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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 OSTI2016

Systematic identification and analysis of frequent gene fusion events in metabolic pathways

Here, gene fusions are the most powerful type of in silico -derived functional associations. However, many fusion compilations were made when <100 genomes were available, and algorithms for identifying fusions need updating to handle the current avalanche of sequenced genomes. The availability of a large fusion dataset would help probe functional associations and enable systematic analysis of where and why fusion events occur. As a result, here we present a systematic analysis of fusions in prokaryotes. We manually generated two training sets: (i) 121 fusions in the model organism Escherichia coli ; (ii) 131 fusions found in B vitamin metabolism. These sets were used to develop a fusion prediction algorithm that captured the training set fusions with only 7 % false negatives and 50 % false positives, a substantial improvement over existing approaches. This algorithm was then applied to identify 3.8 million potential fusions across 11,473 genomes. The results of the analysis are available in a searchable database. A functional analysis identified 3,000 reactions associated with frequent fusion events and revealed areas of metabolism where fusions are particularly prevalent. In conclusion, customary definitions of fusions were shown to be ambiguous, and a stricter one was proposed. Exploring the genes participating in fusion events showed that they most commonly encode transporters, regulators, and metabolic enzymes. The major rationales for fusions between metabolic genes appear to be overcoming pathway bottlenecks, avoiding toxicity, controlling competing pathways, and facilitating expression and assembly of protein complexes. Finally, our fusion dataset provides powerful clues to decipher the biological activities of domains of unknown function.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2022

Exploring Applications of Machine Learning for Wildfire Monitoring and Detection using Unmanned Aerial Vehicles

Wildfires are increasing in frequency and severity around the world, including the United States. The losses caused by wildfires could be mitigated if high-risk areas, hotspots, and flare-ups could be monitored continuously, such as through the use of Unmanned Aerial Vehicles (UAVs). This paper documents exploratory efforts using machine learning to determine efficient flight paths for UAVs and to detect wildfires using image classification. On path planning, three machine learning techniques—Genetic Algorithm, Simulated Annealing, and Dynamic Programming—were explored. Genetic Algorithm was found to be an effective approach for path planning for wildfire monitoring and surveillance by UAVs. For a scenario of 25 locations in a circular arrangement, the algorithm was able to return the optimal path. The accuracy and execution time was found to be sensitive to the algorithm hyperparameters selected, which was especially evident in scenarios with hundreds or thousands of locations. Simulated Annealing was also found to be an effective approach for UAV path planning, with a major benefit of avoiding getting trapped in local minima and being straightforward to implement. Like Genetic Algorithm, the performance of Simulated Annealing was also found to be sensitive to the algorithm hyperparameters selected. By comparison, Dynamic Programming guarantees optimality for any number of locations, but it was found to be less practical in terms of execution time for scenarios with more than about a couple dozen locations. On wildfire detection, image classification using deep learning with a convolutional neural network was explored. Transfer learning was found to be a useful technique to efficiently train deep learning models. Also, it was determined that GPU processing can increase training speed by an order of magnitude, which enables significantly faster development. For a validation test set of 500 images, there were only two false negatives and zero false positives. These results demonstrate that detecting wildfires in static cameras using machine learning is feasible and establish a baseline for using images captured by UAVs in flight for wildfire detection.

Wildfire management↗
DOE OSTI2016

Global Sensitivity Analysis with Small Sample Sizes: Ordinary Least Squares Approach

A new version of global sensitivity analysis is developed in this paper. This new version coupled with tools from statistics, machine learning, and optimization can devise small sample sizes that allow for the accurate ordering of sensitivity coefficients for the first 10-30 most sensitive chemical reactions in complex chemical-kinetic mechanisms, and is particularly useful for studying the chemistry in realistic devices. A key part of the paper is calibration of these small samples. Because these small sample sizes are developed for use in realistic combustion devices, the calibration is done over the ranges of conditions in such devices, with a test case being the operating conditions of a compression ignition engine studied earlier. Compression ignition engines operate under low-temperature combustion conditions with quite complicated chemistry making this calibration difficult, leading to the possibility of false positives and false negatives in the ordering of the reactions. So an important aspect of the paper is showing how to handle the trade-off between false positives and false negatives using ideas from the multiobjective optimization literature. The combination of the new global sensitivity method and the calibration are sample sizes a factor of approximately 10 times smaller than were available with our previous algorithm.

