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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

SU-E-J-06: Additional Imaging Guidance Dose to Patient Organs Resulting From X-Ray Tubes Used in CyberKnife Image Guidance System

Purpose: The use of image-guided radiation therapy (IGRT) has become increasingly common, but the additional radiation exposure resulting from repeated image guidance procedures raises concerns. Although there are many studies reporting imaging dose from different image guidance devices, imaging dose for the CyberKnife Robotic Radiosurgery System is not available. This study provides estimated organ doses resulting from image guidance procedures on the CyberKnife system. Methods: Commercially available Monte Carlo software, PCXMC, was used to calculate average organ doses resulting from x-ray tubes used in the CyberKnife system. There are seven imaging protocols with kVp ranging from 60 – 120 kV and 15 mAs for treatment sites in the Cranium, Head and Neck, Thorax, and Abdomen. The output of each image protocol was measured at treatment isocenter. For each site and protocol, Adult body sizes ranging from anorexic to extremely obese were simulated since organ dose depends on patient size. Doses for all organs within the imaging field-of-view of each site were calculated for a single image acquisition from both of the orthogonal x-ray tubes. Results: Average organ doses were <1.0 mGy for every treatment site and imaging protocol. For a given organ, dose increases as kV increases or body size decreases. Higher doses are typically reported for skeletal components, such as the skull, ribs, or clavicles, than for softtissue organs. Typical organ doses due to a single exposure are estimated as 0.23 mGy to the brain, 0.29 mGy to the heart, 0.08 mGy to the kidneys, etc., depending on the imaging protocol and site. Conclusion: The organ doses vary with treatment site, imaging protocol and patient size. Although the organ dose from a single image acquisition resulting from two orthogonal beams is generally insignificant, the sum of repeated image acquisitions (>100) could reach 10–20 cGy for a typical treatment fraction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-AB-209-10: Breast Cancer Identification Through X-Ray Coherent Scatter Spectral Imaging

Purpose: We have previously described the development and testing of a coherent-scatter spectral imaging system for identification of cancer. Our prior evaluations were performed using either tissue surrogate phantoms or formalin-fixed tissue obtained from pathology. Here we present the first results from a scatter imaging study using fresh breast tumor tissues obtained through surgical excision. Methods: A coherent-scatter imaging system was built using a clinical X-ray tube, photon counting detectors, and custom-designed coded-apertures. System performance was characterized using calibration phantoms of biological materials. Fresh breast tumors were obtained from patients undergoing mastectomy and lumpectomy surgeries for breast cancer. Each specimen was vacuum-sealed, scanned using the scatter imaging system, and then sent to pathology for histological workup. Scatter images were generated separately for each tissue specimen and analyzed to identify voxels containing malignant tissue. The images were compared against histological analysis (H&E + pathologist identification of tumors) to assess the match between scatter-based and histological diagnosis. Results: In all specimens scanned, the scatter images showed the location of cancerous regions within the specimen. The detection and classification was performed through automated spectral matching without the need for manual intervention. The scatter spectra corresponding to cancer tissue were found to be in agreement with those reported in literature. Inter-patient variability was found to be within limits reported in literature. The scatter images showed agreement with pathologist-identified regions of cancer. Spatial resolution for this configuration of the scanner was determined to be 2–3 mm, and the total scan time for each specimen was under 15 minutes. Conclusion: This work demonstrates the utility of coherent scatter imaging in identifying cancer based on the scatter properties of the tissue. It presents the first results from coherent scatter imaging of fresh (unfixed) breast tissue using our coded-aperture scatter imaging approach for cancer identification.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

