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At least 1,225 records · Page 68

SU-F-18C-06: Prospective Patient Evaluation of Iterative Reconstruction in Radiation Oncology

Purpose: This work incorporates iterative reconstruction (IR) into a dose reduction study to characterize image quality metrics, delineation, and dosimetric assessment, with the goal of reducing imaging dose in Radiation Oncology. Methods: Three-dimensional noise power spectrum (NPS) analysis characterized noise magnitude/texture (120 kVp, 50–200 mAs, IR levels 1–6 yielding noise reduction of 0.89–0.55 compared to filtered backprojection (FBP)). Task-specific Modulation Transfer Functions (MTFtask) were characterized across varied subject contrasts. A prospective dose reduction study (500 to 150 mAs) was conducted for 12 patients (43 inter-fraction CTs) for high-dose rate brachytherapy. Three physicians performed qualitative image assessment between full-dose FBP (FD-FBP, 500 mAs), low-dose FBP (LD-FBP, 150–250 mAs), and low-dose IRL5-6 (LD-IR) scans for image noise, cuff/bladder interface detectability, spatial resolution, texture, and segmentation confidence. Comparisons between LD-FBP and LD-IR were conducted for the following metrics: delineation (bladder and rectum evaluated via overlap indices (OI) and Dice similarity coefficients (DSC)), noise, boundary changes, dose calculation, and digitally reconstructed radiographs (DRRs). Results: NPS showed ∼50% reduction in noise magnitude and ∼0.1 1/mm spatial frequency shift with IRL6. The largest MTFtask decrease between FBP and IR was 0.08 A.U. Qualitative patient image evaluation revealed LD-IR was equivalent or slightly worse than FD-FBP, and superior to LD-FBP for all metrics except low contrast interface and texture. The largest CT number discrepancy from FBP occurred at a bone/tissue interface using IRL6 (−1.2 ± 4.9 HU (range: −17.6 – 12.5 HU)). No significant contour differences (OIs and DSCs = 0.85 – 0.95) and dose calculation discrepancy (<0.02%) were observed. DRRs preserved anatomical detail and demonstrated <2% difference in intensity between LD-FBP and LD-IRL6. Conclusion: While phantom analysis showed slight noise texture differences with IR, patient results revealed that image quality, contouring ability, and dosimetric parameters were not adversely affected, thus support integrating IR into treatment planning. Research supported in part by a grant from Philips HealthCare.

60 APPLIED LIFE SCIENCES↗

MO-AB-BRA-05: [18F]NaF PET/CT Imaging Biomarkers in Metastatic Prostate Cancer

Purpose: Clinical use of {sup 18}F-Sodium Fluoride (NaF) PET/CT in metastatic settings often lacks technology to quantitatively measure full disease dynamics due to high tumor burden. This study assesses radiomics-based extraction of NaF PET/CT measures, including global metrics of overall burden and local metrics of disease heterogeneity, in metastatic prostate cancer for correlation to clinical outcomes. Methods: Fifty-six metastatic Castrate-Resistant Prostate Cancer (mCRPC) patients had NaF PET/CT scans performed at baseline and three cycles into chemotherapy (N=16) or androgen-receptor (AR) inhibitors (N=39). A novel technology, Quantitative Total Bone Imaging (QTBI), was used for analysis. Employing hybrid PET/CT segmentation and articulated skeletal-registration, QTBI allows for response assessment of individual lesions. Various SUV metrics were extracted from each lesion (iSUV). Global metrics were extracted from composite lesion-level statistics for each patient (pSUV). Proportion of detected lesions and those with significant response (%-increase or %-decrease) was calculated for each patient based on test-retest limits for iSUV metrics. Cox proportional hazard regression analyses were conducted between imaging metrics and progression-free survival (PFS). Results: Functional burden (pSUV{sub total}) assessed mid-treatment was the strongest univariate predictor of PFS (HR=2.03; p<0.0001). Various global metrics outperformed baseline clinical markers, including fraction of skeletal burden, mean uptake (pSUV{sub mean}), and heterogeneity of average lesion uptake (pSUV{sub hetero}). Of 43 patients with paired baseline/mid-treatment imaging, 40 showed heterogeneity in lesion-level response, containing populations of lesions with both increasing/decreasing metrics. Proportion of lesions with significantly increasing iSUV{sub mean} was highly predictive of clinical PFS (HR=2.0; p=0.0002). Patients exhibiting higher proportion of lesions with decreasing iSUV{sub total} saw prolonged radiographic PFS (HR=0.51; p=0.02). Conclusion: Technology presented here provides comprehensive disease quantification on NaF PET/CT imaging, showing strong correlation to clinical outcomes. Total functional burden as well as proportions of similarly responding lesions was predictive of PFS. This supports ongoing development of NaF PET/CT based imaging biomarkers in mCRPC. Prostate Cancer Foundation.