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

Detectability of Varied Hybridization Scenarios Using Genome-Scale Hybrid Detection Methods

Hybridization events complicate the accurate reconstruction of phylogenies, as they lead to patterns of genetic heritability that are unexpected under traditional, bifurcating models of species trees. This phenomenon has led to the development of methods to infer these varied hybridization events, both methods that reconstruct networks directly, as well as summary methods that predict individual hybridization events from a subset of taxa. However, a lack of empirical comparisons between methods – especially those pertaining to large networks with varied hybridization scenarios – hinders their practical use. Here, we provide a comprehensive review of popular summary methods: TICR, MSCquartets, HyDe, Patterson’s D-Statistic (ABBA-BABA), D3, and Dp. TICR and MSCquartets are based on quartet concordance factors gathered from gene tree topologies and HyDe, Patterson’s D-Statistic, D3, and Dp use site pattern frequencies to identify hybridization events between sets of three taxa. We then use simulated data to address questions of method accuracy and ideal use scenarios by testing methods against complex networks which depict gene flow events that differ in depth (timing), quantity (single vs. multiple, overlapping hybridizations), and rate of gene flow (γ). We find that deeper or multiple hybridization events may introduce noise and weaken the signal of hybridization, leading to higher relative false negative rates across all methods. Despite some forms of hybridization eluding quartet-based detection methods, MSCquartets displays high precision in most scenarios. While HyDe results in high false negative rates when tested on hybridizations involving extinct or unsampled ghost lineages, HyDe is the only method able to identify the direction of hybridization, distinguishing the source parental lineages from recipient hybrid lineages. Lastly, we test the methods on a dataset of ultraconserved elements from the bee subfamily Nomiinae, finding possible hybridization events between clades which correspond to regions of poor support in the species tree estimated in a previous study.

Bjorner, Marianne B.↗
DOE OSTI2014

Magnetic resonance imaging-targeted, 3D transrectal ultrasound-guided fusion biopsy for prostate cancer: Quantifying the impact of needle delivery error on diagnosis

Purpose: Magnetic resonance imaging (MRI)-targeted, 3D transrectal ultrasound (TRUS)-guided “fusion” prostate biopsy intends to reduce the ∼23% false negative rate of clinical two-dimensional TRUS-guided sextant biopsy. Although it has been reported to double the positive yield, MRI-targeted biopsies continue to yield false negatives. Therefore, the authors propose to investigate how biopsy system needle delivery error affects the probability of sampling each tumor, by accounting for uncertainties due to guidance system error, image registration error, and irregular tumor shapes. Methods: T2-weighted, dynamic contrast-enhanced T1-weighted, and diffusion-weighted prostate MRI and 3D TRUS images were obtained from 49 patients. A radiologist and radiology resident contoured 81 suspicious regions, yielding 3D tumor surfaces that were registered to the 3D TRUS images using an iterative closest point prostate surface-based method to yield 3D binary images of the suspicious regions in the TRUS context. The probabilityP of obtaining a sample of tumor tissue in one biopsy core was calculated by integrating a 3D Gaussian distribution over each suspicious region domain. Next, the authors performed an exhaustive search to determine the maximum root mean squared error (RMSE, in mm) of a biopsy system that gives P ≥ 95% for each tumor sample, and then repeated this procedure for equal-volume spheres corresponding to each tumor sample. Finally, the authors investigated the effect of probe-axis-direction error on measured tumor burden by studying the relationship between the error and estimated percentage of core involvement. Results: Given a 3.5 mm RMSE for contemporary fusion biopsy systems,P ≥ 95% for 21 out of 81 tumors. The authors determined that for a biopsy system with 3.5 mm RMSE, one cannot expect to sample tumors of approximately 1 cm{sup 3} or smaller with 95% probability with only one biopsy core. The predicted maximum RMSE giving P ≥ 95% for each tumor was consistently greater when using spherical tumor shapes as opposed to no shape assumption. However, an assumption of spherical tumor shape for RMSE = 3.5 mm led to a mean overestimation of tumor sampling probabilities of 3%, implying that assuming spherical tumor shape may be reasonable for many prostate tumors. The authors also determined that a biopsy system would need to have a RMS needle delivery error of no more than 1.6 mm in order to sample 95% of tumors with one core. The authors’ experiments also indicated that the effect of axial-direction error on the measured tumor burden was mitigated by the 18 mm core length at 3.5 mm RMSE. Conclusions: For biopsy systems with RMSE ≥ 3.5 mm, more than one biopsy core must be taken from the majority of tumors to achieveP ≥ 95%. These observations support the authors’ perspective that some tumors of clinically significant sizes may require more than one biopsy attempt in order to be sampled during the first biopsy session. This motivates the authors’ ongoing development of an approach to optimize biopsy plans with the aim of achieving a desired probability of obtaining a sample from each tumor, while minimizing the number of biopsies. Optimized planning of within-tumor targets for MRI-3D TRUS fusion biopsy could support earlier diagnosis of prostate cancer while it remains localized to the gland and curable.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Submixture model to predict nepheline precipitation in waste glasses

