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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis

In the field of fluorescence microscopy, there is continued demand for dynamic technologies that can exploit the complete information from every pixel of an image. One imaging technique with proven ability for yielding additional information from fluorescence imaging is Fluorescence Lifetime Imaging Microscopy (FLIM). FLIM allows for the measurement of how long a fluorophore stays in an excited energy state, and this measurement is affected by changes in its chemical microenvironment, such as proximity to other fluorophores, pH, and hydrophobic regions. This ability to provide information about the microenvironment has made FLIM a powerful tool for cellular imaging studies ranging from metabolic measurement to measuring distances between proteins. The increased use of FLIM has necessitated the development of computational tools for integrating FLIM analysis with image and data processing. To address this need, we have created FLIMJ, an ImageJ plugin and toolkit that allows for easy use and development of extensible image analysis workflows with FLIM data. Built on the FLIMLib decay curve fitting library and the ImageJ Ops framework, FLIMJ offers FLIM fitting routines with seamless integration with many other ImageJ components, and the ability to be extended to create complex FLIM analysis workflows. Building on ImageJ Ops also enables FLIMJ’s routines to be used with Jupyter notebooks and integrate naturally with science-friendly programming in, e.g., Python and Groovy. We show the extensibility of FLIMJ in two analysis scenarios: lifetime-based image segmentation and image colocalization. We also validate the fitting routines by comparing them against industry FLIM analysis standards.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Spontaneous imbibition of water and determination of effective contact angles in the Eagle Ford Shale Formation using neutron imaging

Understanding of fundamental processes and prediction of optimal parameters during the horizontal drilling and hydraulic fracturing process results in economically effective improvement of oil and natural gas extraction. Although, the modern analytical and computational models can capture fracture growth, there is a lack of experimental data on spontaneous imbibition and wettability in oil and gas reservoirs for the validation of further model development. In this work, we used neutron imaging to measure the spontaneous imbibition of water into fractures of Eagle Ford Shale with known geometries and fracture orientations. An analytical solution for a set of nonlinear second-order differential equations was applied to the measured imbibition data to determine effective contact angles. The analytical solution fit the measured imbibition data reasonably well and determined effective contact angles were slightly higher than static contact angles due to effects of in-situ changes in velocity, surface roughness, and heterogeneity of mineral surfaces on the fracture surface. Additionally, small fracture widths may have retarded imbibition and affected model fits, which suggests that average fracture widths are not satisfactory for modeling imbibition in natural systems.

DiStefano, Victoria H. (ORCID:0000000160239716)↗
DOE OSTI2024

High-performance data management for whole slide image analysis in digital pathology

When dealing with giga-pixel digital pathology in whole-slide imaging, a notable proportion of data records holds relevance during each analysis operation. For instance, when deploying an image analysis algorithm on whole-slide images (WSI), the computational bottleneck often lies in the input-output (I/O) system. This is particularly notable as patch-level processing introduces a considerable I/O load onto the computer system. However, this data management process could be further paralleled, given the typical independence of patch-level image processes across different patches. This paper details our endeavors in tackling this data access challenge by implementing the Adaptable IO System version 2 (ADIOS2). Our focus has been constructing and releasing a digital pathology-centric pipeline using ADIOS2, which facilitates streamlined data management across WSIs. Additionally, we’ve developed strategies aimed at curtailing data retrieval times. The performance evaluation encompasses two key scenarios: (1) a pure CPU-based image analysis scenario (“CPU scenario”), and (2) a GPU-based deep learning framework scenario (“GPU scenario”). Our findings reveal noteworthy outcomes. Under the CPU scenario, ADIOS2 showcases an impressive two-fold speed-up compared to the brute-force approach. In the GPU scenario, its performance stands on par with the cutting-edge GPU I/O acceleration framework, NVIDIA Magnum IO GPU Direct Storage (GDS). From what we know, this appears to be among the initial instances, if any, of utilizing ADIOS2 within the field of digital pathology. The source code has been made publicly available at https://github.com/hrlblab/adios.

