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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Physics guided machine learning for multi-material decomposition of tissues from dual-energy CT scans of simulated breast models with calcifications

We introduce a physics guided data-driven method for image-based multi-material decomposition for dual-energy computed tomography (CT) scans. The method is demonstrated for CT scans of virtual human phantoms containing more than two types of tissues. The method is a physics-driven supervised learning technique. We take advantage of the mass attenuation coefficient of dense materials compared to that of muscle tissues to perform a preliminary extraction of the dense material from the images using unsupervised methods. We then perform supervised deep learning on the images processed by the extracted dense material to obtain the final multi-material tissue map. The method is demonstrated on simulated breast models with calcifications as the dense material placed amongst the muscle tissues. The physics-guided machine learning method accurately decomposes the various tissues from input images, achieving a normalized root-mean-squared error of 2.75%.

Gopalakrishnan Meena, Murali↗
DOE OSTI2016

SU-D-207B-01: Radiomics Feature Reproducibility From Repeat CT Scans of Patients with Rectal Cancer

Purpose: To evaluate the reproducibility of radiomics features by repeating computed tomographic (CT) scans in rectal cancer. To choose stable radiomics features for rectal cancer. Methods: 40 rectal cancer patients were enrolled in this study, each of whom underwent two CT scans within average 8.7 days (5 days to 17 days), before any treatment was delivered. The rectal gross tumor volume (GTV) was distinguished and segmented by an experienced oncologist in both CTs. Totally, more than 2000 radiomics features were defined in this study, which were divided into four groups (I: GLCM, II: GLRLM III: Wavelet GLCM and IV: Wavelet GLRLM). For each group, five types of features were extracted (Max slice: features from the largest slice of target images, Max value: features from all slices of target images and choose the maximum value, Min value: minimum value of features for all slices, Average value: average value of features for all slices, Matrix sum: all slices of target images translate into GLCM and GLRLM matrices and superpose all matrices, then extract features from the superposed matrix). Meanwhile a LOG (Laplace of Gauss) filter with different parameters was applied to these images. Concordance correlation coefficients (CCC) and inter-class correlation coefficients (ICC) were calculated to assess the reproducibility. Results: 403 radiomics features were extracted from each type of patients’ medical images. Features of average type are the most reproducible. Different filters have little effect for radiomics features. For the average type features, 253 out of 403 features (62.8%) showed high reproducibility (ICC≥0.8), 133 out of 403 features (33.0%) showed medium reproducibility (0.8≥ICC≥0.5) and 17 out of 403 features (4.2%) showed low reproducibility (ICC≥0.5). Conclusion: The average type radiomics features are the most stable features in rectal cancer. Further analysis of these features of rectal cancer can be warranted for treatment monitoring and prognosis prediction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

MO-C-18C-01: Radiation Risks at Level of Few CT Scans: How Real?- Science to Practice

There are controversies surrounding radiation effects in human population in the range of radiation doses encountered by patients resulting from one to several CT scans. While it is understandable why the effects from low levels of diagnostic radiation are controversial, the situation is complicated by the media which may distort the known facts. There is need to understand the state of science regarding low-level radiation effects and also to understand how to communicate the potential risk with patients, the public and media. This session will seek to come to a consensus in order to speak with one voice to the media and the public. This session will review radiation effects known so far from a variety of exposed groups since the nuclear holocaust, provide clarification where effects are certain and where they are not, at what level extrapolation is the only way and at what level there is weak but agreeable acceptance. We will depict where and why there is agreement among organizations responsible for studying radiation effects, and how to deal with situations where effects are uncertain. Specific focus on radiation effects in children will be provided.Finally, the session will attempt to bridge the communication gap from the science to how to be an effective communicator with patients, parents, and media about ionizing radiation. Learning Objectives: To have a clear understanding about certainties and uncertainties of radiation effects at the level of a few CT scans To understand the results and limitations from 3 major pediatric CT scientific studies on childhood exposures published recently. To understand successful strategies used in risk communication.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Uncertainty quantification of the convolutional neural networks on permeability estimation from micro-CT scanned sandstone and carbonate rock images