Abstract High‐alumina high‐level waste ( HLW ) glasses are prone to nepheline precipitation during canister‐centerline cooling ( CCC ). If sufficient nepheline forms, the chemical durability of the glass will be significantly impacted. Overly conservative constraints have been developed and used to avoid the deleterious effects of nepheline formation in U.S. HLW glasses. The constraints used have been shown to significantly limit the loading of waste in glass at Hanford and therefore the cost and schedule of cleanup. A 90‐glass study was performed to develop an improved understanding of the impacts of glass composition on the formation of nepheline during CCC . The CCC crystallinity data from these glasses were combined with 657 glasses found in the literature. The trends showed significant effects of Na 2 O, Al 2 O 3 , SiO 2 , B 2 O 3 , CaO, Li 2 O, and potentially K 2 O on the propensity for nepheline formation. A pseudo‐ternary submixture model was proposed to identify the glass composition region prone to nepheline precipitation. This pseudo‐ternary with axes of SiO 2 + 1.98B 2 O 3 , Na 2 O + 0.653Li 2 O + 0.158CaO, and Al 2 O 3 was found to divide glasses that precipitate nepheline during CCC from those that do not. Application of this constraint is anticipated to increase the loading of Hanford high‐alumina HLW s in glass by roughly one‐third.

Vienna, John D.↗
DOE OSTI2023

Using electronic health record metadata to predict housing instability amongst veterans

Housing instability is considered a significant life stressor and preemptive screening should be applied to identify those at risk for homelessness as early as possible so that they can be targeted for specialized care. We developed models to classify patient outcomes for an established VA Homelessness Screening Clinical Reminder (HSCR), which identifies housing instability, in the two months prior to its administration. Logistic Regression and Random Forest models were fit to classify responses using the last 18 months of document activity. We measure concentration of risk across stratifications of predicted probability and observe an enriched likelihood of finding confirmed false negative responses from veterans with diagnosed housing instability. Positive responses were 34 times more likely to be detected within the top 1 % of patients predicted at risk than from those randomly selected. There is a 1 in 4 chance of detecting false negatives within the top 1 % of predicted risk. Machine learning methods can classify between episodes of housing instability using a data-driven approach that does not rely on variables curated from domain experts. This method has the potential to improve clinicians’ ability to identify veterans who are experiencing housing instability but are not captured by HSCR.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2011

Development of a Multicomponent Prediction Model for Acute Esophagitis in Lung Cancer Patients Receiving Chemoradiotherapy

