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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Half-Bridge On-State Voltage Sensor for In-Situ Measurements

Current prognostics for life expectancy of power semiconductors utilize off-line measurements of the on-state resistance. Presented is a half-bridge (HB) on-state voltage (VON) sensor capable of being utilized for in-situ measurements enabling continuous and real-time monitoring of the on-state resistance. The design provides a single VON measurement output that contains independent VON information of both high and low side devices in a HB leg. The measurement output is referenced from the middle point of the HB and combines the two complementary VON sensing outputs. This system reduces the number of components, required number of analog signal processing circuits, and ADC channels for in-situ health monitoring of a power electronics system. These reductions will facilitate integration of the VON sensor with the gate driver board and achieve low system profile. The experimental results demonstrate the proposed VON measurements while switching with inductive and resistive loads.

Roy, Chondon↗
DOE OSTI2011

Developing Effective Continuous On-Line Monitoring Technologies to Manage Service Degradation of Nuclear Power Plants

Recently, there has been increased interest in using prognostics (i.e, remaining useful life (RUL) prediction) for managing and mitigating aging effects in service-degraded passive nuclear power reactor components. A vital part of this philosophy is the development of tools for detecting and monitoring service-induced degradation. Experience with in-service degradation has shown that rapidly-growing cracks, including several varieties of stress corrosion cracks (SCCs), can grow through a pipe in less than one fuel outage cycle after they initiate. Periodic inspection has limited effectiveness at detecting and managing such degradation requiring a more versatile monitoring philosophy. Acoustic emission testing (AET) and guided wave ultrasonic testing (GUT) are related technologies with potential for on-line monitoring applications. However, harsh operating conditions within NPPs inhibit the widespread implementation of both technologies. For AET, another hurdle is the attenuation of passive degradation signals as they travel though large components, relegating AET to targeted applications. GUT is further hindered by the complexity of GUT signatures limiting its application to the inspection of simple components. The development of sensors that are robust and inexpensive is key to expanding the use of AET and GUT for degradation monitoring in NPPs and improving overall effectiveness. Meanwhile, the effectiveness of AET and GUT in NPPs can be enhanced through thoughtful application of tandem AET-GUT techniques.

acoustic emission↗
DOE OSTI2020

Sensing Service Transformer Secondary Currents using Planar Magnetic Pick-up Coils

The rapid deployment of distributed energy resources (DERs) is creating stresses on utility assets in the distribution networks. The service transformer, which is the most commonly used utility asset is often not monitored, since the cost of available sensing solutions is as high as the transformer itself. This paper presents a method to use an array of printed circuit board coils to intelligently monitor North American pole top service transformers. The approach, uses a novel, self-calibrating algorithm to infer the fixed geometry of the installation, by recording data over an extended period of time. The proposed method can enable rapid installation of a non-intrusive sensor that can be used to monitor loading levels of the distribution transformer. Furthermore, these sensors can help in planning, prognostics and advanced situational awareness for utilities.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Foreword: Special Section on Multiphysics Aspects of Power Electronics Packaging - Power Die, Power Module, and Converter Level - Part 1

Power electronics are increasingly being used to condition electricity for a wide array of applications, such as transportation (on land, air, and water), data centers, radio frequency, directed energy, wind, solar, and grid-tied applications. To increase power density, performance, efficiency, and reliability and reduce cost, innovations and developments are needed in the multiphysics packaging of power electronics at a die, module, and converter level. This includes fundamental research and development related to emerging high-voltage, high-temperature, and high-switching-frequency power electronics; packaging materials; thermal materials and interfaces; fluid-based thermal management technologies; reliability; condition monitoring; and prognostics. To address these important aspects, this Special Section on Multiphysics Aspects of Power Electronics Packaging includes several articles to be published in two parts. More details are given below on the articles included in the first part.

condition monitoring↗
DOE OSTI2024

Foreword: Special Section on Multiphysics Aspects of Power Electronics Packaging—Power Die, Power Module, and Converter Level: Part 2