Rock permeability is one of the most crucial properties affecting subsurface fluid flow behaviors. To accurately and robustly estimate the permeability, Digital Rock Physics, including micro-CT scanning technology and direct flow simulations on scanned images, has prevailed in recent years. Besides, machine learning techniques such as convolutional neural networks (CNNs) have been widely adopted and achieved success in permeability estimations directly from rock images. However, existing ML methods used for permeability estimation from rock images lack uncertainty quantification that causes unreliable predictions and overconfident estimations on out-of-distribution (OOD) samples. Here, in this work, we propose a PI3NN-CNN framework to address this problem. PI3NN-CNN consists of a CNN model for absolute permeability estimation and a PI3NN method to quantify the estimation uncertainty. It is able to quantify the uncertainty for in-distribution (InD) data with a desired confidence level, and identify OOD samples to avoid overconfident predictions. We demonstrate the method using micro-CT scanned images from two sandstone and two carbonate rocks. We found that PI3NN-CNN generates accurate predictions for InD samples, while producing high-quality prediction uncertainties regardless of the prediction accuracy. Meanwhile, PI3NN-CNN identifies OOD samples using its special network initialization scheme. The unique feature of PI3NN-CNN makes it applicable to more complex real-world image-based data for robust learning and predictions without overconfident estimations when the ground-truth information is unavailable.

58 GEOSCIENCES↗
DOE OSTI2016

Estimating local noise power spectrum from a few FBP-reconstructed CT scans

Purpose: Traditional ways to estimate 2D CT noise power spectrum (NPS) involve an ensemble average of the power spectrums of many noisy scans. When only a few scans are available, regions of interest are often extracted from different locations to obtain sufficient samples to estimate the NPS. Using image samples from different locations ignores the nonstationarity of CT noise and thus cannot accurately characterize its local properties. The purpose of this work is to develop a method to estimate local NPS using only a few fan-beam CT scans. Methods: As a result of FBP reconstruction, the CT NPS has the same radial profile shape for all projection angles, with the magnitude varying with the noise level in the raw data measurement. This allows a 2D CT NPS to be factored into products of a 1D angular and a 1D radial function in polar coordinates. The polar separability of CT NPS greatly reduces the data requirement for estimating the NPS. The authors use this property and derive a radial NPS estimation method: in brief, the radial profile shape is estimated from a traditional NPS based on image samples extracted at multiple locations. The amplitudes are estimated by fitting the traditional local NPS to the estimated radial profile shape. The estimated radial profile shape and amplitudes are then combined to form a final estimate of the local NPS. We evaluate the accuracy of the radial NPS method and compared it to traditional NPS methods in terms of normalized mean squared error (NMSE) and signal detectability index. Results: For both simulated and real CT data sets, the local NPS estimated with no more than six scans using the radial NPS method was very close to the reference NPS, according to the metrics of NMSE and detectability index. Even with only two scans, the radial NPS method was able to achieve a fairly good accuracy. Compared to those estimated using traditional NPS methods, the accuracy improvement was substantial when a few scans were available. Conclusions: The radial NPS method was shown to be accurate and efficient in estimating the local NPS of FBP-reconstructed 2D CT images. It presents strong advantages over traditional NPS methods when the number of scans is limited and can be extended to estimate the in-plane NPS of cone-beam CT and multislice helical CT scans.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

4D-CT scans reveal reduced magnitude of respiratory liver motion achieved by different abdominal compression plate positions in patients with intrahepatic tumors undergoing helical tomotherapy