PIV Uncertainty Methodologies for CFD Code Validation at the MIR Facility

Currently, computational fluid dynamics (CFD) is widely used in the nuclear thermal hydraulics field for design and safety analyses. To validate CFD codes, high quality multi dimensional flow field data are essential. The Matched Index of Refraction (MIR) Flow Facility at Idaho National Laboratory has a unique capability to contribute to the development of validated CFD codes through the use of Particle Image Velocimetry (PIV). The significance of the MIR facility is that it permits non intrusive velocity measurement techniques, such as PIV, through complex models without requiring probes and other instrumentation that disturb the flow. At the heart of any PIV calculation is the cross-correlation, which is used to estimate the displacement of particles in some small part of the image over the time span between two images. This image displacement is indicated by the location of the largest peak. In the MIR facility, uncertainty quantification is a challenging task due to the use of optical measurement techniques. Currently, this study is developing a reliable method to analyze uncertainty and sensitivity of the measured data and develop a computer code to automatically analyze the uncertainty/sensitivity of the measured data. The main objective of this study is to develop a well established uncertainty quantification method for the MIR Flow Facility, which consists of many complicated uncertainty factors. In this study, the uncertainty sources are resolved in depth by categorizing them into uncertainties from the MIR flow loop and PIV system (including particle motion, image distortion, and data processing). Then, each uncertainty source is mathematically modeled or adequately defined. Finally, this study will provide a method and procedure to quantify the experimental uncertainty in the MIR Flow Facility with sample test results.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

WE-G-204-07: Automated Characterization of Perceptual Quality of Clinical Chest Radiographs: Improvements in Lung, Spine, and Hardware Detection

Purpose: To develop and validate more robust methods for automated lung, spine, and hardware detection in AP/PA chest images. This work is part of a continuing effort to automatically characterize the perceptual image quality of clinical radiographs. [Y. Lin et al. Med. Phys. 39, 7019–7031 (2012)] Methods: Our previous implementation of lung/spine identification was applicable to only one vendor. A more generalized routine was devised based on three primary components: lung boundary detection, fuzzy c-means (FCM) clustering, and a clinically-derived lung pixel probability map. Boundary detection was used to constrain the lung segmentations. FCM clustering produced grayscale- and neighborhood-based pixel classification probabilities which are weighted by the clinically-derived probability maps to generate a final lung segmentation. Lung centerlines were set along the left-right lung midpoints. Spine centerlines were estimated as a weighted average of body contour, lateral lung contour, and intensity-based centerline estimates. Centerline estimation was tested on 900 clinical AP/PA chest radiographs which included inpatient/outpatient, upright/bedside, men/women, and adult/pediatric images from multiple imaging systems. Our previous implementation further did not account for the presence of medical hardware (pacemakers, wires, implants, staples, stents, etc.) potentially biasing image quality analysis. A hardware detection algorithm was developed using a gradient-based thresholding method. The training and testing paradigm used a set of 48 images from which 1920 51×51 pixel{sup 2} ROIs with and 1920 ROIs without hardware were manually selected. Results: Acceptable lung centerlines were generated in 98.7% of radiographs while spine centerlines were acceptable in 99.1% of radiographs. Following threshold optimization, the hardware detection software yielded average true positive and true negative rates of 92.7% and 96.9%, respectively. Conclusion: Updated segmentation and centerline estimation methods in addition to new gradient-based hardware detection software provide improved data integrity control and error-checking for automated clinical chest image quality characterization across multiple radiography systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Learning the right channel in multimodal imaging: automated experiment in piezoresponse force microscopy

We report the development and experimental implementation of the automated experiment workflows for the identification of the best predictive channel for a phenomenon of interest in spectroscopic measurements. The approach is based on the combination of ensembled deep kernel learning for probabilistic predictions and a basic reinforcement learning policy for channel selection. It allows the identification of which of the available observational channels, sampled sequentially, are most predictive of selected behaviors, and hence have the strongest correlations. We implement this approach for multimodal imaging in piezoresponse force microscopy (PFM), with the behaviors of interest manifesting in piezoresponse spectroscopy. We illustrate the best predictive channel for polarization-voltage hysteresis loop and frequency-voltage hysteresis loop areas is amplitude in the model samples. The same workflow and code are applicable for any multimodal imaging and local characterization methods.