Power electronics are increasingly being used to condition electricity for a wide array of applications, such as transportation (on land, air, and water), data centers, radio frequency, directed energy, wind, solar, and grid-tied applications. Here, to increase power density, performance, efficiency, and reliability-as well as to reduce cost-innovations and developments are needed in the multiphysics packaging of power electronics at a die, module, and converter level. This includes fundamental R&D related to emerging high-voltage, high-temperature, and high-switching-frequency power electronics, packaging materials, thermal materials and interfaces, fluid-based thermal management technologies, reliability, condition monitoring, and prognostics. Latest developments in this area are published as a Special Section on Multiphysics Aspects of Power Electronics Packaging. The first part was published in the May 2024 issue of the IEEE Transactions on Components, Packaging and Manufacturing Technology (Volume 14, Issue 5). The second part of that Special Section is being published in this issue. A brief summary of the papers included in the second part are given below.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Recursive Use of the Short-Time Fast Fourier Transform for Signature Analysis in Continuous Processes

Although a nuclear reactor is a hostile environment for sensing and electrical communications, the reactor core is amenable to acoustic communication. An acoustic measurement infrastructure (AMI) has been installed in the Advanced Test Reactor (ATR) to record acoustic signals that can capture its different operating regimes. AMI uses coolant pumps as continuous signal sources, coolant and structural components as transmission lines, and accelerometers to capture system motion. A recursive signal processing technique based on the short-time fast Fourier transform (STFFT) for continuous processes provides unique signatures for diagnostic and prognostic analyses from the system motion data. Here this article presents a recursive STFFT methodology that processes acoustic signals from continuous industrial processes. The article first discusses the initial STFFT use with simulated data to elucidate the basic principles necessary to understand and interpret the STFFT results from actual pump vibration data. Each repetitive use of the STFFT on pump vibration data using the results from the prior STFFT processing will generate additional complimentary time-frequency-based signatures. These signatures are generated by the coolant pumps operating under different process conditions. After each use of the STFFT, the resulting signatures provide exemplary examples of the diversity and intuitive nature of recursively using the STFFT. This article focuses on recursively using the STFFT to provide numerous complimentary and diverse signatures that will ultimately be inputs for machine learning algorithms that provide predictive data analytics. The intuitive nature of the information and signatures from recursive STFFT processing will also bring intuitive interpretation capabilities to machine learning and predictive data analytic techniques.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Combining glomerular basement membrane and tubular basement membrane assessment improves the prediction of diabetic end‐stage renal disease

Abstract Background To address the prognostic value of combining tubular basement membrane (TBM) and glomerular basement membrane (GBM) thickness in diabetic nephropathy (DN). Methods This retrospective study enrolled 110 patients with type 2 diabetes and biopsy‐proven DN from 2011 to 2018. The pathological findings were confirmed according to the Renal Pathology Society classifications. GBM and TBM thicknesses were determined using the Haas' direct measurement/arithmetic mean method and orthogonal intercept method, respectively. Cox proportional hazard models were used to investigate the hazard ratios (HRs) for the influence of combined GBM and TBM thickness for predicting end‐stage renal disease (ESRD). Results Patients were assigned to three groups according to the median GBM and TBM thickness: GBM lo TBM lo (GBM < 681 nm and TBM < 1200 nm), GBM hi TBM lo /GBM lo TBM hi (GBM ≥ 681 nm and TBM < 1200 nm, or GBM < 681 nm and TBM ≥ 1200 nm), and GBM hi TBM hi (GBM ≥ 681 nm and TBM ≥ 1200 nm). The GBM hi TBM lo /GBM lo TBM hi and GBM hi TBM hi groups displayed poorer renal function, a more severe glomerular classification and interstitial inflammation, and poorer renal survival rates than the GBM lo TBM lo group The GBM hi TBM lo /GBM lo TBM hi and GBM hi TBM hi groups had adjusted HRs of 1.49 (95% confidence interval [CI], 1.21‐9.75) and 3.07 (95% CI, 2.87‐12.78), respectively, compared with the GBM lo TBM lo group. Conclusions TBM thickness enhanced GBM thickness for renal prognosis in patients with type 2 diabetes.

Zhao, Lijun↗
DOE OSTI2015

Incorporating phosphorus cycling into global modeling efforts: a worthwhile, tractable endeavor

Myriad field, laboratory, and modeling studies show that nutrient availability plays a fundamental role in regulating CO 2 exchange between the Earth's biosphere and atmosphere, and in determining how carbon pools and fluxes respond to climatic change. Accordingly, global models that incorporate coupled climate-carbon cycle feedbacks made a significant advance with the introduction of a prognostic nitrogen cycle. Here we propose that incorporating phosphorus cycling represents an important next step in coupled climate-carbon cycling model development, particularly for lowland tropical forests where phosphorus availability is often presumed to limit primary production. We highlight challenges to including phosphorus in modeling efforts and provide suggestions for how to move forward.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Predictive Modeling of Local Film-Cooling Flow on a Turbine Rotor Blade