Purpose: While abdominal compression (AC) can be used to reduce respiratory liver motion in patients receiving helical tomotherapy for hepatocellular carcinoma, the nature and extent of this effect is not well described. The purpose of this study was to evaluate the changes in magnitude of three-dimensional liver motion with abdominal compression using four-dimensional (4D) computed tomography (CT) images of several plate positions. Methods: From January 2012 to October 2015, 72 patients with intrahepatic carcinoma and divided into four groups underwent 4D-CT scans to assess respiratory liver motion. Of the 72 patients, 19 underwent abdominal compression of the cephalic area between the subxiphoid and umbilicus (group A), 16 underwent abdominal compression of the caudal region between the subxiphoid area and the umbilicus (group B), 11 patients underwent abdominal compression of the caudal umbilicus (group C), and 26 patients remained free breathing (group D). 4D-CT images were sorted into ten-image series, according to the respiratory phase from the end inspiration to the end expiration, and then transferred to treatment planning software. All liver contours were drawn by a single physician and confirmed by a second physician. Liver relative coordinates were automatically generated to calculate the liver respiratory motion in different axial directions to compile the 10 ten contours into a single composite image. Differences in respiratory liver motion were assessed with a one-way analysis of variance test of significance. Results: The average respiratory liver motion in the Y axial direction was 4.53 ± 1.16, 7.56 ± 1.30, 9.95 ± 2.32, and 9.53 ± 2.62 mm in groups A, B, C, and D, respectively, with a significant change among the four groups (p < 0.001). Abdominal compression was most effective in group A (compression plate on the subxiphoid area), with liver displacement being 2.53 ± 0.93, 4.53 ± 1.16, and 2.14 ± 0.92 mm on the X-, Y-, and Z-axes, respectively. There was no significant difference in respiratory liver motion between group C (displacement: 3.23 ± 1.47, 9.95 ± 2.32, and 2.92 ± 1.10 mm on the X-, Y-, and Z-axes, respectively) and group D (displacement: 3.35 ± 1.55, 9.53 ± 2.62, and 3.35 ± 1.73 mm on the X-, Y-, and Z-axes, respectively). Abdominal compression was least effective in group C (compression on caudal umbilicus), with liver motion in this group similar to that of free-breathing patients (group D). Conclusions: 4D-CT scans revealed significant liver motion control via abdominal compression of the subxiphoid area; however, this control of liver motion was not observed with compression of the caudal umbilicus. The authors, therefore, recommend compression of the subxiphoid area in patients undergoing external radiotherapy for intrahepatic carcinoma.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-G-206-11: The Effect of Table Height On CTDIvol and SSDE in CT Scanning: A Phantom Study

Purpose: Localizer projection radiographs acquired prior to CT scans are used to estimate patient size, affecting the function of Automatic Tube Current Modulation (ATCM) and calculation of the Size Specific Dose Estimate (SSDE). Due to geometric effects, the projected patient size varies with scanner table height and with the orientation of the localizer (AP versus PA). Consequently, variations in scanner table height may affect both CTDIvol and the calculated size-corrected dose index (SSDE). This study sought to characterize these effects. Methods: An anthropomorphic phantom was imaged using an AP localizer, followed by a diagnostic scan using ATCM and our institution’s routine abdomen protocol. This was repeated at various scanner table heights, recording the scanner-reported CTDIvol for each diagnostic scan. The width of the phantom was measured from the localizer and diagnostic images using in-house software. The measured phantom width and scanner-reported CTDIvol were used to calculate SSDE. This was repeated using PA localizers followed by diagnostic scans. Results: 1) The localizer-based phantom width varied by up to 54% of the nominal phantom width between minimum and maximum table heights. 2) Changing the table height caused a variation in scanner-reported CTDIvol of a factor greater than 4.6 when using a PA localizer and almost 2 when using an AP localizer. 3) SSDE, calculated from measured phantom size and scanner-reported CTDIvol, varied by a factor of more than 2.8 when using a PA localizer and almost 1.5 when using an AP localizer. Conclusion: Our study demonstrates that off-center patient positioning affects the efficacy of ATCM, more severely when localizers are acquired in the PA rather than AP projection. Further, patient positioning errors can cause a large variation in the calculated SSDE. This hinders interpretation of SSDE for individual patients and aggregate SSDE data when evaluating CT protocols and clinical practices.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-I-37: How Fat Distribution and Table Height Affect Estimates of Patient Size in CT Scanning: A Phantom Study

