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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Co-Registered In-Situ and Ex-Situ Dataset from an Electron Beam Powder Bed Fusion Additive Manufacturing Process (Peregrine v2023-09)

This release contains a co-registered in-situ and ex-situ Peregrine dataset from a single Arcam Q10 Electron Beam Powder Bed Fusion (EB-PBF) Inconel 738 build. These data were collected at the Manufacturing Demonstration Facility (MDF) located at Oak Ridge National Laboratory (ORNL). The dataset includes layer-wise Near Infrared (NIR) in-situ imaging data, in-situ temporal sensor data, ex-situ X-Ray Computed Tomography (X-CT) scans, and the target part geometries. Additionally, anomaly detections produced by a trained Dynamic Segmentation Convolutional Neural Network (DSCNN) are provided.

36 MATERIALS SCIENCE↗
DOE OSTI2016

SU-F-I-45: An Automated Technique to Measure Image Contrast in Clinical CT Images

Purpose: To develop and validate an automated technique for measuring image contrast in chest computed tomography (CT) exams. Methods: An automated computer algorithm was developed to measure the distribution of Hounsfield units (HUs) inside four major organs: the lungs, liver, aorta, and bones. These organs were first segmented or identified using computer vision and image processing techniques. Regions of interest (ROIs) were automatically placed inside the lungs, liver, and aorta and histograms of the HUs inside the ROIs were constructed. The mean and standard deviation of each histogram were computed for each CT dataset. Comparison of the mean and standard deviation of the HUs in the different organs provides different contrast values. The ROI for the bones is simply the segmentation mask of the bones. Since the histogram for bones does not follow a Gaussian distribution, the 25th and 75th percentile were computed instead of the mean. The sensitivity and accuracy of the algorithm was investigated by comparing the automated measurements with manual measurements. Fifteen contrast enhanced and fifteen non-contrast enhanced chest CT clinical datasets were examined in the validation procedure. Results: The algorithm successfully measured the histograms of the four organs in both contrast and non-contrast enhanced chest CT exams. The automated measurements were in agreement with manual measurements. The algorithm has sufficient sensitivity as indicated by the near unity slope of the automated versus manual measurement plots. Furthermore, the algorithm has sufficient accuracy as indicated by the high coefficient of determination, R2, values ranging from 0.879 to 0.998. Conclusion: Patient-specific image contrast can be measured from clinical datasets. The algorithm can be run on both contrast enhanced and non-enhanced clinical datasets. The method can be applied to automatically assess the contrast characteristics of clinical chest CT images and quantify dependencies that may not be captured in phantom data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

A dual model HU conversion from MRI intensity values within and outside of bone segment for MRI-based radiotherapy treatment planning of prostate cancer

Purpose: The lack of electron density information in magnetic resonance images (MRI) poses a major challenge for MRI-based radiotherapy treatment planning (RTP). In this study the authors convert MRI intensity values into Hounsfield units (HUs) in the male pelvis and thus enable accurate MRI-based RTP for prostate cancer patients with varying tissue anatomy and body fat contents. Methods: T{sub 1}/T{sub 2}*-weighted MRI intensity values and standard computed tomography (CT) image HUs in the male pelvis were analyzed using image data of 10 prostate cancer patients. The collected data were utilized to generate a dual model HU conversion technique from MRI intensity values of the single image set separately within and outside of contoured pelvic bones. Within the bone segment local MRI intensity values were converted to HUs by applying a second-order polynomial model. This model was tuned for each patient by two patient-specific adjustments: MR signal normalization to correct shifts in absolute intensity level and application of a cutoff value to accurately represent low density bony tissue HUs. For soft tissues, such as fat and muscle, located outside of the bone contours, a threshold-based segmentation method without requirements for any patient-specific adjustments was introduced to convert MRI intensity values into HUs. The dual model HU conversion technique was implemented by constructing pseudo-CT images for 10 other prostate cancer patients. The feasibility of these images for RTP was evaluated by comparing HUs in the generated pseudo-CT images with those in standard CT images, and by determining deviations in MRI-based dose distributions compared to those in CT images with 7-field intensity modulated radiation therapy (IMRT) with the anisotropic analytical algorithm and 360° volumetric-modulated arc therapy (VMAT) with the Voxel Monte Carlo algorithm. Results: The average HU differences between the constructed pseudo-CT images and standard CT images of each test patient ranged from −2 to 5 HUs and from 22 to 78 HUs in soft and bony tissues, respectively. The average local absolute value differences were 11 HUs in soft tissues and 99 HUs in bones. The planning target volume doses (volumes 95%, 50%, 5%) in the pseudo-CT images were within 0.8% compared to those in CT images in all of the 20 treatment plans. The average deviation was 0.3%. With all the test patients over 94% (IMRT) and 92% (VMAT) of dose points within body (lower than 10% of maximum dose suppressed) passed the 1 mm and 1% 2D gamma index criterion. The statistical tests (t- and F-tests) showed significantly improved (p ≤ 0.05) HU and dose calculation accuracies with the soft tissue conversion method instead of homogeneous representation of these tissues in MRI-based RTP images. Conclusions: This study indicates that it is possible to construct high quality pseudo-CT images by converting the intensity values of a single MRI series into HUs in the male pelvis, and to use these images for accurate MRI-based prostate RTP dose calculations.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Untargeted Spatial Metabolomics and Spatial Proteomics on the Same Tissue Section