Wang, Xiao↗
DOE OSTI2020

Multimodal Imaging of Autofluorescent Sites Reveals Varied Chemical Speciation in SSZ-13 Crystals

Here, a multimodal imaging study of chabazite is used to show the distribution of and discriminate between different emissive deposits arising as a result of the detemplation process. 3D confocal imaging, fluorescence lifetime imaging, spectral imaging, and Raman mapping are used to show three different types of emissive behaviours each characterised by different spatial distributions, trends in lifetime, spectral signals, and Raman signatures. A notable difference is seen in the morphology of agglomerated surface deposits and larger subsurface deposits, which experience lifetime augmentation due to spatial confinement. The distribution of organic residue throughout the crystal volume is comparable to XRF mapping that shows Si enrichment on the outer edges and higher Al content through the centre, demonstrating that a fluorescence-based technique can also be used to indirectly comment on the compositional chemistry of the inorganic framework.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

INR-TEM: Robust cavity detection in multifocus TEM images via implicit neural representations

When characterizing materials using transmission electron microscopy (TEM) images, detecting and quantifying small features in microstructures, such as cavities, pose significant challenges. Off-the-shelf object detection models, including YOLOv8, show considerable performance degradation, particularly when images vary in resolution and the objects of interest possess a low percentage of the total image region of interest. In this study, we introduce a novel detection pipeline that incorporates an implicit neural representation (INR)-based detection method, INR-TEM, and two-modality imaging (e.g., under-focused and over-focused images typically acquired during materials characterization) to improve object detection performance. The INR-TEM method incorporates a pixel-wise prediction principle inspired by pixel-wise centerness weighting. INR-TEM demonstrates superior robustness to resolution variability, maintaining high detection accuracy even at low image resolutions compared to YOLOv8. To leverage INR-TEM effectively in real-world two-modality characterization applications, we further integrate a two-stage motion correction pipeline designed explicitly for aligning multifocus TEM images. The alignment process, comprising keypoint (based on scale-invariant feature transform, SIFT) and intensity matching, significantly mitigates the adverse effects of perceived motion-induced image degradation during through-focal TEM imaging, directly enhancing INR-TEM’s detection capability over conventional single-focus images. Our integrated INR-TEM cavity detection framework notably improves performance across various cavity sizes, outperforming off-the-shelf YOLOv8 detections that rely on a single image modality.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Direct evidence of the dominant role of multiphoton permanent-dipole transitions in strong-field dissociation of NO 2 +

We study laser-induced dissociation of a metastable NO 2+ ion-beam target into N + +O + , focusing on the prominent contribution by molecules breaking parallel to the polarization at high peak laser intensity (~10 15 W/cm 2 ). Our experimental results and time-dependent Schrödinger equation calculations show that, contrary to commonly held intuition that electronic transitions always prevail, the dominant process underlying this highly aligned dissociation is a multiphoton permanent-dipole transition involving only the electronic ground state and leading to its vibrational continuum. Strong-field permanent-dipole transitions should thus be considered generally, as they may play a significant role in other heteronuclear molecules. Moreover, their role should only grow in importance for longer wavelengths, a trending direction in ultrafast laser studies.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2015

A multimodality segmentation framework for automatic target delineation in head and neck radiotherapy

Purpose: To develop an automatic segmentation algorithm integrating imaging information from computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) to delineate target volume in head and neck cancer radiotherapy. Methods: Eleven patients with unresectable disease at the tonsil or base of tongue who underwent MRI, CT, and PET/CT within two months before the start of radiotherapy or chemoradiotherapy were recruited for the study. For each patient, PET/CT and T1-weighted contrast MRI scans were first registered to the planning CT using deformable and rigid registration, respectively, to resample the PET and magnetic resonance (MR) images to the planning CT space. A binary mask was manually defined to identify the tumor area. The resampled PET and MR images, the planning CT image, and the binary mask were fed into the automatic segmentation algorithm for target delineation. The algorithm was based on a multichannel Gaussian mixture model and solved using an expectation–maximization algorithm with Markov random fields. To evaluate the algorithm, we compared the multichannel autosegmentation with an autosegmentation method using only PET images. The physician-defined gross tumor volume (GTV) was used as the “ground truth” for quantitative evaluation. Results: The median multichannel segmented GTV of the primary tumor was 15.7 cm{sup 3} (range, 6.6–44.3 cm{sup 3}), while the PET segmented GTV was 10.2 cm{sup 3} (range, 2.8–45.1 cm{sup 3}). The median physician-defined GTV was 22.1 cm{sup 3} (range, 4.2–38.4 cm{sup 3}). The median difference between the multichannel segmented and physician-defined GTVs was −10.7%, not showing a statistically significant difference (p-value = 0.43). However, the median difference between the PET segmented and physician-defined GTVs was −19.2%, showing a statistically significant difference (p-value =0.0037). The median Dice similarity coefficient between the multichannel segmented and physician-defined GTVs was 0.75 (range, 0.55–0.84), and the median sensitivity and positive predictive value between them were 0.76 and 0.81, respectively. Conclusions: The authors developed an automated multimodality segmentation algorithm for tumor volume delineation and validated this algorithm for head and neck cancer radiotherapy. The multichannel segmented GTV agreed well with the physician-defined GTV. The authors expect that their algorithm will improve the accuracy and consistency in target definition for radiotherapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-H-BRA-01: The Physics of High Power Radiofrequency Isolation in a Novel Compact Linear Accelerator Based MRI Guided Radiation Therapy System