Purpose: Localizer projection radiographs acquired prior to CT scans are used to estimate patient size, affecting the function of Automatic Tube Current Modulation (ATCM) and hence CTDIvol and SSDE. Due to geometric effects, the projected patient size varies with scanner table height and with the orientation of the localizer (AP versus PA). This study sought to determine if patient size estimates made from localizer scans is affected by variations in fat distribution, specifically when the widest part of the patient is not at the geometric center of the patient. Methods: Lipid gel bolus material was wrapped around an anthropomorphic phantom to simulate two different body mass distributions. The first represented a patient with fairly rigid fat and had a generally oval shape. The second was bell-shaped, representing corpulent patients more susceptible to gravity’s lustful tug. Each phantom configuration was imaged using an AP localizer and then a PA localizer. This was repeated at various scanner table heights. The width of the phantom was measured from the localizer and diagnostic images using in-house software. Results: 1) The projected phantom width varied up to 39% as table height changed.2) At some table heights, the width of the phantom, designed to represent larger patients, exceeded the localizer field of view, resulting in an underestimation of the phantom width.3) The oval-shaped phantom approached a normalized phantom width of 1 at a table height several centimeters lower (AP localizer) or higher (PA localizer) than did the bell-shaped phantom. Conclusion: Accurate estimation of patient size from localizer scans is dependent on patient positioning with respect to scanner isocenter and is limited in large patients. Further, patient size is more accurately measured on projection images if the widest part of the patient, rather than the geometric center of the patient, is positioned at scanner isocenter.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Patient dose estimation from CT scans at the Mexican National Neurology and Neurosurgery Institute

In the radiology department of the Mexican National Institute of Neurology and Neurosurgery, a dedicated institute in Mexico City, on average 19.3 computed tomography (CT) examinations are performed daily on hospitalized patients for neurological disease diagnosis, control scans and follow-up imaging. The purpose of this work was to estimate the effective dose received by hospitalized patients who underwent a diagnostic CT scan using typical effective dose values for all CT types and to obtain the estimated effective dose distributions received by surgical and non-surgical patients. Effective patient doses were estimated from values per study type reported in the applications guide provided by the scanner manufacturer. This retrospective study included all hospitalized patients who underwent a diagnostic CT scan between 1 January 2011 and 31 December 2012. A total of 8777 CT scans were performed in this two-year period. Simple brain scan was the CT type performed the most (74.3%) followed by contrasted brain scan (6.1%) and head angiotomography (5.7%). The average number of CT scans per patient was 2.83; the average effective dose per patient was 7.9 mSv; the mean estimated radiation dose was significantly higher for surgical (9.1 mSv) than non-surgical patients (6.0 mSv). Three percent of the patients had 10 or more brain CT scans and exceeded the organ radiation dose threshold set by the International Commission on Radiological Protection for deterministic effects of the eye-lens. Although radiation patient doses from CT scans were in general relatively low, 187 patients received a high effective dose (>20 mSv) and 3% might develop cataract from cumulative doses to the eye lens.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

SU-E-J-204: Can a Commercial System for MR-IGRT Be Used to Treat Patients Without Acquiring a CT Scan?