Abstract In the turbine section of a modern gas turbine engine, components exposed to the main gas path flow rely on cooling air to maintain hardware durability targets. Therefore, monitoring turbine cooling flow is essential to the diagnostic and prognostic efficacy of a condition-based operation and maintenance (CBOM) approach. This study supports CBOM goals by leveraging supervised machine learning to estimate relative changes to local film-cooling flowrate using surface temperature measured on the pressure side of a rotating turbine blade operating at engine-relevant aerothermal conditions. Throughout the lifetime of a film-cooled turbine component, characteristics of the film-cooling flow—such as film trajectory and cooling effectiveness—vary as degradation-driven geometry distortions occur, which ultimately affects the relationship between the model input and the model output—film-cooling flowrate predictions. The present study addresses this complication by testing a data-driven model on multiple turbine blades of the same nominal design, but with each blade exhibiting different localized film-cooling flow characteristics. By testing the model in this manner, strategies for mitigating the detrimental effects of film-cooling flow characteristic variations on model performance were investigated, and the corresponding flowrate prediction accuracy was quantified.

Engineering↗
DOE OSTI2026

Uncertainty quantification of fireball features extracted from nuclear test films using computer vision

Films from the US’s historic nuclear testing era comprise the only extensive collection of imagery depicting high-yield detonations. These films offer unique insights into the characteristics of flows occurring on scales that are difficult to replicate experimentally, and they are a valuable source of data for the validation of models used to describe nuclear detonations. In recent work, we implemented modern computer vision and machine learning techniques to extract features of the fireball following nuclear detonation. With a training dataset of fireball films, we fine-tuned a You Only Look Once 11 (YOLO11) model to detect and track the fireball. Applied to a video, the outer bounding box produced in each frame by YOLO11 is used as an input prompt to Meta’s Segment Anything Model 2 (SAM2), which is shown to accurately predict the boundary of the fireball over time with high resolution. These state-of-the-art computer vision foundation models exhibit impressive visual accuracy in their results but lack an output of values that robustly quantify uncertainty in scientific applications. In this paper, we develop procedures for uncertainty quantification of extracted fireball features. We outline the application of a parallel attention mechanism to calculate uncertainty ranges that complement and better pose model validation data. This higher quality fireball validation data may serve to improve prognostic models describing nuclear detonations in support of nuclear forensic and emergency response activities.

Khristy, Joel [ORNL] (ORCID:0000000209963060)↗
DOE OSTI2014

Predicting oropharyngeal tumor volume throughout the course of radiation therapy from pretreatment computed tomography data using general linear models

Purpose: The purpose of this work was to develop and evaluate the accuracy of several predictive models of variation in tumor volume throughout the course of radiation therapy. Methods: Nineteen patients with oropharyngeal cancers were imaged daily with CT-on-rails for image-guided alignment per an institutional protocol. The daily volumes of 35 tumors in these 19 patients were determined and used to generate (1) a linear model in which tumor volume changed at a constant rate, (2) a general linear model that utilized the power fit relationship between the daily and initial tumor volumes, and (3) a functional general linear model that identified and exploited the primary modes of variation between time series describing the changing tumor volumes. Primary and nodal tumor volumes were examined separately. The accuracy of these models in predicting daily tumor volumes were compared with those of static and linear reference models using leave-one-out cross-validation. Results: In predicting the daily volume of primary tumors, the general linear model and the functional general linear model were more accurate than the static reference model by 9.9% (range: −11.6%–23.8%) and 14.6% (range: −7.3%–27.5%), respectively, and were more accurate than the linear reference model by 14.2% (range: −6.8%–40.3%) and 13.1% (range: −1.5%–52.5%), respectively. In predicting the daily volume of nodal tumors, only the 14.4% (range: −11.1%–20.5%) improvement in accuracy of the functional general linear model compared to the static reference model was statistically significant. Conclusions: A general linear model and a functional general linear model trained on data from a small population of patients can predict the primary tumor volume throughout the course of radiation therapy with greater accuracy than standard reference models. These more accurate models may increase the prognostic value of information about the tumor garnered from pretreatment computed tomography images and facilitate improved treatment management.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

SU-D-9A-02: Relative Effects of Threshold Choice and Spatial Resolution Modeling On SUV and Volume Quantification in F18-FDG PET Imaging of Anal Cancer Patients