Purpose: To construct a model for the prediction of acute esophagitis in lung cancer patients receiving chemoradiotherapy by combining clinical data, treatment parameters, and genotyping profile. Patients and Methods: Data were available for 273 lung cancer patients treated with curative chemoradiotherapy. Clinical data included gender, age, World Health Organization performance score, nicotine use, diabetes, chronic disease, tumor type, tumor stage, lymph node stage, tumor location, and medical center. Treatment parameters included chemotherapy, surgery, radiotherapy technique, tumor dose, mean fractionation size, mean and maximal esophageal dose, and overall treatment time. A total of 332 genetic polymorphisms were considered in 112 candidate genes. The predicting model was achieved by lasso logistic regression for predictor selection, followed by classic logistic regression for unbiased estimation of the coefficients. Performance of the model was expressed as the area under the curve of the receiver operating characteristic and as the false-negative rate in the optimal point on the receiver operating characteristic curve. Results: A total of 110 patients (40%) developed acute esophagitis Grade {>=}2 (Common Terminology Criteria for Adverse Events v3.0). The final model contained chemotherapy treatment, lymph node stage, mean esophageal dose, gender, overall treatment time, radiotherapy technique, rs2302535 (EGFR), rs16930129 (ENG), rs1131877 (TRAF3), and rs2230528 (ITGB2). The area under the curve was 0.87, and the false-negative rate was 16%. Conclusion: Prediction of acute esophagitis can be improved by combining clinical, treatment, and genetic factors. A multicomponent prediction model for acute esophagitis with a sensitivity of 84% was constructed with two clinical parameters, four treatment parameters, and four genetic polymorphisms.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2010

A More Efficient, Radiation-Free Alternative to Systematic Chest X-Ray for the Detection of Embolized Seeds to the Lung

Purpose: To evaluate the efficacy of a seed-migration detector and to compare its performance to fluoroscopy and postoperative chest radiographs. Methods and Materials: A gamma scintillation survey meter was converted to a seed-migration detector by adding a shield on the probe detection window. The detector response to three {sup 125}I seed activities was characterized for different source-to-detector distances in water. The detector was used to perform a chest evaluation on 737 patients at their first postoperative visit. When the detector showed positive activity, seed migration was confirmed by taking a chest radiograph and by looking at the region with fluoroscopy. Results: One hundred and three patients (14.0%) presented at least one embolized seed. This accounts for 123 of the 39,887 seeds. Eighty-seven, 12, and 4 patients had respectively one, two, and three seed embolization. Compared with the seed-migration detector, detection based on fluoroscopy would have led to 13 false-negative detections (of 103, or 12.6%), and the radiograph would have resulted in 31 or 30.1%. More important, standard chest X-ray would have required a survey and extra radiation dose to lung to 100% of the patients, rather than the 14% who required it. Conclusions: The usual recommendation to perform chest radiographs at the first follow-up visit to scan lungs for embolized seeds should be revised because of the high false-negative rate. Scintillator-based gamma counter detector provides superior detection sensitivity and should be adopted as a standard of practice. Chest X-ray could be limited to documenting cases of positive migration.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Patterns in the Use of Axillary Operations for Patients with Node-Positive Breast Cancer After Neoadjuvant Chemotherapy: A National Cancer Database (NCDB) Analysis

Background: The American College of Surgeons Oncology Group (ACOSOG) Z1071 and Sentinel Neoadjuvant (SENTINA) trials of sentinel node biopsy for node-positive breast cancer treated with neoadjuvant chemotherapy (NAC) demonstrated false-negative rates that varied on the basis of surgical technique. This study evaluated trends in axillary operations before and after publication of these trials. Methods: This study analyzed patients from National Cancer Database (NCDB) with clinical T0 through T4, N1 and N2, M0 breast cancer who received NAC from 1 January 2012 to 31 December 2015 and sentinel lymph node biopsy (SNB) or axillary lymph node dissection (ALND). The patients were divided into the following groups: SNB, ALND, and (SNB + ALND). Results: Of the 32,036 evaluable patients identified in this study. 5565 had SNB, 19,930 had ALND, and 6541 had SNB + ALND. Compared with the ALND group, the SNB group was younger, had more invasive ductal cancers, and had lower clinical T- and N-stage disease (p < 0.001). The patients in the SNB group had a higher rate of estrogen receptor-positive and triple-negative breast cancers, but a lower rate of human epidermal growth factor receptor 2 (HER2)-positive cancer (p < 0.001). The nodal pathologic complete response (PCR) rate, defined as no residual invasive cancer, was 66.5% in the SNB group and 33.1% in the ALND group. Since 2013, the rate of ALND has decreased from 88.7 to 77.1% in both community and academic institutions (p < 0.001). Conclusion: Since publication of the ACOSOG Z1071 and SENTINA trials, the national rates of ALND in node positive breast cancer treated with NAC have decreased despite reported false-negative SNB rates and lack of prospective outcome data regarding the oncologic safety of ALND omission.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