Patients treated using a magnetic-resonance image guided radiation therapy (MR-IGRT) system received both CT and MR simulations. During planning, the CT is used to determine relative electron density (RED) using a calibration table. This study aims to investigate the feasibility of MR-only treatments by comparing CT-computed dose distributions to those computed with combinations of water (1.0), lung (0.26), tissue (1.02), and bone (1.12) bulk RED overrides, and to identify the effects of the magnetic field on the RED-overridden doses. Methods: Four patients who received treatment using a commercial MR-IGRT system were analyzed (1 lung, 2 abdomen, and 1 pelvis). The clinical plans were computed using the first fraction MRI as primary, and the simulation CT as secondary for REDs. Plans were reoptimized using default bulk RED overrides (water/lung and tissue/lung for the lung patient, water/bone, tissue/bone, water only, and tissue only for the abdomen and pelvis patients). Additionally, each plan was re-optimized to include the static magnetic field. All plans were normalized to the same PTV coverage as the clinical plan. Dose-difference volumes and DVHs were computed for bulk density override plans, and 3D gamma analyses between each plan and its accompanying magnetic field plan were performed using 3%/3 mm dose difference and distance-to-agreement criteria using the PTV and Skin as masking structures. Results: The average differences in PTV and organs-at-risk mean dose for all RED combinations tested were −0.19 Gy (−0.62 – 0.06 Gy) and −0.34 Gy (−1.76 – 0.33 Gy), respectively. The average PTV and Skin gamma pass rates for all RED combinations tested were 99.88% (99.5% – 100%) and 98. 35% (96.3% – 99.6%). No systematic differences in DVHs or isodoses were observed. Conclusions: It is likely that that a commercial MR-IGRT system may produce high quality treatment plans without the need for CT scans. Authors of this abstract are members of the Washington University Radiation Oncology department, which has a research agreement with ViewRay, Inc.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Secondary porosity prediction in complex carbonate reefs using 3D CT scan image analysis and machine learning

Development and preservation of carbonate porosity and permeability are critical to characterizing reservoirs. However, secondary porosity, such as vugs and fractures, are difficult to identify and require collection of expensive wireline tools or core sampling. Wireline logs and cores have traditionally been used to identify the presence of secondary porosity but fail to quantify the contribution to total reservoir porosity. Additionally, advanced wireline logs and core are not readily available for most wells. Dual energy CT scans were collected on whole core from the A-1 Carbonate and Brown Niagaran formations drawn from six wells in northern Michigan. 3D analysis techniques were applied to identify and isolate secondary porosity features. A series of machine learning and data analytics techniques were applied to the dataset to predict secondary porosity features on basic wireline logs. The predictive model successfully predicted secondary porosity with high confidence.

02 PETROLEUM↗
DOE OSTI2015

A “loop” shape descriptor and its application to automated segmentation of airways from CT scans

Purpose: A novel shape descriptor is presented to aid an automated identification of the airways depicted on computed tomography (CT) images. Methods: Instead of simplifying the tubular characteristic of the airways as an ideal mathematical cylindrical or circular shape, the proposed “loop” shape descriptor exploits the fact that the cross sections of any tubular structure (regardless of its regularity) always appear as a loop. In implementation, the authors first reconstruct the anatomical structures in volumetric CT as a three-dimensional surface model using the classical marching cubes algorithm. Then, the loop descriptor is applied to locate the airways with a concave loop cross section. To deal with the variation of the airway walls in density as depicted on CT images, a multiple threshold strategy is proposed. A publicly available chest CT database consisting of 20 CT scans, which was designed specifically for evaluating an airway segmentation algorithm, was used for quantitative performance assessment. Measures, including length, branch count, and generations, were computed under the aid of a skeletonization operation. Results: For the test dataset, the airway length ranged from 64.6 to 429.8 cm, the generation ranged from 7 to 11, and the branch number ranged from 48 to 312. These results were comparable to the performance of the state-of-the-art algorithms validated on the same dataset. Conclusions: The authors’ quantitative experiment demonstrated the feasibility and reliability of the developed shape descriptor in identifying lung airways.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