Purpose: PET imaging with F18-FDG is utilized for treatment planning, treatment assessment, and prognosis. A region of interest (ROI) encompassing the tumor may be determined on the PET image, often by a threshold T on the PET standard uptake values (SUVs). Several studies have shown prognostic value for relevant ROI properties including maximum SUV value (SUVmax), metabolic tumor volume (MTV), and total glycolytic activity (TGA). The choice of threshold T may affect mean SUV value (SUVmean), MTV, and TGA. Recently spatial resolution modeling (SRM) has been introduced on many PET systems. SRM may also affect these ROI properties. The purpose of this work is to investigate the relative influence of SRM and threshold choice T on SUVmean, MTV, TGA, and SUVmax. Methods: For 9 anal cancer patients, 18F-FDG PET scans were performed prior to treatment. PET images were reconstructed by 2 iterations of Ordered Subsets Expectation Maximization (OSEM), with and without SRM. ROI contours were generated by 5 different SUV threshold values T: 2.5, 3.0, 30%, 40%, and 50% of SUVmax. Paired-samples t tests were used to compare SUVmean, MTV, and TGA (a) for SRM on versus off and (b) between each pair of threshold values T. SUVmax was also compared for SRM on versus off. Results: For almost all (57/60) comparisons of 2 different threshold values, SUVmean, MTV, and TGA showed statistically significant variation. For comparison of SRM on versus off, there were no statistically significant changes in SUVmax and TGA, but there were statistically significant changes in MTV for T=2.5 and T=3.0 and in SUVmean for all T. Conclusion: The near-universal statistical significance of threshold choice T suggests that, regarding harmonization across sites, threshold choice may be a greater concern than choice of SRM. However, broader study is warranted, e.g. other iterations of OSEM should be considered.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-T-320: The Effect of Survivin Perturbation On the Radiation Response of Breast Cancer Cell Lines

Purpose: Survivin is the smallest member of the inhibitor of apoptosis protein family and is well-known for its universal over-expression in human cancers. Due to its role in apoptosis and cellular proliferation, survivin is implicated in the radiation response in several cancer types, and antisurvivin treatments have had success as a radiation sensitizer in many preclinical cancer models. As no studies to date have reported survivin as a factor affecting radiation resistance in breast cancer models, we sought to evaluate the synergistic relationship between survivin function and irradiation in breast cancer cell lines. Methods: Information regarding survivin protein expression in breast cancer was retrieved from three public databases: Oncomine, Kaplan-Meier Plotter, and GOBO. For the in vitro studies, survivin function was compromised by transducing a non-functional mutant form (survivin-DN) into two breast cancer cell lines, the estrogen receptor-positive MCF7 and the triple-negative, inflammatory SUM149. Cell growth was compared in the survivin-DN and control populations with colony-formation assays. To assess how survivin affects radiation response, clonogenic assays were performed by irradiating the cell lines up to 6 Gy. Results: From the public databases, survivin is more highly expressed in triple-negative breast cancer compared to all other subtypes, and is prognostic of poor survival in all breast cancer patients. In MCF7, the survivin-DN population had decreased colony-formation potential; the opposite was true in SUM149. In the clonogenic assays, abrogation of survivin function radio-protected MCF7 cells in monolayer and 3D growth conditions, while SUM149 survivin-DN cells were radiosensitized in monolayer conditions. Conclusion: We observed synergy between survivin function and radiation, although the results between the two cell lines were disparate. Further investigation is required to identify the mechanism of this discrepancy, including evaluation of the potential role of estrogen receptor status, before proceeding with small-animal experiments.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-QI-16: Reproducibility of Computed Tomography Quantitative Structural Features Using the FDA Thoracic Phantom Image Database