Purpose: To develop a method for isolating the radiofrequency waves emanating from linear accelerator components from the magnetic resonance imaging (MRI) system of an integrated MRI-linac. Methods: An MRI-guided radiation therapy system has been designed that integrates a linear accelerator with simultaneous MR imaging. The radiofrequency waves created by the accelerating process would degrade MR image quality, so a method for containing the radiofrequency waves and isolating the MR imager from them was developed. The linear accelerator radiofrequency modulator was placed outside the room, so a filter was designed to eliminate the radiofrequency corresponding to the proton Larmour frequency of 14.7 MHz. Placing the radiofrequency emitting components in a typical Faraday cage would have reduced the radiofrequency emissions, but the design would be susceptible to small gaps in the shield due to the efficiency of the Faraday cage reflecting internal radiofrequency emissions. To reduce internal radiofrequency reflections, the Faraday cage was lined with carbon fiber sheets. Carbon fiber has the property of attenuating the radiofrequency energy so that the overall radiofrequency field inside the Faraday cage is reduced, decreasing any radiofrequency energy emitted from small gaps in the cage walls. Results: Within a 1.2 MHz band centered on the Larmor frequency, the radiofrequency (RF) leakage from the Faraday cage was measured to be −90 dB with no RF on, −40 dB with the RF on and no shield, returning to −90 dB with the RF on and shields in place. The radiofrequency filter attenuated the linear accelerator modulator emissions in the 14.7 MHz band by 70 dB. Conclusions: One of the major challenges in designing a compact linear accelerator based MRI-guided radiation therapy system, that of isolating the high power RF system from the MRI, has been solved. The measured radiofrequency emissions are sufficiently small to enable system integration. This research was funded by ViewRay, Inc., Oakwood, OH.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Conditional Latent Diffusion for High-Resolution Prediction of Electrochemical Surface Morphology

A conditionally guided generative latent diffusion process that is trained on a set of experimental processing parameters and their associated resulting electron microscope images of the electrodeposition process is able to interpolate between processing parameters in a physically consistent way. Electrodeposition of rhenium with pulse and pulse-reverse waveforms is used as a model system, and the process is adaptable to other electrodeposition, electropolishing, or corrosion processes. The method is able to extrapolate, predicting estimates of material morphologies for experimental setups unseen in the training data. The results are demonstrated with experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Quantifying Moss Response to Metal Contaminant Exposure Using Laser-Induced Fluorescence

Tracing sources of contamination, including potentially toxic elements (PTEs), has historically been achieved through sampling and analysis of soil or biota, which are labor-intensive, costly, and destructive methods. Thus, availability of a non-destructive in situ remote sensing method for monitoring metals deposited in biota is of great interest. Laser-induced fluorescence (LIF) is an emerging spectroscopic and imaging technique that documents changes in molecular energy level in plants as a biological response to metal contamination. For a proof-of-concept study and preliminary experiment, moss was selected for experimentation due to its long history of use in tracing atmospheric deposition of PTEs. Consecutive treatments of copper chloride (CuCl2) were administered to three moss samples, simulating wet deposition every 48 h over 10 days until reaching cumulative Cu concentrations of 2.690 to 8.075 μmol/cm2. While these Cu amounts are above environmentally relevant concentrations, they allowed the best conditions for testing and fine tuning of the imaging and data processing protocols presented in this paper. Moss fluorescence was induced using both 532 nm green and 355 nm UV lasers. A CMOS camera captured images of the LIF response, and red–green–blue (RGB) decimal code values were extracted for each pixel in the images, and pixel densities of color channels from treated and untreated moss samples were compared. Results show a shift towards lower color decimal codes corresponding to increased Cu concentration. We developed and contrasted multiple quantitative analyses of color distributions and demonstrated that LIF shows great promise for remote sensing of Cu accumulation in moss at μmol/cm2 levels. Though currently, the method would be limited to highly toxic sites, it illustrates the possibility and provides a framework for development of higher-sensitivity methods to detect nmol/cm2 that are viable for urban contamination level monitoring.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2016

