization from single MV cine EPID images

Purpose: In this work the authors develop and investigate the feasibility of a method to estimate time-varying volumetric images from individual MV cine electronic portal image device (EPID) images. Methods: The authors adopt a two-step approach to time-varying volumetric image estimation from a single cine EPID image. In the first step, a patient-specific motion model is constructed from 4DCT. In the second step, parameters in the motion model are tuned according to the information in the EPID image. The patient-specific motion model is based on a compact representation of lung motion represented in displacement vector fields (DVFs). DVFs are calculated through deformable image registration (DIR) of a reference 4DCT phase image (typically peak-exhale) to a set of 4DCT images corresponding to different phases of a breathing cycle. The salient characteristics in the DVFs are captured in a compact representation through principal component analysis (PCA). PCA decouples the spatial and temporal components of the DVFs. Spatial information is represented in eigenvectors and the temporal information is represented by eigen-coefficients. To generate a new volumetric image, the eigen-coefficients are updated via cost function optimization based on digitally reconstructed radiographs and projection images. The updated eigen-coefficients are then multiplied with the eigenvectors to obtain updated DVFs that, in turn, give the volumetric image corresponding to the cine EPID image. Results: The algorithm was tested on (1) Eight digital eXtended CArdiac-Torso phantom datasets based on different irregular patient breathing patterns and (2) patient cine EPID images acquired during SBRT treatments. The root-mean-squared tumor localization error is (0.73 ± 0.63 mm) for the XCAT data and (0.90 ± 0.65 mm) for the patient data. Conclusions: The authors introduced a novel method of estimating volumetric time-varying images from single cine EPID images and a PCA-based lung motion model. This is the first method to estimate volumetric time-varying images from single MV cine EPID images, and has the potential to provide volumetric information with no additional imaging dose to the patient.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-J-26: A Novel Technique for Markerless Self-Sorted 4D-CBCT Using Patient Motion Modeling: A Feasibility Study

Purpose: To develop an automatic markerless 4D-CBCT projection sorting technique by using a patient respiratory motion model extracted from the planning 4D-CT images. Methods: Each phase of onboard 4D-CBCT is considered as a deformation of one phase of the prior planning 4D-CT. The deformation field map (DFM) is represented as a linear combination of three major deformation patterns extracted from the planning 4D-CT using principle component analysis (PCA). The coefficients of the PCA deformation patterns are solved by matching the digitally reconstructed radiograph (DRR) of the deformed volume to the onboard projection acquired. The PCA coefficients are solved for each single projection, and are used for phase sorting. Projections at the peaks of the Z direction coefficient are sorted as phase 1 and other projections are assigned into 10 phase bins by dividing phases equally between peaks. The 4D digital extended-cardiac-torso (XCAT) phantom was used to evaluate the proposed technique. Three scenarios were simulated, with different tumor motion amplitude (3cm to 2cm), tumor spatial shift (8mm SI), and tumor body motion phase shift (2 phases) from prior to on-board images. Projections were simulated over 180 degree scan-angle for the 4D-XCAT. The percentage of accurately binned projections across entire dataset was calculated to represent the phase sorting accuracy. Results: With a changed tumor motion amplitude from 3cm to 2cm, markerless phase sorting accuracy was 100%. With a tumor phase shift of 2 phases w.r.t. body motion, the phase sorting accuracy was 100%. With a tumor spatial shift of 8mm in SI direction, phase sorting accuracy was 86.1%. Conclusion: The XCAT phantom simulation results demonstrated that it is feasible to use prior knowledge and motion modeling technique to achieve markerless 4D-CBCT phase sorting. National Institutes of Health Grant No. R01-CA184173 Varian Medical System.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Improved accuracy of quantitative parameter estimates in dynamic contrast-enhanced CT study with low temporal resolution