An increasing number of spatial multiomic workflows have been recently developed. Some of these approaches have leveraged initial mass spectrometry imaging (MSI)-based spatial metabolomics to inform region of interest (ROI) selection for downstream spatial proteomics. However, these workflows have been limited by varied substrate requirements between modalities or have required analyzing serial sections (i.e., one section per modality). To mitigate these issues, we present a novel multiomic workflow that uses desorption electrospray ionization (DESI)-MSI to identify representative spatial metabolite patterns on-tissue prior to spatial proteomic analyses on the same tissue section. Further, this workflow is demonstrated here with a model mammalian tissue (coronal rat brain section) mounted on a polyethylene naphthalate-membrane slide. Initial DESI-MSI resulted in 160 annotations (SwissLipids) within to the METASPACE platform (≤20% false discovery rate). A segmentation map from the annotated ion images informed downstream ROI selection for spatial proteomics characterization from the same sample. The unspecific substrate requirements and minimal sample disruption inherent to DESI-MSI allowed for an optimized, downstream spatial proteomics assay, resulting in 3888 ± 240 to 4717 ± 48 proteins being confidently directed per ROI (200 µm x 200 µm). Finally, we demonstrate the integration of multiomic information, where we found ceramide localization to be correlated with SMPD3 abundance (ceramide synthesis protein), and we also utilized protein abundance to resolve metabolite isomeric ambiguity. Overall, the integration of DESI-MSI into the multiomic workflow allows for complementary spatial and molecular-level information to be achieved from optimized implementations of each MS assay inherent to the workflow itself.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

PyCMG-based Simulation of Volumetric Concrete Microstructure

Concrete is a complex, heterogeneous material with a microstructure composed of aggregates, cement paste, and pores spanning multiple length scales. Understanding this microstructure is critical for advancing the performance, durability, and modeling of concrete-based systems. While experimental imaging such as X-ray computed tomography (XCT) provides valuable insights, generating large datasets with detailed ground truth annotations is both costly and labor-intensive due to challenges in segmenting similar phases, such as aggregates and cement paste, that often share similar attenuation properties. To address this, we developed a pipeline to simulate realistic 3D concrete microstructures using the open-source Python package PyCMG. This simulation effort focuses on generating high-fidelity, annotated microstructures that can serve as training or benchmarking datasets for image analysis, segmentation algorithms, and machine learning models, particularly in scenarios where experimental data is scarce.

Ziabari, Amir [Oak Ridge National Laboratory; ORNL↗
NASA NTRS1973

Digital processing of stereoscopic image pairs.

The problem under consideration is concerned with scene analysis during robot navigation on the surface of Mars. In this mode, the world model of the robot must be continuously updated to include sightings of new obstacles and scientific samples. In order to describe the content of a particular scene, it is first necessary to segment it into known objects. One technique for accomplishing this segmentation is by analyzing the pair of images produced by the stereoscopic cameras mounted on the robot. A heuristic method is presented for determining the range for each point in the two-dimensional scene under consideration. The method is conceptually based on a comparison of corresponding points in the left and right images of the stereo pair. However, various heuristics which are adaptive in nature are used to make the algorithm both efficient and accurate. Examples are given of the use of this so-called range picture for the purpose of scene segmentation.