60 APPLIED LIFE SCIENCES↗

Incremental growth of short SWNT arrays by pulsed chemical vapor deposition

Very short, aligned arrays of continuous single wall carbon nanotubes were grown incrementally in steps as small as 25 nm using pulsed chemical vapor deposition. In situ optical extinction measurements indicate that over 98% of the nanotubes reinitiate growth on successive gas pulses, and HRTEM images show that the SWNTs do not exhibit segments, caps, or noticeable sidewall defects resulting from repeatedly stopping and restarting growth. Time-resolved laser reflectivity (3-ms temporal resolution) was used to record the nucleation and growth kinetics for each fast (0.2 second) gas pulse and measure the height increase of the array in situ, providing a "digital" method to incrementally grow short nanotube arrays to precise heights. Derivatives of the optical reflectivity signal reveal distinct temporal signatures for both nucleation and growth kinetics, with their amplitude ratio on the first gas pulse serving as a good predictor for aligned vs. unaligned growth. Incremental growth by pulsed CVD is interpreted in the context of autocatalytic kinetic models as a special processing window in which a sufficiently high flux of feedstock gas drives the nucleation and rapid growth phases of a catalyst nanoparticle ensemble to occur within the temporal period of the gas pulse, but without inducing growth termination.

36 MATERIALS SCIENCE↗

Electric field enhanced diffusion welding of alloy 617: Microstructural characteristics and mechanical properties

This study investigated the microstructural characteristics and mechanical behavior of diffusion welded nickel-based Alloy 617 obtained by electric field-assisted sintering (EFAS) using various parameters. The interfacial microstructure exhibited different characteristics including good grain boundary (GB) migration across the interface in the samples diffusion-welded at 1100 °C and a flat interface in the samples joined at 1000 °C and 1050 °C. The interface consisted of fine Al 2 O 3 oxides, while precipitation of interfacial M 23 C 6 carbides was not observed. Grain boundaries migrated across the Al 2 O 3 oxides, leaving these oxides within the grains. Graded grain size was observed, with grain coarsening being more significant near the sample surface due to the temperature gradient induced by EFAS. Tensile testing revealed that the specimens fractured in the matrix away from the interface, indicting strong diffusion-welded joints. Further, the peak tensile strength of 807 MPa was obtained in the samples welded at 1000 °C due to minimal grain growth. The materials obtained at 1100 °C exhibited reduced tensile strength but improved ductility. Strain maps revealed by digital image correlation showed alternating high and low strain segments in the samples produced at 1000 °C and 1050 °C, indicating that the flat interfaces with no GB migration were less ductile compared to the matrix. A greater strain uniformity was observed along the bond interfaces with improved GB migration. The hardness reduced near the sample surfaces due to enlarged grains induced by temperature gradient. This study demonstrates that GB migration and enhanced mechanical strength can be achieved in diffusion-welded Alloy 617.