Purpose: To assess the reproducibility of quantitative structural features using images from the computed tomography thoracic FDA phantom database under different scanning conditions. Methods: Development of quantitative image features to describe lesion shape and size, beyond conventional RECIST measures, is an evolving area of research in need of benchmarking standards. Gavrielides et al. (2010) scanned a FDA-developed thoracic phantom with nodules of various Hounsfield units (HU) values, shapes and sizes close to vascular structures using several scanners and varying scanning conditions/parameters; these images are in the public domain. We tested six structural features, namely, Convexity, Perimeter, Major Axis, Minor Axis, Extent Mean and Eccentricity, to characterize lung nodules. Convexity measures lesion irregularity referenced to a convex surface. Previously, we showed it to have prognostic value in lung adenocarcinoma. The above metrics and RECIST measures were evaluated on three spiculated (8mm/-300HU, 12mm/+30HU and 15mm/+30HU) and two non-spiculated (8mm/+100HU and 10mm/+100HU) nodules (from layout 2) imaged at three different mAs values: 25, 100 and 200 mAs; on a Phillips scanner (16-slice Mx8000-IDT; 3mm slice thickness). The nodules were segmented semi-automatically using a commercial software tool; the same HU range was used for all nodules. Results: Analysis showed convexity having the lowest maximum coefficient of variation (MCV): 1.1% and 0.6% for spiculated and non-spiculated nodules, respectively, much lower compared to RECIST Major and Minor axes whose MCV were 10.1% and 13.4% for spiculated, and 1.9% and 2.3% for non-spiculated nodules, respectively, across the various mAs. MCVs were consistently larger for speculated nodules. In general, the dependence of structural features on mAs (noise) was low. Conclusion: The FDA phantom CT database may be used for benchmarking of structural features for various scanners and scanning conditions; we used only a small fraction of available data. Our feature convexity outperformed other structural features including RECIST measures.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-QI-17: Dependence of 3D/4D PET Quantitative Image Features On Noise

Purpose: Quantitative imaging is a fast evolving discipline where a large number of features are extracted from images; i.e., radiomics. Some features have been shown to have diagnostic, prognostic and predictive value. However, they are sensitive to acquisition and processing factors; e.g., noise. In this study noise was added to positron emission tomography (PET) images to determine how features were affected by noise. Methods: Three levels of Gaussian noise were added to 8 lung cancer patients PET images acquired in 3D mode (static) and using respiratory tracking (4D); for the latter images from one of 10 phases were used. A total of 62 features: 14 shape, 19 intensity (1stO), 18 GLCM textures (2ndO; from grey level co-occurrence matrices) and 11 RLM textures (2ndO; from run-length matrices) features were extracted from segmented tumors. Dimensions of GLCM were 256×256, calculated using 3D images with a step size of 1 voxel in 13 directions. Grey levels were binned into 256 levels for RLM and features were calculated in all 13 directions. Results: Feature variation generally increased with noise. Shape features were the most stable while RLM were the most unstable. Intensity and GLCM features performed well; the latter being more robust. The most stable 1stO features were compactness, maximum and minimum length, standard deviation, root-mean-squared, I30, V10-V90, and entropy. The most stable 2ndO features were entropy, sum-average, sum-entropy, difference-average, difference-variance, difference-entropy, information-correlation-2, short-run-emphasis, long-run-emphasis, and run-percentage. In general, features computed from images from one of the phases of 4D scans were more stable than from 3D scans. Conclusion: This study shows the need to characterize image features carefully before they are used in research and medical applications. It also shows that the performance of features, and thereby feature selection, may be assessed in part by noise analysis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TU-F-12A-05: Sensitivity of Textural Features to 3D Vs. 4D FDG-PET/CT Imaging in NSCLC Patients

Purpose: Neighborhood Gray-level difference matrices (NGLDM) based texture parameters extracted from conventional (3D) 18F-FDG PET scans in patients with NSCLC have been previously shown to associate with response to chemoradiation and poorer patient outcome. However, the change in these parameters when utilizing respiratory-correlated (4D) FDG-PET scans has not yet been characterized for NSCLC. The Objectives: of this study was to assess the extent to which NGLDM-based texture parameters on 4D PET images vary with reference to values derived from 3D scans in NSCLC. Methods: Eight patients with newly diagnosed NSCLC treated with concomitant chemoradiotherapy were included in this study. 4D PET scans were reconstructed with OSEM-IR in 5 respiratory phase-binned images and corresponding CT data of each phase were employed for attenuation correction. NGLDM-based texture features, consisting of coarseness, contrast, busyness, complexity and strength, were evaluated for gross tumor volumes defined on 3D/4D PET scans by radiation oncologists. Variation of the obtained texture parameters over the respiratory cycle were examined with respect to values extracted from 3D scans. Results: Differences between texture parameters derived from 4D scans at different respiratory phases and those extracted from 3D scans ranged from −30% to 13% for coarseness, −12% to 40% for contrast, −5% to 50% for busyness, −7% to 38% for complexity, and −43% to 20% for strength. Furthermore, no evident correlations were observed between respiratory phase and 4D scan texture parameters. Conclusion: Results of the current study showed that NGLDM-based texture parameters varied considerably based on choice of 3D PET and 4D PET reconstruction of NSCLC patient images, indicating that standardized image acquisition and analysis protocols need to be established for clinical studies, especially multicenter clinical trials, intending to validate prognostic values of texture features for NSCLC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