36 MATERIALS SCIENCE↗
DOE OSTI2015

SU-E-J-115: Correlation of Displacement Vector Fields Calculated by Deformable Image Registration Algorithms with Motion Parameters of CT Images with Well-Defined Targets and Controlled-Motion

Purpose: To investigate correlation of displacement vector fields (DVF) calculated by deformable image registration algorithms with motion parameters in helical axial and cone-beam CT images with motion artifacts. Methods: A mobile thorax phantom with well-known targets with different sizes that were made from water-equivalent material and inserted in foam to simulate lung lesions. The thorax phantom was imaged with helical, axial and cone-beam CT. The phantom was moved with a cyclic motion with different motion amplitudes and frequencies along the superior-inferior direction. Different deformable image registration algorithms including demons, fast demons, Horn-Shunck and iterative-optical-flow from the DIRART software were used to deform CT images for the phantom with different motion patterns. The CT images of the mobile phantom were deformed to CT images of the stationary phantom. Results: The values of displacement vectors calculated by deformable image registration algorithm correlated strongly with motion amplitude where large displacement vectors were calculated for CT images with large motion amplitudes. For example, the maximal displacement vectors were nearly equal to the motion amplitudes (5mm, 10mm or 20mm) at interfaces between the mobile targets lung tissue, while the minimal displacement vectors were nearly equal to negative the motion amplitudes. The maximal and minimal displacement vectors matched with edges of the blurred targets along the Z-axis (motion-direction), while DVF’s were small in the other directions. This indicates that the blurred edges by phantom motion were shifted largely to match with the actual target edge. These shifts were nearly equal to the motion amplitude. Conclusions: The DVF from deformable-image registration algorithms correlated well with motion amplitude of well-defined mobile targets. This can be used to extract motion parameters such as amplitude. However, as motion amplitudes increased, image artifacts increased significantly and that limited image quality and poor correlation between the motion amplitude and DVF was obtained.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-02: Comparative Analysis and Constancy Check of Image Quality Parameters for Three Linear Accelerators Per TG 142 Protocol

Purpose: To compare image quality parameters and assessing the image stability of three different linear accelerators (linac) for 2D and 3D imaging modalities: planar kV, MV images and cone-beam CT (CBCT). Methods: QCkV1, QC-3 and Cathpan-600 phantoms were utilized to acquire kV, MV and CBCT images respectively on monthly basis per TG142 QA protocol for over 2 years on 21Ex, NovalisTx and TrueBeam linacs. DICOM images were analyzed with the help of QA analysis software: PIPsPro from Standard Imaging. For planar kV and MV images, planar spatial resolution, contrast to noise ratio (CNR) and noise; for CBCT, HU values were collected and analyzed. Results: Two years of monthly QA measurements were analyzed for the planar and CBCT images. Values were normalized to the mean and the standard deviations (STD) are presented. For the kV planar radiographic images the STD of spatial resolution for f30, f40, f50, CNR and noise for 21Ex are 0.006, 0.011, 0.013, 0.046, 0.026; Novalis-Tx are 0.009, 0.016, 0.016, 0.067, 0.053 ; TrueBeam are 0.007, 0.005, 0.009, 0.017, 0.016 respectively. For the MV planar radiographic images, the STD of spatial resolution for f30, f40, f50, CNR and noise for 21Ex are 0.009, 0.010, 0.008, 0.023, 0.023; for Novalix-Tx are 0.012, 0.010, 0.008, 0.029, 0.023 and for TrueBeam are 0.010, 0.010, 0.007, 0.022, 0.022 respectively. For the CBCT images, HU constancies of Air, Polystyrene, Teflon, PMP, LDPE and Delrin for 21Ex are 0.014, 0.070, 0.031, 0.053, 0.076, 0.087; for Novalis Tx are 0.019, 0.047, 0.035, 0.059, 0.077, 0.087 and for TrueBeam are 0.011, 0.044, 0.025, 0.044, 0.056, 0.020 respectively. Conclusion: These Imaging QA results demonstrated that the TrueBeam, performed better in terms of image quality stability for both kV planer and CBCT images as well as EPID MV images, however other two linacs were also satisfied TG142 guidelines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-AB-209-12: Tissue Equivalent Phantom with Excised Human Tissue for Assessing Clinical Capabilities of Coherent Scatter Imaging Applications