SU-D-206-03: Segmentation Assisted Fast Iterative Reconstruction Method for Cone-Beam CT

Purpose: Total Variation (TV) based iterative reconstruction (IR) methods enable accurate CT image reconstruction from low-dose measurements with sparse projection acquisition, due to the sparsifiable feature of most CT images using gradient operator. However, conventional solutions require large amount of iterations to generate a decent reconstructed image. One major reason is that the expected piecewise constant property is not taken into consideration at the optimization starting point. In this work, we propose an iterative reconstruction method for cone-beam CT (CBCT) using image segmentation to guide the optimization path more efficiently on the regularization term at the beginning of the optimization trajectory. Methods: Our method applies general knowledge that one tissue component in the CT image contains relatively uniform distribution of CT number. This general knowledge is incorporated into the proposed reconstruction using image segmentation technique to generate the piecewise constant template on the first-pass low-quality CT image reconstructed using analytical algorithm. The template image is applied as an initial value into the optimization process. Results: The proposed method is evaluated on the Shepp-Logan phantom of low and high noise levels, and a head patient. The number of iterations is reduced by overall 40%. Moreover, our proposed method tends to generate a smoother reconstructed image with the same TV value. Conclusion: We propose a computationally efficient iterative reconstruction method for CBCT imaging. Our method achieves a better optimization trajectory and a faster convergence behavior. It does not rely on prior information and can be readily incorporated into existing iterative reconstruction framework. Our method is thus practical and attractive as a general solution to CBCT iterative reconstruction. This work is supported by the Zhejiang Provincial Natural Science Foundation of China (Grant No. LR16F010001), National High-tech R&D Program for Young Scientists by the Ministry of Science and Technology of China (Grant No. 2015AA020917).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Analysis of contour images using optics of spiral beams

An approach is outlined to the recognition of contour images using computer technology based on coherent optics principles. A mathematical description of the recognition process algorithm and the results of numerical modelling are presented. The developed approach to the recognition of contour images using optics of spiral beams is described and justified. (image recognition)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Proof of principle study: synchrotron X-ray fluorescence microscopy for identification of previously radioactive microparticles and elemental mapping of FFPE tissues

Biobanks containing formalin-fixed, paraffin-embedded (FFPE) tissues from animals and human atomic-bomb survivors exposed to radioactive particulates remain a vital resource for understanding the molecular effects of radiation exposure. These samples are often decades old and prepared using harsh fixation processes which limit sample imaging options. Optical imaging of hematoxylin and eosin (H&E) stained tissues may be the only feasible processing option, however, H&E images provide no information about radioactive microparticles or radioactive history. Synchrotron X-ray fluorescence microscopy (XFM) is a robust, non-destructive, semi-quantitative technique for elemental mapping and identifying candidate chemical element biomarkers in FFPE tissues. Still, XFM has never been used to uncover distribution of formerly radioactive micro-particulates in FFPE canine specimens collected more than 30 years ago. In this work, we demonstrate the first use of low-, medium-, and high-resolution XFM to generate 2D elemental maps of ~ 35-year-old, canine FFPE lung and lymph node specimens stored in the Northwestern University Radiobiology Archive documenting distribution of formerly radioactive micro-particulates. Additionally, we use XFM to identify individual microparticles and detect daughter products of radioactive decay. The results of this proof-of-principle study support the use of XFM to map chemical element composition in historic FFPE specimens and conduct radioactive micro-particulate forensics.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