Levine, M. D.↗
DOE OSTI2025

Super-Resolution Ptychography with Small Segmented Detectors

To overcome the spatial resolution limit set by aperture-limited diffraction in traditional scanning transmission electron microscopy, microscopists have developed ptychography enabled by iterative phase retrieval algorithms and high-dynamic-range pixel array detectors. Current detector designs are limited by the data rate off chip, so a high-pixel-count detector has a proportionally lower frame rate than the few-segment detectors used for differential phase contrast (DPC) imaging. This slower acquisition speed leads to heightened vulnerability to scan noise, drift, and potential sample damage. This creates opportunities for repurposing fast segmented detectors for ptychography by trading a reduction in reciprocal space pixels for an increase in real space pixels. Here, we explore a strategy of oversampling in real space and instead apply detector pixel upsampling during the reconstruction process. Further, we demonstrate the viability of achieving super-resolution ptychography on thin objects using only 2 × 2 detector pixels, surpassing the resolution of integrated DPC (iDPC) imaging. With optimization using simulated datasets and experiments on MoTe 2 /WSe 2 bilayer moiré superlattices, we achieved super-resolution ptychography reconstructions under rapid acquisition conditions (37.5 pA, 1 μs dwell time), yielding over 50% improvements in contrast and information limit compared to annular dark field and iDPC imaging on the same detectors.

2D materials↗
DOE OSTI2012

On proton CT reconstruction using MVCT-converted virtual proton projections

Purpose: To describe a novel methodology of converting megavoltage x-ray projections into virtual proton projections that are otherwise missing due to the proton range limit. These converted virtual proton projections can be used in the reconstruction of proton computed tomography (pCT). Methods: Relations exist between proton projections and multispectral megavoltage x-ray projections for human tissue. Based on these relations, these tissues can be categorized into: (a) adipose tissue; (b) nonadipose soft tissues; and (c) bone. These three tissue categories can be visibly identified on a regular megavoltage x-ray computed tomography (MVCT) image. With an MVCT image and its projection data available, the x-ray projections through heterogeneous anatomy can be converted to the corresponding proton projections using predetermined calibration curves for individual materials, aided by a coarse segmentation on the x-ray CT image. To show the feasibility of this approach, mathematical simulations were carried out. The converted proton projections, plotted on a proton sinogram, were compared to the simulated ground truth. Proton stopping power images were reconstructed using either the virtual proton projections only or a blend of physically available proton projections and virtual proton projections that make up for those missing due to the range limit. These images were compared to a reference image reconstructed from theoretically calculated proton projections. Results: The converted virtual projections had an uncertainty of {+-}0.8% compared to the calculated ground truth. Proton stopping power images reconstructed using a blend of converted virtual projections (48%) and physically available projections (52%) had an uncertainty of {+-}0.86% compared with that reconstructed from theoretically calculated projections. Reconstruction solely from converted virtual proton projections had an uncertainty of {+-}1.1% compared with that reconstructed from theoretical projections. If these images are used for treatment planning, the average proton range uncertainty is estimated to be less than 1.5% for an imaging dose in the milligray range. Conclusions: The proposed method can be used to convert x-ray projections into virtual proton projections. The converted proton projections can be blended with existing proton projections or can be used solely for pCT reconstruction, addressing the range limit problem of pCT using current therapeutic proton machines.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2006

Performance of Dispersed Fringe Sensor in the Presence of Segmented Mirror Aberrations: Modeling and Simulation

Dispersed Fringe Sensing (DFS) is an efficient and robust method for coarse phasing of a segmented primary mirror such as the James Webb Space Telescope (JWST). In this paper, modeling and simulations are used to study the effect of segmented mirror aberrations on the fringe image, DFS signals and DFS detection accuracy. The study has shown due to the pixilation spatial filter effect from DFS signal extraction the effect of wavefront error is reduced and DFS algorithm will be more robust against wavefront aberration by using multi-trace DFS approach. We also studied the JWST Dispersed Hartmann Sensor (DHS) performance in presence of wavefront aberrations caused by the gravity sag and we use the scaled gravity sag to explore the JWST DHS performance relationship with the level of the wavefront aberration. This also includes the effect from line-of-sight jitter.

segmented mirrors↗
DOE OSTI2013

Combining multiple FDG-PET radiotherapy target segmentation methods to reduce the effect of variable performance of individual segmentation methods