In vitro dose measurements in a human cadaver with abdomen/pelvis CT scans

Purpose: To present a study of radiation dose measurements with a human cadaver scanned on a clinical CT scanner. Methods: Multiple point dose measurements were obtained with high-accuracy Thimble ionization chambers placed inside the stomach, liver, paravertebral gutter, ascending colon, left kidney, and urinary bladder of a human cadaver (183 cm in height and 67.5 kg in weight) whose abdomen/pelvis region was scanned repeatedly with a multidetector row CT. The flat energy response and precision of the dosimeters were verified, and the slight differences in each dosimeter's response were evaluated and corrected to attain high accuracy. In addition, skin doses were measured for radiosensitive organs outside the scanned region with OSL dosimeters: the right eye, thyroid, both nipples, and the right testicle. Three scan protocols were used, which shared most scan parameters but had different kVp and mA settings: 120-kVp automA, 120-kVp 300 mA, and 100-kVp 300 mA. For each protocol three repeated scans were performed. Results: The tube starting angle (TSA) was found to randomly vary around two major conditions, which caused large fluctuations in the repeated point dose measurements: for the 120-kVp 300 mA protocol this angle changed from approximately 110° to 290°, and caused 8% − 25% difference in the point dose measured at the stomach, liver, colon, and urinary bladder. When the fluctuations of the TSA were small (within 5°), the maximum coefficient of variance was approximately 3.3%. The soft tissue absorbed doses averaged from four locations near the center of the scanned region were 27.2 ± 3.3 and 16.5 ± 2.7 mGy for the 120 and 100-kVp fixed-mA scans, respectively. These values were consistent with the corresponding size specific dose estimates within 4%. The comparison of the per-100-mAs tissue doses from the three protocols revealed that: (1) dose levels at nonsuperficial locations in the TCM scans could not be accurately deduced by simply scaling the fix-mA doses with local mA values; (2) the general power law relationship between dose and kVp varied from location to location, with the power index ranged between 2.7 and 3.5. The averaged dose measurements at both nipples, which were about 0.6 cm outside the prescribed scan region, ranged from 23 to 27 mGy at the left nipple, and varied from 3 to 20 mGy at the right nipple over the three scan protocols. Large fluctuations over repeated scans were also observed, as a combined result of helical scans of large pitch (1.375) and small active areas of the skin dosimeters. In addition, the averaged skin dose fell off drastically with the distance to the nearest boundary of the scanned region. Conclusions: This study revealed the complexity of CT dose fluctuation and variation with a human cadaver.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-F-207-03: Dosimetric Effect of the Position of Arms in Torso CT Scan with Tube Current Modulation

Purpose: To evaluate the patient organ dose differences between the arms-raised and arms-lowered postures in Torso multidetector computed tomography (MDCT) scan protocols with tube current modulation (TCM). Methods: Patient CT organ doses were simulated using the Monte Carlo method with human phantoms and a validated CT scanner model. A set of adult human phantoms with arms raised and arms lowered postures were developed using advanced BREP-based mesh surface geometries. Organ doses from routine Torso scan protocols such as chest, abdomen-pelvis, and CAP scans were simulated. The organ doses differences caused by two different posutres were investigated when tube current modulation (TCM) were applied during the CT scan. Results: With TCM applied, organ doses of all the listed organs of arms-lowered posture phantom are larger than those of arms raised phantom. The dose difference for most of the organs or tissues are larger than 50%, and the skin doses difference for abdomen-pelvis scan even reaches 112.03%. This is due to the fact that the tube current for patient with arms-lowered is much higher than for the arms raised posture. Conclusion: Considering CT scan with TCM, which is commonly applied clinically, patients who could not raise their arms will receive higher radiation dose than the arms raised patient, with dose differences for some tissues such as the skin being larger than 100%. This is due to the additional tube current necessary to penetrate the arms while maintaining consistent image quality. National Nature Science Foundation of China(No.11475047)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-G-206-06: Analytic Dose Function for CT Scans in Infinite Cylinders as a Function of Scan Length and Cylinder Radius