SU-D-202-02: Quantitative Imaging: Correlation Between Image Feature Analysis and the Accuracy of Manually Drawn Contours On PET Images

Purpose: To evaluate the correlation between image features and the accuracy of manually drawn target contours on synthetic PET images Methods: A digital PET phantom was used in combination with Monte Carlo simulation to create a set of 26 simulated PET images featuring a variety of tumor shapes and activity heterogeneity. These tumor volumes were used as a gold standard in comparisons with manual contours delineated by 10 radiation oncologist on the simulated PET images. Metrics used to evaluate segmentation accuracy included the dice coefficient, false positive dice, false negative dice, symmetric mean absolute surface distance, and absolute volumetric difference. Image features extracted from the simulated tumors consisted of volume, shape complexity, mean curvature, and intensity contrast along with five texture features derived from the gray-level neighborhood difference matrices including contrast, coarseness, busyness, strength, and complexity. Correlation between these features and contouring accuracy were examined. Results: Contour accuracy was reasonably well correlated with a variety of image features. Dice coefficient ranged from 0.7 to 0.90 and was correlated closely with contrast (r=0.43, p=0.02) and complexity (r=0.5, p<0.001). False negative dice ranged from 0.10 to 0.50 and was correlated closely with contrast (r=0.68, p<0.001) and complexity (r=0.66, p<0.001). Absolute volumetric difference ranged from 0.0002 to 0.67 and was correlated closely with coarseness (r=0.46, p=0.02) and complexity (r=0.49, p=0.008). Symmetric mean absolute difference ranged from 0.02 to 1 and was correlated closely with mean curvature (r=0.57, p=0.02) and contrast (r=0.6, p=0.001). Conclusion: The long term goal of this study is to assess whether contouring variability can be reduced by providing feedback to the practitioner based on image feature analysis. The results are encouraging and will be used to develop a statistical model which will enable a prediction of contour accuracy based purely on image feature analysis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Prostate-Specific Antigen Velocity Before and After Elimination of Factors That Can Confound the Prostate-Specific Antigen Level

Purpose: Prostate-specific antigen (PSA) velocity, like PSA level, can be confounded. In this study, we estimated the impact that confounding factors could have on correctly identifying a patient with a PSA velocity >2 ng/ml/y. Methods and Materials: Between 2006 and 2010, a total of 50 men with newly diagnosed PC comprised the study cohort. We calculated and compared the false-positive and false-negative PSA velocity >2 ng/ml/y rates for all men and those with low-risk disease using two approaches to calculate PSA velocity. First, we used PSA values obtained within 18 months of diagnosis; second, we used values within 18 months of diagnosis, substituting the prebiopsy PSA for a repeat, nonconfounded PSA that was obtained using the same assay and without confounders. Results: Using PSA levels pre-biopsy, 46% of all men had a PSA velocity >2 ng/ml/y; whereas this value declined to 32% when substituting the last prebiopsy PSA for a repeat, nonconfounded PSA using the same assay and without confounders. The false-positive rate for PSA velocity >2 ng/ml/y was 43% as compared with a false-negative rate of PSA velocity >2 ng/ml/y of 11% (p = 0.0008) in the overall cohort. These respective values in the low-risk subgroup were 60% and 16.7% (p = 0.09). Conclusion: This study provides evidence to explain the discordance in cancer-specific outcomes among groups investigating the prognostic significance of PSA velocity >2 ng/ml/y, and highlights the importance of patient education on potential confounders of the PSA test before obtaining PSA levels.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