Purpose: Many approaches have been proposed to segment high uptake objects in 18F-fluoro-deoxy-glucose positron emission tomography images but none provides consistent performance across the large variety of imaging situations. This study investigates the use of two methods of combining individual segmentation methods to reduce the impact of inconsistent performance of the individual methods: simple majority voting and probabilistic estimation. Methods: The National Electrical Manufacturers Association image quality phantom containing five glass spheres with diameters 13-37 mm and two irregularly shaped volumes (16 and 32 cc) formed by deforming high-density polyethylene bottles in a hot water bath were filled with 18-fluoro-deoxyglucose and iodine contrast agent. Repeated 5-min positron emission tomography (PET) images were acquired at 4:1 and 8:1 object-to-background contrasts for spherical objects and 4.5:1 and 9:1 for irregular objects. Five individual methods were used to segment each object: 40% thresholding, adaptive thresholding, k-means clustering, seeded region-growing, and a gradient based method. Volumes were combined using a majority vote (MJV) or Simultaneous Truth And Performance Level Estimate (STAPLE) method. Accuracy of segmentations relative to CT ground truth volumes were assessed using the Dice similarity coefficient (DSC) and the symmetric mean absolute surface distances (SMASDs). Results: MJV had median DSC values of 0.886 and 0.875; and SMASD of 0.52 and 0.71 mm for spheres and irregular shapes, respectively. STAPLE provided similar results with median DSC of 0.886 and 0.871; and median SMASD of 0.50 and 0.72 mm for spheres and irregular shapes, respectively. STAPLE had significantly higher DSC and lower SMASD values than MJV for spheres (DSC, p < 0.0001; SMASD, p= 0.0101) but MJV had significantly higher DSC and lower SMASD values compared to STAPLE for irregular shapes (DSC, p < 0.0001; SMASD, p= 0.0027). DSC was not significantly different between 128 Multiplication-Sign 128 and 256 Multiplication-Sign 256 grid sizes for either method (MJV, p= 0.0519; STAPLE, p= 0.5672) but was for SMASD values (MJV, p < 0.0001; STAPLE, p= 0.0164). The best individual method varied depending on object characteristics. However, both MJV and STAPLE provided essentially equivalent accuracy to using the best independent method in every situation, with mean differences in DSC of 0.01-0.03, and 0.05-0.12 mm for SMASD. Conclusions: Combining segmentations offers a robust approach to object segmentation in PET. Both MJV and STAPLE improved accuracy and were robust against the widely varying performance of individual segmentation methods. Differences between MJV and STAPLE are such that either offers good performance when combining volumes. Neither method requires a training dataset but MJV is simpler to interpret, easy to implement and fast.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Three-dimensional, Time-Resolved, Intrafraction Motion Monitoring Throughout Stereotactic Liver Radiation Therapy on a Conventional Linear Accelerator

Purpose: To investigate the time-resolved 3-dimensional (3D) internal motion throughout stereotactic body radiation therapy (SBRT) of tumors in the liver using standard x-ray imagers of a conventional linear accelerator. Methods and Materials: Ten patients with implanted gold markers received 11 treatment courses of 3-fraction SBRT in a stereotactic body-frame on a conventional linear accelerator. Two pretreatment and 1 posttreatment cone-beam computed tomography (CBCT) scans were acquired during each fraction. The CBCT projection images were used to estimate the internal 3D marker motion during CBCT acquisition with 11-Hz resolution by a monoscopic probability-based method. Throughout the treatment delivery by conformal or volumetric modulated arc fields, simultaneous MV portal imaging (8 Hz) and orthogonal kV imaging (5 Hz) were applied to determine the 3D marker motion using either MV/kV triangulation or the monoscopic method when marker segmentation was unachievable in either MV or kV images. The accuracy of monoscopic motion estimation was quantified by also applying monoscopic estimation as a test for all treatments during which MV/kV triangulation was possible. Results: Root-mean-square deviations between monoscopic estimations and triangulations were less than 1.0 mm. The mean 3D intrafraction and intrafield motion ranges during liver SBRT were 17.6 mm (range, 5.6-39.5 mm) and 11.3 mm (2.1-35.5mm), respectively. The risk of large intrafraction baseline shifts correlated with intrafield respiratory motion range. The mean 3D intrafractional marker displacement relative to the first CBCT was 3.4 mm (range, 0.7-14.5 mm). The 3D displacements exceeded 8.8 mm 10% of the time. Conclusions: Highly detailed time-resolved internal 3D motion was determined throughout liver SBRT using standard imaging equipment. Considerable intrafraction motion was observed. The demonstrated methods provide a widely available approach for motion monitoring that, combined with motion-adaptive treatment techniques, has the potential to improve the accuracy of radiation therapy for moving targets.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS1987