Purpose: A previously proposed method to reduce radiation dose to patient in dynamic contrast-enhanced (DCE) CT is enhanced by principal component analysis (PCA) filtering which improves the signal-to-noise ratio (SNR) of time-concentration curves in the DCE-CT study. The efficacy of the combined method to maintain the accuracy of kinetic parameter estimates at low temporal resolution is investigated with pixel-by-pixel kinetic analysis of DCE-CT data. Methods: The method is based on DCE-CT scanning performed with low temporal resolution to reduce the radiation dose to the patient. The arterial input function (AIF) with high temporal resolution can be generated with a coarsely sampled AIF through a previously published method of AIF estimation. To increase the SNR of time-concentration curves (tissue curves), first, a region-of-interest is segmented into squares composed of 3 × 3 pixels in size. Subsequently, the PCA filtering combined with a fraction of residual information criterion is applied to all the segmented squares for further improvement of their SNRs. The proposed method was applied to each DCE-CT data set of a cohort of 14 patients at varying levels of down-sampling. The kinetic analyses using the modified Tofts’ model and singular value decomposition method, then, were carried out for each of the down-sampling schemes between the intervals from 2 to 15 s. The results were compared with analyses done with the measured data in high temporal resolution (i.e., original scanning frequency) as the reference. Results: The patients’ AIFs were estimated to high accuracy based on the 11 orthonormal bases of arterial impulse responses established in the previous paper. In addition, noise in the images was effectively reduced by using five principal components of the tissue curves for filtering. Kinetic analyses using the proposed method showed superior results compared to those with down-sampling alone; they were able to maintain the accuracy in the quantitative histogram parameters of volume transfer constant [standard deviation (SD), 98th percentile, and range], rate constant (SD), blood volume fraction (mean, SD, 98th percentile, and range), and blood flow (mean, SD, median, 98th percentile, and range) for sampling intervals between 10 and 15 s. Conclusions: The proposed method of PCA filtering combined with the AIF estimation technique allows low frequency scanning for DCE-CT study to reduce patient radiation dose. The results indicate that the method is useful in pixel-by-pixel kinetic analysis of DCE-CT data for patients with cervical cancer.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

SU-C-209-02: 3D Fluoroscopic Image Generation From Patient-Specific 4DCBCT-Based Motion Models Derived From Clinical Patient Images

Purpose: We develop a method to generate time varying volumetric images (3D fluoroscopic images) using patient-specific motion models derived from four-dimensional cone-beam CT (4DCBCT). Methods: Motion models are derived by selecting one 4DCBCT phase as a reference image, and registering the remaining images to it. Principal component analysis (PCA) is performed on the resultant displacement vector fields (DVFs) to create a reduced set of PCA eigenvectors that capture the majority of respiratory motion. 3D fluoroscopic images are generated by optimizing the weights of the PCA eigenvectors iteratively through comparison of measured cone-beam projections and simulated projections generated from the motion model. This method was applied to images from five lung-cancer patients. The spatial accuracy of this method is evaluated by comparing landmark positions in the 3D fluoroscopic images to manually defined ground truth positions in the patient cone-beam projections. Results: 4DCBCT motion models were shown to accurately generate 3D fluoroscopic images when the patient cone-beam projections contained clearly visible structures moving with respiration (e.g., the diaphragm). When no moving anatomical structure was clearly visible in the projections, the 3D fluoroscopic images generated did not capture breathing deformations, and reverted to the reference image. For the subset of 3D fluoroscopic images generated from projections with visibly moving anatomy, the average tumor localization error and the 95th percentile were 1.6 mm and 3.1 mm respectively. Conclusion: This study showed that 4DCBCT-based 3D fluoroscopic images can accurately capture respiratory deformations in a patient dataset, so long as the cone-beam projections used contain visible structures that move with respiration. For clinical implementation of 3D fluoroscopic imaging for treatment verification, an imaging field of view (FOV) that contains visible structures moving with respiration should be selected. If no other appropriate structures are visible, the images should include the diaphragm. This project was supported, in part, through a Master Research Agreement with Varian Medical Systems, Inc, Palo Alto, CA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-AB-207B-07: Dose Cloud: Generating “Big Data” for Radiation Therapy Treatment Plan Optimization Research

Purpose: To provide a tool to generate large sets of realistic virtual patient geometries and beamlet doses for treatment optimization research. This tool enables countless studies exploring the fundamental interplay between patient geometry, objective functions, weight selections, and achievable dose distributions for various algorithms and modalities. Methods: Generating realistic virtual patient geometries requires a small set of real patient data. We developed a normalized patient shape model (PSM) which captures organ and target contours in a correspondence-preserving manner. Using PSM-processed data, we perform principal component analysis (PCA) to extract major modes of variation from the population. These PCA modes can be shared without exposing patient information. The modes are re-combined with different weights to produce sets of realistic virtual patient contours. Because virtual patients lack imaging information, we developed a shape-based dose calculation (SBD) relying on the assumption that the region inside the body contour is water. SBD utilizes a 2D fluence-convolved scatter kernel, derived from Monte Carlo simulations, and can compute both full dose for a given set of fluence maps, or produce a dose matrix (dose per fluence pixel) for many modalities. Combining the shape model with SBD provides the data needed for treatment plan optimization research. Results: We used PSM to capture organ and target contours for 96 prostate cases, extracted the first 20 PCA modes, and generated 2048 virtual patient shapes by randomly sampling mode scores. Nearly half of the shapes were thrown out for failing anatomical checks, the remaining 1124 were used in computing dose matrices via SBD and a standard 7-beam protocol. As a proof of concept, and to generate data for later study, we performed fluence map optimization emphasizing PTV coverage. Conclusions: We successfully developed and tested a tool for creating customizable sets of virtual patients suitable for large-scale radiation therapy optimization research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Use of regularized principal component analysis to model anatomical changes during head and neck radiation therapy for treatment adaptation and response assessment