3D DVH-based metric analysis versus per-beam planar analysis in IMRT pretreatment verification

Purpose: To evaluate methods of pretreatment IMRT analysis, using real measurements performed with a commercial 2D detector array, for clinical relevance and accuracy by comparing clinical DVH parameters. Methods: We divided the work into two parts. The first part consisted of six in-phantom tests aimed to study the sensitivity of the different analysis methods. Beam fluences, 3D dose distribution, and DVH of an unaltered original plan were compared to those of the delivered plan, in which an error had been intentionally introduced. The second part consisted of comparing gamma analysis with DVH metrics for 17 patient plans from various sites. Beam fluences were measured with the MapCHECK 2 detector, per-beam planar analysis was performed with the MapCHECK software, and 3D gamma analysis and the DVH evaluation were performed using 3DVH software. Results: In a per-beam gamma analysis some of the tests yielded false positives or false negatives. However, the 3DVH software correctly described the DVH of the plan which included the error. The measured DVH from the plan with controlled error agreed with the planned DVH within 2% dose or 2% volume. We also found that a gamma criterion of 3%/3 mm was too lax to detect some of the forced errors. Global analysis masked some problems, while local analysis magnified irrelevant errors at low doses. Small hotspots were missed for all metrics due to the spatial resolution of the detector panel. DVH analysis for patient plans revealed small differences between treatment plan calculations and 3DVH results, with the exception of very small volume structures such as the eyes and the lenses. Target coverage (D{sub 98} and D{sub 95}) of the measured plan was systematically lower than that predicted by the treatment planning system, while other DVH characteristics varied depending on the parameter and organ. Conclusions: We found no correlation between the gamma index and the clinical impact of a discrepancy for any of the gamma index evaluation possibilities (global, local, 2D, or 3D). Some of the tests yielded false positives or false negatives in a per-beam gamma analysis. However, they were correctly accounted for in a DVH analysis. We also showed that 3DVH software is reliable for our tests, and is a viable method for correlating planar discrepancies with clinical relevance by comparing the measured DVH of target and OAR's with clinical tolerance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Optimally configuring a measurement system to detect diversions from a nuclear fuel cycle

The civilian nuclear fuel cycle is an industrial process that produces electrical power from the nuclear fission of uranium. Using a measurement system to accurately account for nuclear material, such as uranium, in a fuel cycle is important because of the possible loss or diversion of this potentially dangerous material. A measurement system is defined by a set of measurement methods, or “devices,” used to account for material flows and inventory values at specific locations in the fuel cycle. Here, we develop a simulation-optimization algorithm and an integer-programming model to find the best, or near-best, resource-limited measurement system with a high degree of confidence. The simulation-optimization algorithm minimizes a weighted sum of false positive and false negative diversion-detection probabilities while accounting for material quantities and measurement errors across a finite, discrete time horizon in hypothetical non-diversion and diversion contexts. In each time period, the estimated cumulative material unaccounted for is compared to a fixed or an optimized threshold value to assess if a “significant amount of material” is lost from a measurement system. The integer-programming model minimizes the population variance of the estimated material loss, i.e., material unaccounted for, in a measurement system. We analyze three potential problems in nuclear fuel cycle measurement systems: (i) given location-dependent device precisions, find the configuration of n devices at n locations ( n = 3) that gives the lowest corresponding objective values using the simulation-optimization algorithm and integer-programming model, (ii) find the location at which improving device precision reduces objective values the most using the simulation-optimization algorithm, and determine the effect of measurement frequency on measurement system configurations and objective values using the simulation-optimization algorithm. We obtain comparable results for each problem at least an order of magnitude faster than existing methods do. Using an optimized, rather than fixed, detection threshold in the simulation-optimization algorithm reduces the weighted sum of false positive and false negative probabilities.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2011

Intraoperative Imaging Guidance for Sentinel Node Biopsy in Melanoma Using a Mobile Gamma Camera