Measurements of OH(X2pi) in the stratosphere by high resolution UV spectroscopy

This paper reports the first results obtained using high spectral resolution imaging ultraviolet spectroscopy to observe multiple rotational lines of OH A2 Sigma-X2pi (0-0) band. A 9.2 A spectral segment from 3075.8 A to 3085.0 A is imaged at 0.08 A FWHM spectral resolution, allowing the simultaneous acquisition of six of the brightest OH resonance fluorescence emission lines. The high spectral resolution and low scattered light design of the instrument allows these lines to be detected above the Rayleigh scattered sunlight background. The technique permits remote sensing of stratospheric OH from a high altitude instrument. The instrument was flown to an altitude of 40 km on Aug. 25, 1983, and again on June 12, 1986, on scientific balloons from Palestine, TX. The OH profiles inverted from the limb scans made during these flights are reported here. These profiles represent the first measurements of the temporal variation of OH over an extended height range. The results demonstrate that the technique can be used to monitor OH from orbit.

Torr, D. G.↗
NASA NTRS1992

Modeling and initialization of a segmented telescope

The closely integrated design effort entailed by such prospective NASA-JPL project hardware as the Large Deployable Reflector, encompassing controls, dynamics, structures, optics, and thermal considerations, has prompted the development of the Figure Control Simulation, or 'FCSim' simulation program. This code is presently demonstrated for the case of a 7-segment, 3.65-m diameter primary mirror designed for 100-800 micron wavelengths. The optical component of the analysis can handle multispectral images, extended sources, image smear due to structural jitter, and mirror segment surface irregularities.

Sirlin, Samuel W.↗
DOE OSTI2014

TH-E-17A-05: Optimizing Four Dimensional Cone Beam Computed Tomography Projection Allocation to Respiratory Bins

Purpose: Four dimensional cone beam computed tomography (4DCBCT) is an emerging image guidance strategy but it can suffer from poor image quality. To avoid repeating scans it is beneficial to make the best use of the imaging data obtained. For conventional 4DCBCT the location and size of respiratory bins is fixed and projections are allocated to the respiratory bin within which it falls. Strictly adhering to this rule is unnecessary and can compromise image quality. In this study we optimize the size and location of respiratory bins and allow projections to be sourced from adjacent phases of the respiratory cycle. Methods: A mathematical optimization framework using mixed integer quadratic programming has been developed that determines when to source projections from adjacent respiratory bins and optimizes the size and location of the bins. The method, which we will call projection sharing, runs in under 2 seconds of CPU time. Five 4DCBCT datasets of stage III-IV lung cancer patients were used to test the algorithm. The standard deviation of the angular separation between projections (SD-A) and the standard deviation in the volume of the reconstructed fiducial gold coil (SD-V) were used as proxies to measure streaking artefacts and motion blur respectively. Results: The SD-A using displacement binning and projection sharing was 30%–50% smaller than conventional phase based binning and 59%–76% smaller than conventional displacement binning indicating more uniformly spaced projections and fewer streaking artefacts. The SD-V was 20–90% smaller when using projection sharing than using conventional phase based binning suggesting more uniform marker segmentation and less motion blur. Conclusion: Image quality was visibly and significantly improved with projection sharing. Projection sharing does not require any modifications to existing hardware and offers a more robust replacement to phase based binning, or, an option if phase based reconstruction is not of a suitable quality. This project is supported by an NHMRC Australia Fellowship and NHMRC project grant number 1034060.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-AB-BRA-11: Evaluation of Fully Automatic Volumetric GBM Segmentation in the TCGA-GBM Dataset: Prognosis and Correlation with VASARI Features

Purpose: Reproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features. Methods: MRI sets of 67 GBM patients were downloaded from the Cancer Imaging archive. GBM sub-compartments were defined manually and automatically using the Brain Tumor Image Analysis (BraTumIA), including necrosis, edema, contrast enhancing and non-enhancing tumor. Spearman’s correlation was used to evaluate the agreement with VASARI features. Prognostic significance was assessed using the C-index. Results: Auto-segmented sub-volumes showed high agreement with manually delineated volumes (range (r): 0.65 – 0.91). Also showed higher correlation with VASARI features (auto r = 0.35, 0.60 and 0.59; manual r = 0.29, 0.50, 0.43, for contrast-enhancing, necrosis and edema, respectively). The contrast-enhancing volume and post-contrast abnormal volume showed the highest C-index (0.73 and 0.72), comparable to manually defined volumes (p = 0.22 and p = 0.07, respectively). The non-enhancing region defined by BraTumIA showed a significantly higher prognostic value (CI = 0.71) than the edema (CI = 0.60), both of which could not be distinguished by manual delineation. Conclusion: BraTumIA tumor sub-compartments showed higher correlation with VASARI data, and equivalent performance in terms of prognosis compared to manual sub-volumes. This method can enable more reproducible definition and quantification of imaging based biomarkers and has a large potential in high-throughput medical imaging research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