Purpose: To develop standard (SPCA) and regularized (RPCA) principal component analysis models of anatomical changes from daily cone beam CTs (CBCTs) of head and neck (H&N) patients and assess their potential use in adaptive radiation therapy, and for extracting quantitative information for treatment response assessment. Methods: Planning CT images of ten H&N patients were artificially deformed to create “digital phantom” images, which modeled systematic anatomical changes during radiation therapy. Artificial deformations closely mirrored patients’ actual deformations and were interpolated to generate 35 synthetic CBCTs, representing evolving anatomy over 35 fractions. Deformation vector fields (DVFs) were acquired between pCT and synthetic CBCTs (i.e., digital phantoms) and between pCT and clinical CBCTs. Patient-specific SPCA and RPCA models were built from these synthetic and clinical DVF sets. EigenDVFs (EDVFs) having the largest eigenvalues were hypothesized to capture the major anatomical deformations during treatment. Results: Principal component analysis (PCA) models achieve variable results, depending on the size and location of anatomical change. Random changes prevent or degrade PCA’s ability to detect underlying systematic change. RPCA is able to detect smaller systematic changes against the background of random fraction-to-fraction changes and is therefore more successful than SPCA at capturing systematic changes early in treatment. SPCA models were less successful at modeling systematic changes in clinical patient images, which contain a wider range of random motion than synthetic CBCTs, while the regularized approach was able to extract major modes of motion. Conclusions: Leading EDVFs from the both PCA approaches have the potential to capture systematic anatomical change during H&N radiotherapy when systematic changes are large enough with respect to random fraction-to-fraction changes. In all cases the RPCA approach appears to be more reliable at capturing systematic changes, enabling dosimetric consequences to be projected once trends are established early in a treatment course, or based on population models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Precancer Atlas to Drive Precision Prevention Trials

Cancer development is a complex process driven by inherited and acquired molecular and cellular alterations. Prevention is the holy grail of cancer elimination, but making this a reality will take a fundamental rethinking and deep understanding of premalignant biology. Here, we propose a national concerted effort to create a Precancer Atlas (PCA), integrating multi-omics and immunity – basic tenets of the neoplastic process. The biology of neoplasia caused by germline mutations has led to paradigm-changing precision prevention efforts, including: tumor testing for mismatch repair (MMR) deficiency in Lynch syndrome establishing a new paradigm, combinatorial chemoprevention efficacy in familial adenomatous polyposis (FAP), signal of benefit from imaging-based early detection research in high-germline risk for pancreatic neoplasia, elucidating early ontogeny in BRCA1-mutation carriers leading to an international breast cancer prevention trial, and insights into the intricate germline-somatic-immunity interaction landscape. Emerging genetic and pharmacologic (metformin) disruption of mitochondrial (mt) respiration increased autophagy to prevent cancer in a Li-Fraumeni mouse model (biology reproduced in clinical pilot) and revealed profound influences of subtle changes in mt DNA background variation on obesity, aging, and cancer risk. The elaborate communication between the immune system and neoplasia includes an increasingly complex cellular microenvironment and dynamic interactions between host genetics, environmental factors, and microbes in shaping the immune response. Cancer vaccines are in early murine and clinical precancer studies, building on the recent successes of immunotherapy and HPV vaccine immune prevention. Molecular monitoring in Barrett's esophagus to avoid overdiagnosis/treatment highlights an important PCA theme. Next generation sequencing (NGS) discovered age-related clonal hematopoiesis of indeterminate potential (CHIP). Ultra-deep NGS reports over the past year have redefined the premalignant landscape remarkably identifying tiny clones in the blood of up to 95% of women in their 50s, suggesting that potentially premalignant clones are ubiquitous. Similar data from eyelid skin and peritoneal and uterine lavage fluid provide unprecedented opportunities to dissect the earliest phases of stem/progenitor clonal (and microenvironment) evolution/diversity with new single-cell and liquid biopsy technologies. Cancer mutational signatures reflect exogenous or endogenous processes imprinted over time in precursors. In conclusion, accelerating the prevention of cancer will require a large-scale, longitudinal effort, leveraging diverse disciplines (from genetics, biochemistry, and immunology to mathematics, computational biology, and engineering), initiatives, technologies, and models in developing an integrated multi-omics and immunity PCA – an immense national resource to interrogate, target, and intercept events that drive oncogenesis.

59 BASIC BIOLOGICAL SCIENCES↗