36 MATERIALS SCIENCE↗

Measuring thermal curing induced shrinkage of material extrusion based additive manufacturing silicone elastomer lattices by X-ray computed tomography

Thermal curing induces shrinkage in material extrusion based additive manufacturing silicone elastomer samples, resulting in discrepancies between as printed and final geometries. Knowing the extent to which the samples change in shape and size allows us to make appropriate modifications to the printing design to better control the geometry of the samples. We present an X-ray computed tomography (CT) based approach to determine filament-level shrinkage due to thermal curing of silicone elastomer samples printed with direct ink writing (DIW). The approach relies on custom-designed build plates that are resistant to the elevated curing temperatures and that have sufficiently distinct X-ray absorption characteristics from the silicone elastomer to ensure adequate segmentation of the latter in X-ray imaging data. We implement the approach to evaluate shrinkage in DIW ‘log pile’ samples with three distinct strand arrangements and demonstrate of how filament-level information can be extracted from the X-ray CT data.

Additive manufacturing↗

Phase contrast tomography of the mouse cochlea at microfocus x-ray sources

We present phase contrast x-ray tomography of functional soft tissue within the bony cochlear capsule of mice, carried out at laboratory microfocus sources with well-matched source, detector, geometry, and reconstruction algorithms at spatial resolutions down to 2 μm. Contrast, data quality and resolution enable the visualization of thin membranes and nerve fibers as well as automated segmentation of surrounding bone. By complementing synchrotron radiation imaging techniques, a broad range of biomedical applications becomes possible as demonstrated for optogenetic cochlear implant research.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Bragg Spot Finder (BSF): a new machine-learning-aided approach to deal with spot finding for rapidly filtering diffraction pattern images

Macromolecular crystallography contributes significantly to understanding diseases and, more importantly, how to treat them by providing atomic resolution 3D structures of proteins. This is achieved by collecting X-ray diffraction images of protein crystals from important biological pathways. Spotfinders are used to detect the presence of crystals with usable data, and the spots from such crystals are the primary data used to solve the relevant structures. Having fast and accurate spot finding is essential, but recent advances in synchrotron beamlines used to generate X-ray diffraction images have brought us to the limits of what the best existing spotfinders can do. This bottleneck must be removed so spotfinder software can keep pace with the X-ray beamline hardware improvements and be able to see the weak or diffuse spots required to solve the most challenging problems encountered when working with diffraction images. In this paper, we first present Bragg Spot Detection (BSD), a large benchmark Bragg spot image dataset that contains 304 images with more than 66 000 spots. We then discuss the open source extensible U-Net-based spotfinder Bragg Spot Finder (BSF), with image pre-processing, a U-Net segmentation backbone, and post-processing that includes artifact removal and watershed segmentation. Finally, we perform experiments on the BSD benchmark and obtain results that are (in terms of accuracy) comparable to or better than those obtained with two popular spotfinder software packages ( Dozor and DIALS ), demonstrating that this is an appropriate framework to support future extensions and improvements.

36 MATERIALS SCIENCE↗

DeepAndes: A Self-Supervised Vision Foundation Model for Multispectral Remote Sensing Imagery of the Andes

By mapping sites at large scales usingremotely sensed data, archaeologists can generate unique insights into long-term demographic trends, interregional social networks, and human adaptations in the past. Remote sensing surveys complement field-based approaches, and their reach can be especially great when combined with deep learning and computer vision techniques. However, conventional supervised deep learning methods face challenges in annotating fine-grained archaeological features at scale. In addition, while recent vision foundation models have shown remarkable success in learning large-scale remote sensing data with minimal annotations, most off-the-shelf solutions are designed for RGB images rather than multispectral satellite imagery, such as the eight-band data used in our study. In this article, we introduce DeepAndes, a transformer-based vision foundation model trained on three million multispectral satellite images, specifically tailored for Andean archaeology. DeepAndes incorporates a customized DINOv2 self-supervised learning algorithm optimized for eight-band multispectral imagery, marking the first foundation model designed explicitly for the Andes region. We evaluate its image understanding performance through imbalanced image classification, image instance retrieval, and pixel-level semantic segmentation tasks. Our experiments show that DeepAndes achieves superior F1 scores, mean average precision, and Dice scores in few-shot learning scenarios, significantly outperforming models trained from scratch or pretrained on smaller datasets. This underscores the effectiveness of large-scale self-supervised pretraining in archaeological remote sensing.