The Effect of Membrane Casting Irregularities on Initial Fuel Cell Performance

Membrane electrode assemblies (MEA) for polymer electrolyte membrane fuel cells (PEMFC) may possess as-manufactured non-uniformities in any of its constituent components. This article studies casting irregularities located within the PEM to understand their potential impact on MEA initial performance. Membrane material was cast on a lab-scale polymer casting line operating either within or at the boundaries of the process window. The resulting membrane material was either pristine in the former case, or, in the latter case, contained air bubbles, cracks, and other irregularities, ranging in size from about 0.6 to 3 mm. Spatial polarization experiments were conducted using a 121-channel segmented fuel cell system, and thermal imaging was performed subsequently to map hydrogen crossover to spatial performance. While total-cell polarization data was minimally impacted by the irregularities, the spatial diagnostics showed local performance impacts that, in operation over time, could cause degradation in performance or earlier failure of the MEA. Such impacts could lead to these irregularities being classified as defects, i.e., manufacturing variations that should be identified and not included in a fuel cell stack. Classification of irregularities as defects will ultimately assist the industry by contributing to the development of threshold detection limits for in-line quality control diagnostics.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

An intensity-based post-processing tool for 3D instance segmentation of organelles in soft X-ray tomograms

Investigating the 3D structures and rearrangements of organelles within a single cell is critical for better characterizing cellular function. Imaging approaches such as soft X-ray tomography have been widely applied to reveal a complex subcellular organization involving multiple inter-organelle interactions. However, 3D segmentation of organelle instances has been challenging despite its importance in organelle characterization. Here we propose an intensity-based post-processing tool to identify and separate organelle instances. Our tool separates sphere-like (insulin vesicle) and columnar-shaped organelle instances (mitochondrion) based on the intensity of raw tomograms, semantic segmentation masks, and organelle morphology. We validate our tool using synthetic tomograms of organelles and experimental tomograms of pancreatic β -cells to separate insulin vesicle and mitochondria instances. As compared to the commonly used connected regions labeling, watershed, and watershed + Gaussian filter methods, our tool results in improved accuracy in identifying organelles in the synthetic tomograms and an improved description of organelle structures in β -cell tomograms. In addition, under different experimental treatment conditions, significant changes in volumes and intensities of both insulin vesicle and mitochondrion are observed in our instance results, revealing their potential roles in maintaining normal β -cell function. Our tool is expected to be applicable for improving the instance segmentation of other images obtained from different cell types using multiple imaging modalities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

A promising limited angular computed tomography reconstruction via segmentation based regional enhancement and total variation minimization

X-ray computed tomography (CT) is a powerful and common inspection technique used for the industrial non-destructive testing. However, large-sized and heavily absorbing objects cause the formation of artifacts because of either the lack of specimen penetration in specific directions or the acquisition of data from only a limited angular range of views. Although the sparse optimization-based methods, such as the total variation (TV) minimization method, can suppress artifacts to some extent, reconstructing the images such that they converge to accurate values remains difficult because of the deficiency in continuous angular data and inconsistency in the projections. To address this problem, we use the idea of regional enhancement of the true values and suppression of the illusory artifacts outside the region to develop an efficient iterative algorithm. This algorithm is based on the combination of regional enhancement of the true values and TV minimization for the limited angular reconstruction. In this algorithm, the segmentation approach is introduced to distinguish the regions of different image knowledge and generate the support mask of the image. A new regularization term, which contains the support knowledge to enhance the true values of the image, is incorporated into the objective function. Then, the proposed optimization model is solved by variable splitting and the alternating direction method efficiently. A compensation approach is also designed to extract useful information from the initial projections and thus reduce false segmentation result and correct the segmentation support and the segmented image. The results obtained from comparing both simulation studies and real CT data set reconstructions indicate that the proposed algorithm generates a more accurate image than do the other reconstruction methods. The experimental results show that this algorithm can produce high-quality reconstructed images for the limited angular reconstruction and suppress the illusory artifacts caused by the deficiency in valid data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