The objective is to evaluate the sensitivity and clinical utility of intraoperative mobile gamma camera (MGC) imaging in sentinel lymph node biopsy (SLNB) in melanoma. The false-negative rate for SLNB for melanoma is approximately 17%, for which failure to identify the sentinel lymph node (SLN) is a major cause. Intraoperative imaging may aid in detection of SLN near the primary site, in ambiguous locations, and after excision of each SLN. The present pilot study reports outcomes with a prototype MGC designed for rapid intraoperative image acquisition. We hypothesized that intraoperative use of the MGC would be feasible and that sensitivity would be at least 90%. From April to September 2008, 20 patients underwent Tc99 sulfur colloid lymphoscintigraphy, and SLNB was performed with use of a conventional fixed gamma camera (FGC), and gamma probe followed by intraoperative MGC imaging. Sensitivity was calculated for each detection method. Intraoperative logistical challenges were scored. Cases in which MGC provided clinical benefit were recorded. Sensitivity for detecting SLN basins was 97% for the FGC and 90% for the MGC. A total of 46 SLN were identified: 32 (70%) were identified as distinct hot spots by preoperative FGC imaging, 31 (67%) by preoperative MGC imaging, and 43 (93%) by MGC imaging pre- or intraoperatively. The gamma probe identified 44 (96%) independent of MGC imaging. The MGC provided defined clinical benefit as an addition to standard practice in 5 (25%) of 20 patients. Mean score for MGC logistic feasibility was 2 on a scale of 1-9 (1 = best). Intraoperative MGC imaging provides additional information when standard techniques fail or are ambiguous. Sensitivity is 90% and can be increased. This pilot study has identified ways to improve the usefulness of an MGC for intraoperative imaging, which holds promise for reducing false negatives of SLNB for melanoma.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Toward adaptive radiotherapy for head and neck patients: Uncertainties in dose warping due to the choice of deformable registration algorithm

Purpose: The aims of this work were to evaluate the performance of several deformable image registration (DIR) algorithms implemented in our in-house software (NiftyReg) and the uncertainties inherent to using different algorithms for dose warping. Methods: The authors describe a DIR based adaptive radiotherapy workflow, using CT and cone-beam CT (CBCT) imaging. The transformations that mapped the anatomy between the two time points were obtained using four different DIR approaches available in NiftyReg. These included a standard unidirectional algorithm and more sophisticated bidirectional ones that encourage or ensure inverse consistency. The forward (CT-to-CBCT) deformation vector fields (DVFs) were used to propagate the CT Hounsfield units and structures to the daily geometry for “dose of the day” calculations, while the backward (CBCT-to-CT) DVFs were used to remap the dose of the day onto the planning CT (pCT). Data from five head and neck patients were used to evaluate the performance of each implementation based on geometrical matching, physical properties of the DVFs, and similarity between warped dose distributions. Geometrical matching was verified in terms of dice similarity coefficient (DSC), distance transform, false positives, and false negatives. The physical properties of the DVFs were assessed calculating the harmonic energy, determinant of the Jacobian, and inverse consistency error of the transformations. Dose distributions were displayed on the pCT dose space and compared using dose difference (DD), distance to dose difference, and dose volume histograms. Results: All the DIR algorithms gave similar results in terms of geometrical matching, with an average DSC of 0.85 ± 0.08, but the underlying properties of the DVFs varied in terms of smoothness and inverse consistency. When comparing the doses warped by different algorithms, we found a root mean square DD of 1.9% ± 0.8% of the prescribed dose (pD) and that an average of 9% ± 4% of voxels within the treated volume failed a 2%pD DD-test (DD{sub 2%-pp}). Larger DD{sub 2%-pp} was found within the high dose gradient (21% ± 6%) and regions where the CBCT quality was poorer (28% ± 9%). The differences when estimating the mean and maximum dose delivered to organs-at-risk were up to 2.0%pD and 2.8%pD, respectively. Conclusions: The authors evaluated several DIR algorithms for CT-to-CBCT registrations. In spite of all methods resulting in comparable geometrical matching, the choice of DIR implementation leads to uncertainties in dose warped, particularly in regions of high gradient and/or poor imaging quality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