Purpose: Previously we reported the development of anthropomorphic tissue-equivalent scatter phantoms of the human breast. Here we present the first results from the scatter imaging of the tissue equivalent breast phantoms for breast cancer diagnosis. Methods: A breast phantom was designed to assess the capability of coded aperture coherent x-ray scatter imaging to classify different types of breast tissue (adipose, fibroglandular, tumor). The phantom geometry was obtained from a prone breast geometry scanned on a dedicated breast CT system. The phantom was 3D printed using the segmented DICOM breast CT data. The 3D breast phantom was filled with lard (as a surrogate for adipose tissue) and scanned in different geometries alongside excised human breast tissues (obtained from lumpectomy and mastectomy procedures). The raw data were reconstructed using a model-based reconstruction algorithm and yielded the location and form factor (i.e., momentum transfer (q) spectrum) of the materials that were imaged. The measured material form factors were then compared to the ground truth measurements acquired by x-ray diffraction (XRD) imaging. Results: Our scatter imaging system was able to define the location and composition of the various materials and tissues within the phantom. Cancerous breast tissue was detected and classified through automated spectral matching and an 86% correlation threshold. The total scan time for the sample was approximately 10 minutes and approaches workflow times for clinical use in intra-operative or other diagnostic tasks. Conclusion: This work demonstrates the first results from an anthropomorphic tissue equivalent scatter phantom to characterize a coherent scatter imaging system. The functionality of the system shows promise in applications such as intra-operative margin detection or virtual biopsy in the diagnosis of breast cancer. Future work includes using additional patient-derived tissues (e.g., human fat), and modeling additional organs (e.g., lung).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TU-A-9A-03: Development and Verification of a Forward Model That Assists in Iterative Post-Processing Algorithms Used to Reduce Blur in Compton Backscatter Imaging Systems

Purpose: In Compton Backscatter Imaging (CBI), the source and detector reside on the same side of the patient. We previously demonstrated the applicability of CBI systems for medical purposes using an industrial system. To assist in post-processing images from a CBI system, a forward model based on radiation absorption and scatter principles has been developed. Methods: The forward model was developed in C++ using raytracing to track particles. The algorithm accepts phantoms of any size and resolution to calculate the fraction of incident photons scattered back to the detector, and can perform these calculations for any detector geometry and source specification. To validate the model, results were compared to MCNP-X, which is a Monte Carlo based simulation software, for various combinations of source specifications, detector geometries, and phantom compositions. Results: The model verified that the backscatter signal to the detector was based on three interaction probabilities: a) attenuation of photons going into the phantom, b) Compton scatter of photons toward the detector, and c) attenuation of photons coming out of the phantom. The results from the MCNP-X simulations and the forward model varied from 1 to 5%. This difference was less than 1% for energies higher than 30 keV, but was up to 4% for lower energies. At 50 keV, the difference was less than 1% for multiple detector widths and for both homogeneous and heterogeneous phantoms. Conclusion: As part of the optimization of a medical CBI system, an efficient and accurate forward model was constructed in C++ to estimate the output of CBI system. The model characterized individual components contributing to CBI output and increased computational efficiency over Monte Carlo simulations. It is now used in the development of novel post-processing algorithms that reduce image blur by reversing undesired contribution from outside the region of interest.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

SU-E-J-52: Decreasing Frequency of Performing TG-142 Imaging QA – 5 Year Experience

Purpose This study is an update to check if the frequency of imaging QA suggested by AAPM Task Group Report 142 (TG142) is necessary with our 5 year experience. TG142 presents recommendations for QA criteria of IGRT treatment. ACR has adopted it to be the requirements for any radiatiotherapy practices; however, we propose to reduce the frequency on image quality QA according to this 5 year study.Method and Materials: This study uses VarianIX2100 and Siemens Artiste Linacs to perform QAs on KV, MV, CBCT modalities. The QA was designed following under the recommendations of TG142. This study reports the daily imaging positioning/repositioning and imaging and treatment coordinate coincidence. QA results on kV, MV and CBCT from 4/7/2010∼3/11/15 are analyzed. KV, MV, CBCT images are taken with the Varian isocube localized at the isocenter. Digital graticule is used in the software to verify the isocenter position. CBCT images are taken with the cube placed at 1cm superior, lateral and anterior of the isocenter. In-line fusion software is used to verify the contrived shift. Digital ruler provided at the on-board-imaging software or adaptive-targeting software was used to measure the position differences. The position differences were recorded at AP,LR,SI directions. Results 5 year records on kV, MV, CBCT show the shifts in all three directions are within the tolerance of 1mm suggested in TG142 for stereotactic radiation treatment(SRS/SRT). There is no occasion where shifts are outside 1mm tolerance. Conclusions The daily imaging QA suggested in TG142 is useful in ensuring the accuracy needed for SRS/SRT in IGRT. 5 year measurements presented suggest that decreasing the frequency of imaging QA may be acceptable, in particular for institutions reporting no violation of tolerance over periods of few years.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Feature-space assessment of electrical impedance tomography coregistered with computed tomography in detecting multiple contrast targets