WE-E-17A-03: FDG-PET-Based Radiomics to Predict Local Control and Survival Following Radiotherapy

Purpose: An exploding field in cancer research is “radiomics,” based on the hypothesis that there is statistical (hidden) information in medical images that is prognostic or predictive of outcomes. Our group has developed an efficient pipeline to extract and analyze quantitative image features from medical images as related to outcomes or diagnosis. In this work, we summarize our previous studies with positron emission tomography (PET) images and show the potential of the use of radiomics for outcomes research. Methods: We analyzed two cancer datasets, each consisting of pre-radiotherapy-treatment PET scans: 163 T1-2N0M0 non-small cell lung cancer (NSCLC) patients and 174 head and neck (H and N) cancer patients with stage III–IV. The PET scans were converted to Computational Environment for Radiological Research (CERR) format, and CERR was used to generate 24 shape, texture, and intensity-histogram based image features. Data-mining and logistic regression methods were then used to model local failure (LF) and overall survival (OS). Unbiased estimates of performance were generated using leave-one-out cross-validation (LOOCV). Results: For predicting LF, the models with biologically equivalent dose (BED) and TLG (metabolic tumor volume (MTV) x SUVmean) in NSCLC, and skewness and MTV in H and N, achieved the best performance with AUC=0.818 (p<0.0001) and AUC=0.826 (p=0.0002), respectively. For predicting OS, the models with kurtosis and volume in NSCLC and SUVmax and homogeneity in H and N achieved the best performance with AUC=0.706 (p<0.0001) and AUC=0.656 (p=0.0003), respectively. On LOOCV, all these models retained significant predictive power. Interestingly, MTV was highly correlated with LF in both sites. Conclusion: PET-based imaged features are promising tools for improving treatment management decision making. Much more research is needed to identify optimal radiomics metrics and to correlate imaging phenotype with other clinical or genomic information.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

WE-G-BRD-06: Variation in Dynamic Positron Emission Tomography Imaging of Tumor Hypoxia in Early Stage Non-Small Cell Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy

Purpose: Tumor hypoxia is correlated with treatment failure. To date, there are no published studies investigating hypoxia in non-small cell lung cancer (NSCLC) patients undergoing SBRT. We aim to use 18F-fluoromisonidazole (18F-FMISO) positron emission tomography (PET) imaging to non-invasively quantify the tumor hypoxic volume (HV), to elucidate potential roles of reoxygenation and tumor vascular response at high doses, and to identify an optimal prognostic imaging time-point. Methods: SBRT-eligible patients with NSCLC tumors >1cm were prospectively enrolled in an IRB-approved study. Computed Tomography and dynamic PET images (0–120min, 150–180min, and 210–240min post-injection) were acquired using a Siemens BiographmCT PET/CT scanner. 18F-FMISO PET was performed on a single patient at 3 different time points around a single SBRT delivery of 18 Gy and HVs were compared using a tumor-to-blood ratio (TBR)>1.2 and rate of influx (Ki)>0.0015 (Patlak). Results: Results from our first patient showed substantial temporal changes in HV following SBRT. Using a TBR threshold >1.2 and summed images 210–240min, the HVs were 19%, 31% and 13% of total tumor volume on day 0, 2 (48 hours post-SBRT), and 4 (96 hours post-SBRT). The absolute volume of hypoxia increased by nearly a factor of 2 after 18 Gy and then decreased almost to baseline 96 hours later. Selected imaging timepoints resulted in temporal changes in HV quantification obtained with TBR. Ki, calculated using 4-hour dynamic data, evaluated HVs as 22%, 75% and 21%, respectively. Conclusions: ith the results of only one patient, this novel pilot study highlights the potential benefit of 18F-FMISO PET imaging as results indicate substantial temporal changes in tumor HV post-SBRT. Analysis suggests that TBR is not a robust parameter for accurate HV quantification and heavily influenced by imaging timepoint selection. Kinetic modeling parameters are more sensitive and may aid in future treatment individualization based on patient-specific biological information.

60 APPLIED LIFE SCIENCES↗