Purpose: The radiation dose absorbed at a particular radius ρ within the central plane of a long cylinder following a CT scan is a function of the length of the scan L and the cylinder radius R along with kVp and cylinder composition. An analytic function was created that that not only expresses these dependencies but is integrable in closed form over the area of the central plane. This feature facilitates explicit calculation of the planar average dose. The “approach to equilibrium” h(L) discussed in the TG111 report is seamlessly included in this function. Methods: For a cylindrically symmetric radiation field, Monte Carlo calculations were performed to compute the dose distribution to long polyethylene cylinders for scans of varying L for cylinders ranging in radius from 5 to 20 cm. The function was developed from the resultant Monte Carlo data. In addition, the function was successfully fit to data taken from measurements on the 30 cm diameter ICRU/TG200 phantom using a real-time dosimeter. Results: Symmetry and continuity dictate a local extremum at the center which is a minimum for the larger sizes. There are competing effects as the beam penetrates the cylinder from the outside: attenuation, resulting in a decrease; scatter, abruptly increasing at the circumference. This competition may result in an absolute maximum between the center and outer edge leading to a “gull wing” shape for the radial dependence. For the smallest cylinders, scatter may dominate to the extent that there is an absolute maximum at the center. Conclusion: An integrable, analytic function has been developed that provides the radial dependency of dose for the central plane of a scan of length L for cylinders of varying diameter. Equivalently, we have developed h(L,R,ρ).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Automated Segmentation of Porous Thermal Spray Material CT Scans with Geometric Uncertainty Estimation

Thermal sprayed metal coatings are used in many industrial applications, and characterizing the structure and performance of these materials is vital to understanding their behavior in the field. X-ray Computed Tomography (CT) machines enable volumetric, nondestructive imaging of these materials, but precise segmentation of this grayscale image data into discrete material phases is necessary to calculate quantities of interest related to material structure. In this work, we present a methodology to automate the CT segmentation process as well as quantify uncertainty in segmentations via deep learning. Neural networks (NNs) are shown to accurately segment full resolution CT scans of thermal sprayed materials and provide maps of uncertainty that conservatively bound the predicted geometry. These bounds are propagated through calculations of material properties such as porosity that may provide an understanding of anticipated behavior in the field.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Automated segmentation of porous thermal spray material CT scans with predictive uncertainty estimation

Abstract Thermal sprayed metal coatings are used in many industrial applications, and characterizing the structure and performance of these materials is vital to understanding their behavior in the field. X-ray computed tomography (CT) enables volumetric, nondestructive imaging of these materials, but precise segmentation of this grayscale image data into discrete material phases is necessary to calculate quantities of interest related to material structure. In this work, we present a methodology to automate the CT segmentation process as well as quantify uncertainty in segmentations via deep learning. Neural networks (NNs) have been shown to excel at segmentation tasks; however, memory constraints, class imbalance, and lack of sufficient training data often prohibit their deployment in high resolution volumetric domains. Our 3D convolutional NN implementation mitigates these challenges and accurately segments full resolution CT scans of thermal sprayed materials with maps of uncertainty that conservatively bound the predicted geometry. These bounds are propagated through calculations of material properties such as porosity that may provide an understanding of anticipated behavior in the field.

Martinez, Carianne↗
NASA NTRSDate not supplied

Enhanced Quantification of 3D Woven Composites Via Fourier Analysis and Structure Tensors Applied to CT Scans

NASA's development of advanced 3D woven carbon composites, such as HEEET and 3MDCP, represents a significant leap in materials designed to endure extreme atmospheric entry conditions. However, the internal complexity of these materials introduces a variety of structures and novel defect types. Traditional characterization methods rely on labor-intensive, manual examination of X-ray CT scans, demanding considerable time and expertise. This process, while thorough, is not sustainable for large-scale analysis and defect tracking. Addressing this critical challenge, this presentation introduces an innovative suite of automated tools and analytical techniques, pioneered by the Mars Sample Return (MSR) Earth Entry System (EES) Thermal Protection System (TPS) project. These advancements offer a more efficient and accurate approach for quantifying CT data and characterizing the defects of 3D woven composites.

Magnus Haw↗