Purpose: Fusion of electrical impedance tomography (EIT) with computed tomography (CT) can be useful as a clinical tool for providing additional physiological information about tissues, but requires suitable fusion algorithms and validation procedures. This work explores the feasibility of fusing EIT and CT images using an algorithm for coregistration. The imaging performance is validated through feature space assessment on phantom contrast targets. Methods: EIT data were acquired by scanning a phantom using a circuit, configured for injecting current through 16 electrodes, placed around the phantom. A conductivity image of the phantom was obtained from the data using electrical impedance and diffuse optical tomography reconstruction software (EIDORS). A CT image of the phantom was also acquired. The EIT and CT images were fused using a region of interest (ROI) coregistration fusion algorithm. Phantom imaging experiments were carried out on objects of different contrasts, sizes, and positions. The conductive medium of the phantoms was made of a tissue-mimicking bolus material that is routinely used in clinical radiation therapy settings. To validate the imaging performance in detecting different contrasts, the ROI of the phantom was filled with distilled water and normal saline. Spatially separated cylindrical objects of different sizes were used for validating the imaging performance in multiple target detection. Analyses of the CT, EIT and the EIT/CT phantom images were carried out based on the variations of contrast, correlation, energy, and homogeneity, using a gray level co-occurrence matrix (GLCM). A reference image of the phantom was simulated using EIDORS, and the performances of the CT and EIT imaging systems were evaluated and compared against the performance of the EIT/CT system using various feature metrics, detectability, and structural similarity index measures. Results: In detecting distilled and normal saline water in bolus medium, EIT as a stand-alone imaging system showed contrast discrimination of 47%, while the CT imaging system showed a discrimination of only 1.5%. The structural similarity index measure showed a drop of 24% with EIT imaging compared to CT imaging. The average detectability measure for CT imaging was found to be 2.375 ± 0.19 before fusion. After complementing with EIT information, the detectability measure increased to 11.06 ± 2.04. Based on the feature metrics, the functional imaging quality of CT and EIT were found to be 2.29% and 86%, respectively, before fusion. Structural imaging quality was found to be 66% for CT and 16% for EIT. After fusion, functional imaging quality improved in CT imaging from 2.29% to 42% and the structural imaging quality of EIT imaging changed from 16% to 66%. The improvement in image quality was also observed in detecting objects of different sizes. Conclusions: The authors found a significant improvement in the contrast detectability performance of CT imaging when complemented with functional imaging information from EIT. Along with the feature assessment metrics, the concept of complementing CT with EIT imaging can lead to an EIT/CT imaging modality which might fully utilize the functional imaging abilities of EIT imaging, thereby enhancing the quality of care in the areas of cancer diagnosis and radiotherapy treatment planning.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

TU-F-CAMPUS-J-03: Elasticity Functions Based On 4DCT Images to Predict Tumor and Normal Tissue Response to Radiation for Patients with Lung Cancers