Guo, Junlin [Vanderbilt Univ., Nashville, TN (Unit↗

SU-D-209-06: Study On the Dose Conversion Coefficients in Pediatric Radiography with the Development of Children Voxel Phantoms

Purpose: Conversion coefficients of organ dose normalized to entrance skin dose (ESD) are widely used to evaluate the organ doses directly using ESD without time-consuming dose measurement, this work aims to investigate the dose conversion coefficients in pediatric chest and abdomen radiography with the development of 5 years and 10 years old children voxel phantoms. Methods: After segmentation of organs and tissues from CT slice images of ATOM tissue-equivalent phantoms, a 5-year-old and a 10-year-old children computational voxel phantoms were developed for Monte Carlo simulation. The organ doses and the entrance skin dose for pediatric chest postero-anterior projection and abdominal antero-posterior projection were simulated at the same time, and then the organ dose conversion coefficients were calculated.To verify the simulated results, dose measurement was carried out with ATOM tissue-equivalent phantoms for 5 year chest radiography. Results: Simulated results and experimental results matched very well with each other, the result differences of all the organs covered in radiation field were below 16% for 5-year-old child in chest projection. I showed that the conversion coefficients of organs covered in the radiation field were much larger than organs out of the field for all the study cases, for example, the conversion coefficients of stomach, liver intestines, and pancreas are larger for abdomen radiography while conversion coefficients of lungs are larger for chest radiography. Conclusion: The voxel children phantoms were helpful to evaluate the radiation doses more accurately and efficiently. Radiation field was the essential factor that affects the organ dose, use reasonably small field should be encouraged for radiation protection. This work was supported by the National Natural Science Foundation of China(11475047)

60 APPLIED LIFE SCIENCES↗

A Deployable Fast-Neutron Coded-Aperture Imager for Quantifying Nuclear Material

This brief report describes a newly constructed fast-neutron coded-aperture imager intended for performing quantitative measurements of plutonium holdup at fuel cycle facilities. The present report was prepared in fulfillment of milestone M4FT-13OR0402053, “Construct Imager,” of Fuel Cycle R&D Material Protection, Accounting, and Control Technology (MPACT) work package FT-13OR040205, “Fast-Neutron Imaging to Quantify Nuclear Materials - ORNL.” The first measurements taken with the imager will be reported later this year. For quantifying plutonium holdup, fast-neutron imaging is an appealing mode of measurement in scenarios where contact measurements with non-imaging detectors may be impractical or their results ambiguous because of surrounding material or nearby sources. The use of fast neutrons (as opposed to the more numerous gamma rays) is appealing because of their ability to penetrate equipment and nuclear material that is opaque to gamma radiation. However, the ability of fast neutrons to penetrate material also leads to challenges in constructing a fast-neutron imager sufficiently compact to be amenable for use in a facility while remaining sufficiently sensitive to neutron emanations from plutonium or other actinides. Furthermore, while fast-neutron detection is commonly achieved via organic liquid scintillators, use of the imager in a production environment favors mechanically robust detectors that do not contain flammable liquid. In response to these challenges, Oak Ridge National Laboratory (ORNL) has built a fast-neutron imager that has a compact footprint and whose segmented neutron detectors are built from the first cast plate of the recently developed plastic scintillator with pulse shape discrimination properties developed by Lawrence Livermore National Laboratory and commercially available from Eljen Technologies as experimental scintillator EJ-299-34.