An image-guidance system for dynamic dose calculation in prostate brachytherapy using ultrasound and fluoroscopy

Purpose: Brachytherapy is a standard option of care for prostate cancer patients but may be improved by dynamic dose calculation based on localized seed positions. The American Brachytherapy Society states that the major current limitation of intraoperative treatment planning is the inability to localize the seeds in relation to the prostate. An image-guidance system was therefore developed to localize seeds for dynamic dose calculation. Methods: The proposed system is based on transrectal ultrasound (TRUS) and mobile C-arm fluoroscopy, while using a simple fiducial with seed-like markers to compute pose from the nonencoded C-arm. Three or more fluoroscopic images and an ultrasound volume are acquired and processed by a pipeline of algorithms: (1) seed segmentation, (2) fiducial detection with pose estimation, (3) seed matching with reconstruction, and (4) fluoroscopy-to-TRUS registration. Results: The system was evaluated on ten phantom cases, resulting in an overall mean error of 1.3 mm. The system was also tested on 37 patients and each algorithm was evaluated. Seed segmentation resulted in a 1% false negative rate and 2% false positive rate. Fiducial detection with pose estimation resulted in a 98% detection rate. Seed matching with reconstruction had a mean error of 0.4 mm. Fluoroscopy-to-TRUS registration had a mean error of 1.3 mm. Moreover, a comparison of dose calculations between the authors’ intraoperative method and an independent postoperative method shows a small difference of 7% and 2% forD{sub 90} and V{sub 100}, respectively. Finally, the system demonstrated the ability to detect cold spots and required a total processing time of approximately 1 min. Conclusions: The proposed image-guidance system is the first practical approach to dynamic dose calculation, outperforming earlier solutions in terms of robustness, ease of use, and functional completeness.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2011

FY11 Report on Metagenome Analysis using Pathogen Marker Libraries

A method, sequence library, and software suite was invented to rapidly assess whether any member of a pre-specified list of threat organisms or their near neighbors is present in a metagenome. The system was designed to handle mega- to giga-bases of FASTA-formatted raw sequence reads from short or long read next generation sequencing platforms. The approach is to pre-calculate a viral and a bacterial "Pathogen Marker Library" (PML) containing sub-sequences specific to pathogens or their near neighbors. A list of expected matches comparing every bacterial or viral genome against the PML sequences is also pre-calculated. To analyze a metagenome, reads are compared to the PML, and observed PML-metagenome matches are compared to the expected PML-genome matches, and the ratio of observed relative to expected matches is reported. In other words, a 3-way comparison among the PML, metagenome, and existing genome sequences is used to quickly assess which (if any) species included in the PML is likely to be present in the metagenome, based on available sequence data. Our tests showed that the species with the most PML matches correctly indicated the organism sequenced for empirical metagenomes consisting of a cultured, relatively pure isolate. These runs completed in 1 minute to 3 hours on 12 CPU (1 thread/CPU), depending on the metagenome and PML. Using more threads on the same number of CPU resulted in speed improvements roughly proportional to the number of threads. Simulations indicated that detection sensitivity depends on both sequencing coverage levels for a species and the size of the PML: species were correctly detected even at ~0.003x coverage by the large PMLs, and at ~0.03x coverage by the smaller PMLs. Matches to true positive species were 3-4 orders of magnitude higher than to false positives. Simulations with short reads (36 nt and ~260 nt) showed that species were usually detected for metagenome coverage above 0.005x and coverage in the PML above 0.05x, and detection probability appears to be a function of both coverages. Multiple species could be detected simultaneously in a simulated low-coverage, complex metagenome, and the largest PML gave no false negative species and no false positive genera. The presence of multiple species was predicted in a complex metagenome from a human gut microbiome with 1.9 GB of short reads (75 nt); the species predicted were reasonable gut flora and no biothreat agents were detected, showing the feasibility of PML analysis of empirical complex metagenomes.

59 BASIC BIOLOGICAL SCIENCES↗