Purpose: To investigate radiotherapy outcomes by incorporating 4DCT-based physiological and tumor elasticity functions for lung cancer patients. Methods: 4DCT images were acquired from 28 lung SBRT patients before radiation treatment. Deformable image registration (DIR) was performed from the end-inhale to the end-exhale using a B-Spline-based algorithm (Elastix, an open source software package). The resultant displacement vector fields (DVFs) were used to calculate a relative Jacobian function (RV) for each patient. The computed functions in the lung and tumor regions represent lung ventilation and tumor elasticity properties, respectively. The 28 patients were divided into two groups: 16 with two-year tumor local control (LC) and 12 with local failure (LF). The ventilation and elasticity related RV functions were calculated for each of these patients. Results: The LF patients have larger RV values than the LC patients. The mean RV value in the lung region was 1.15 (±0.67) for the LF patients, higher than 1.06 (±0.59) for the LC patients. In the tumor region, the elasticity-related RV values are 1.2 (±0.97) and 0.86 (±0.64) for the LF and LC patients, respectively. Among the 16 LC patients, 3 have the mean RV values greater than 1.0 in the tumors. These tumors were located near the diaphragm, where the displacements are relatively large.. RV functions calculated in the tumor were better correlated with treatment outcomes than those calculated in the lung. Conclusion: The ventilation and elasticity-related RV functions in the lung and tumor regions were calculated from 4DCT image and the resultant values showed differences between the LC and LF patients. Further investigation of the impact of the displacements on the computed RV is warranted. Results suggest that the RV images might be useful for evaluation of treatment outcome for lung cancer patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Applications of LIF to Document Natural Variability of Chlorophyll Content and Cu Uptake in Moss

Chlorophyll has long been used as a natural indicator of plant health and photosynthetic efficiency. Laser-induced fluorescence (LIF) is an emerging technique for understanding broad spectrum organic processes and has more recently been used to monitor chlorophyll response in plants. Previous work has focused on developing a LIF technique for imaging moss mats to identify metal contamination with the current focus shifting toward application to moss fronds and aiding sample collection for chemical analysis. Two laser systems (CoCoBi a Nd:YGa pulsed laser system and Chl-SL with two blue continuous semiconductor diodes) were used to collect images of moss fronds exposed to increasing levels of Cu (1, 10, and 100 nmol/cm 2 ) using a CMOS camera. The best methods for the preprocessing of images were conducted before the analysis of fluorescence signatures were compared to a control. The Chl-SL system performed better than the CoCoBi, with dynamic time warping (DTW) proving the most effective for image analysis. Manual thresholding to remove lower decimal code values improved the data distributions and proved whether using one or two fronds in an image was more advantageous. A higher DTW difference from the control correlated to lower chlorophyll a/b ratios and a higher metal content, indicating that LIF, with the aid of image processing, can be an effective technique for identifying Cu contamination shortly after an event.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

A Generative Model for Realistic Galaxy Cluster X-Ray Morphologies

Abstract The X-ray morphologies of clusters of galaxies display significant variations, reflecting their dynamical histories and the nonlinear dependence of X-ray emissivity on the density of the intracluster gas. Qualitative and quantitative assessments of X-ray morphology have long been considered a proxy for determining whether clusters are dynamically active or “relaxed.” Conversely, the use of circularly or elliptically symmetric models for cluster emission can be complicated by the variety of complex features realized in nature, spanning scales from megaparsecs down to the resolution limit of current X-ray observatories. In this work, we use mock X-ray images from simulated clusters from The Three Hundred project to define a basis set of cluster image features. We take advantage of the clusters’ approximate self-similarity to minimize the differences between images before encoding the remaining diversity through a distribution of high-order polynomial coefficients. Principal component analysis then provides an orthogonal basis for this distribution, corresponding to natural perturbations from an average model. This representation allows novel, realistically complex X-ray cluster images to be easily generated, and we provide code to do so. The approach provides a simple way to generate training data for cluster image analysis algorithms and could be straightforwardly adapted to generate clusters displaying specific types of features or selected by physical characteristics available in the original simulations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Automated bubble analysis of high-speed subcooled flow boiling images using U-net transfer learning and global optical flow