Hausladen, Paul↗

Expanded genetic variation (SNP) and phenomics (image based) dataset for Populus trichocarpa

The image dataset consists of 11,791 images representing 1,219 genotypes of Populus trichocarpa undergoing in planta regeneration. Genotypes were imaged with a median of four weekly timepoints and a median of two replicates each. A representative and diverse subset of 249 images was annotated using the IDEAS annotation interface (ideas.eecs.oregonstate.edu) and these annotated images were used to train a deep semantic segmentation model (PSPNet), which was deployed for inference over the entire dataset. Annotated classes include specific stages of regeneration (callus and shoot) in addition to unregenerated plant material and background. Statistics of relative tissue area were extracted and used for downstream genetic association mapping in a genome-wide association study. The SNP dataset consists of over 40 million single-nucleotide polymorphisms across 1,323 wild accessions of Populus trichocarpa

09 BIOMASS FUELS↗

NASA TM processing system description and status

An overview of the processing system for LANDSAT 4 imagery is given. Charts show system requirements; the LANDSAT 4 ground segment with its elements, configurations, and data flow; image generation requirements; and the major functions, interfaces, and hardware of the image generation facility and the image processing systems for the TM and MSS. The laser beam film recorder and the high density digital recorder are covered as well as the data available at EDC.

Watt, W.↗

Model-based orientation-independent 3-D machine vision techniques

Orientation-dependent techniques for the identification of a three-dimensional object by a machine vision system are represented in parts. In the first part, the data consist of intensity images of polyhedral objects obtained by a single camera, while in the second part, the data consist of range images of curved objects obtained by a laser scanner. In both cases, the attributed graphic representation of the object surface is used to drive the respective algorithm. In this representation, a graph node represents a surface patch and a link represents the adjacency between two patches. The attributes assigned to nodes are moment invariants of the corresponding face for polyhedral objects. For range images, the Gaussian curvature is used as a segmentation criterion for providing symbolic shape attributes. Identification is achieved by an efficient graph-matching algorithm used to match the graph obtained from the data to a subgraph of one of the model graphs stored in the commputer memory.

De Figueiredo, R. J. P.↗

Unsupervised Segmentation Of Polarimetric SAR Data

Method of unsupervised segmentation of polarimetric synthetic-aperture-radar (SAR) image data into classes involves selection of classes on basis of multidimensional fuzzy clustering of logarithms of parameters of polarimetric covariance matrix. Data in each class represent parts of image wherein polarimetric SAR backscattering characteristics of terrain regarded as homogeneous. Desirable to have each class represent type of terrain, sea ice, or ocean surface distinguishable from other types via backscattering characteristics. Unsupervised classification does not require training areas, is nearly automated computerized process, and provides nonsubjective selection of image classes naturally well separated by radar.

Rignot, Eric J.↗

Segmentation Of Polarimetric SAR Data

Report presents one in continuing series of studies of segmentation of polarimetric synthetic-aperture-radar, SAR, image data into regions. Studies directed toward refinement of method of automated analysis of SAR data.

Rignot, Eric J. M.↗

Neural networks: Application to medical imaging

The research mission is the development of computer assisted diagnostic (CAD) methods for improved diagnosis of medical images including digital x-ray sensors and tomographic imaging modalities. The CAD algorithms include advanced methods for adaptive nonlinear filters for image noise suppression, hybrid wavelet methods for feature segmentation and enhancement, and high convergence neural networks for feature detection and VLSI implementation of neural networks for real time analysis. Other missions include (1) implementation of CAD methods on hospital based picture archiving computer systems (PACS) and information networks for central and remote diagnosis and (2) collaboration with defense and medical industry, NASA, and federal laboratories in the area of dual use technology conversion from defense or aerospace to medicine.

Clarke, Laurence P.↗

Highlights of 10-Year Remote Sensing Industry Analysis

A background and highlights of a 10 year remote sensing industry analysis are provided.Included are the following:Training, educational analysis, staff levels, and end-users analysis, market drivers, market segments,application areas, spatial resolution needs, use of image types.

Rabin, Ron↗