Capturing and analyzing the bubble dynamics is crucial to improving the understanding of boiling heat transfer mechanisms and predicting boiling heat transfer coefficient and boiling crisis. High speed video (HSV) imaging has been used for decades towards this end. Still, there is no universal approach to quantitatively analyze bubble dynamics from HSV images. In this study, we propose a data-driven post-processing approach to segment, track, and identify wall-attached vapor bubbles from HSV images of the boiling process in subcooled flow conditions. Firstly, we employ a transfer learning framework with a U-Net-based convolution neural network (CNN) architecture to detect and segment bubbles in HSV images of diverse contrast and surface texture using very little data (e.g., 10 images) for training. Then, we evaluate the trained CNN model with 100 ground-truth images, and the validation results show that the model accuracy and precision in detecting the optical footprint of bubbles are higher than 90%. Finally, we suggest a criterion to identify a condensing bubble based on the divergence of the bubble displacement, which is calculated from sequential segmented bubble images using a global optical flow code. Using this combination of machine learning and optical flow, we can identify nucleation sites and track the growth of bubbles nucleating at each site to quantify nucleation site density, nucleation frequency, and other fundamental boiling parameters. The proposed system is validated using results obtained on a special heater, which enables both infrared (IR) thermometry and HSV imaging on a metallic surface. We compare the fundamental boiling parameters obtained by the two different diagnostics. The results show good agreement. In conclusion, the difference between the measurements of nucleation site density, averaged nucleation frequency, and averaged growth time performed with the two techniques is always within ± 20% and mostly ± 10% of the values measured with IR thermometry.

42 ENGINEERING↗
DOE OSTI2025

A Standardized Analysis Process Using Digital Image Correlation to Calculate In Situ Cladding Strain from Modified Burst Tests for Fuel Performance Code Validation

Historical data collection on nuclear fuel cladding materials has focused on generating a statistically significant amount of data to assess the material and its failure behavior. Furthermore, data generated to support material model and failure criteria development were previously posttest evaluations, so a large number of tests was required to gain new understanding. A way to expedite this process is to develop techniques capable of generating large, high-fidelity data sets from a single test with lower uncertainty or quantified uncertainty. One such example of this approach is Oak Ridge National Laboratory’s use of modified burst tests (MBTs) to analyze the mechanical behavior and failure conditions of cladding during a simulated reactivity-initiated accident (RIA). Each test incorporates digital image correlation (DIC) analysis techniques that are used to assess the accumulated strain in situ, as well as eventual cladding failure. This work has been fruitful in defining strain-to-failure conditions for materials like silicon carbide (SiC) fiber–reinforced/SiC matrix composite tubes (SiC/SiC), iron-chromium-aluminum (FeCrAl) alloy tubes, and chromium-coated Zircaloy-4 tubes. However, there are numerous DIC software available, including open-source and proprietary software. The different DIC software use various algorithms to process images and calculate displacement values. Using these different software and algorithms can lead to varying results, and perhaps larger-than-expected uncertainties. In the present study, previously published MBT data encompassing a variety of test conditions were reanalyzed with two different DIC software to assess the variance in the calculated strain results. The data consisted of SiC/SiC, FeCrAl, and chromium-coated Zircaloy-4 tubes. Plots of the calculated strains during the transient revealed good agreement between the two DIC software. The average root-mean-square errors between the two software was 0.20% strain, which is slightly larger than a previously reported error value for these tests. In conclusion, this variance in results is low enough that this analysis method can be used for code validation.

Reactivity-initiated accident↗
DOE OSTI2016

SU-F-J-188: Clinical Implementation of in Room Mobile CT for Image Guided Proton Therapy

Purpose: To implement soft-tissue image-guided proton therapy using inroom mobile CT. Methods: Anthropomorphic phantom was first used to determine the setup accuracy using in- room mobile CT. Laser and bbs were used for the initial setup (marked isocenter). CT data was then acquired with in-room mobile CT (daily CT). The shift between the marked isocenter and the planned isocenter (final isocenter) was determined from the daily CT using in-house Computer Assisted Targeting (CAT) software. Orthogonal DRRs of the day was also generated from the daily CT. The phantom was then transferred on the treatment couch top to the treatment machine using a transportation system, and again aligned to the marked isocenter. Couch shifts were made to align the phantom to the final isocenter using the shifts as determined using the CAT software, and verified using orthogonal X-ray images with the daily DRRs. Results: Phantom data suggests that following the setup procedure as described above, targeting accuracy could be within 1 mm. Patient data are being acquired and analyzed. Conclusion: In-room mobile CT is capable of providing soft-tissue image-guided proton therapy.

60 APPLIED LIFE SCIENCES↗