SU-E-P-12: Pinnacle-Based Tool to Evaluate the Effect of Prostate Rotation as Determined by Calypso

Purpose: When the Calypso reports a large prostate rotation and one does not have the means to make corrections, it is helpful to quantify its dosimetric effects. We have devised a simple scheme to achieve this in Pinnacle TPS. If it is determined that the PTV margin is inadequate, an alternate larger-margin plan may be adapted for the day. Methods: A three-step process using the Pinnacle Image fusion module was formulated to analyze the clinical effect of prostate rotation. The process includes (1) translating the image set such that the rotational axis is about the isocenter, (2) performing 3D rotations of the structures of interest, and (3) restoring the coordinates associated with the plan. The rotated structures are imported and overlayed on the original plan for evaluation. The tool was applied to three prostate cancer patients with relatively large rotations (95 fractions total). Prostate rotations are primarily due to variations in bladder and rectal filling, which may also affect the seminal vesicles. It is difficult to estimate the perturbation of seminal vesicles for a given prostate rotation. Therefore, the Calypso-derived rotation matrix was applied only to CTV and PTV (CTV+5mm). Results: The rotations were predominantly in the pitch direction with an absolute value ranging from 0–29 degrees (Median = 10°, Average = 14.5°). For various magnitudes of rotation, the CTV V100 decreased from the original 100% to 99.6%/99%/95% for 10°/15°/29° rotations, respectively. Likewise, the PTV V100 diminished from 95% to 92%/89%/82%. Conclusion: A tool, consisting only of features within a TPS, was used to evaluate the dosimetric effect of prostate rotation. Our study shows that under large uncorrected rotations, the tumor coverage is degraded and may require intervention in the form of plan adaptation. To this end, alternate plans with variable margins can be generated in advance for daily adaption.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

RadPro: Radiography Processing Tool

Presentation describes project to improve processing of Cygnus radiographs by simplifying data processing and modifying options/parameters, displaying and saving images in desired format and path, and improving performance of basic processing methods, to include de-star, subtract, normalize, rotate, tile boundaries, and de-warp.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

SU-F-J-157: Effect of Contouring Uncertainty in Post Implant Dosimetry of Low-Dose-Rate Prostate Permanent Seed Brachytherapy

Purpose: There is strong evidence relating post-implant dosimetry for low-dose-rate (LDR) prostate seed brachytherapy to local control rates. The delineation of the prostate on CT images, however, represents a challenge due to the lack of soft tissue contrast in order to identify the prostate borders. This study aims at quantifying the sensitivity of clinically relevant dosimetric parameters to uncertainty in the contouring of prostate. Methods: CT images, post-op plans and contours of a cohort of patients (n=43) (low risk=55.8%, intermediate risk=39.5%, high risk=4.7%), who had received prostate seed brachytherapy, were imported into MIM Symphony treatment planning system. The prostate contours in post-implant CT images were expanded/contracted uniformly for margins of ±1.00 mm, ±2.00 mm, ±3.00 mm, ±4.00 mm and ±5.00 mm. The values for V100 and D90 were extracted from Dose Volume Histograms for each contour and compared. Results: Significant changes were observed in the values of D90 and V100 as well as the number of suboptimal plans for expansion or contraction margins of only few millimeters. Evaluation of coverage based on D90 was found to be less sensitive to expansion errors compared to V100. D90 led to a lower number of implants incorrectly identified with insufficient coverage for expanded contours which increases the accuracy of post-implant QA using CT images compared to V100. Conclusion: In order to establish a successful post implant QA for LDR prostate seed brachytherapy, it is necessary to identify the low and high thresholds of important dose metrics of the target volume such as D90 and V100. Since these parameters are sensitive to target volume definition, accurate identification of prostate borders would help to improve accuracy and predictive value of the post-implant QA process. In this respect, use of imaging modalities such as MRI where prostate is well delineated should prove useful.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

TEMImageNet training library and AtomSegNet deep-learning models for high-precision atom segmentation, localization, denoising, and deblurring of atomic-resolution images

Abstract Atom segmentation and localization, noise reduction and deblurring of atomic-resolution scanning transmission electron microscopy (STEM) images with high precision and robustness is a challenging task. Although several conventional algorithms, such has thresholding, edge detection and clustering, can achieve reasonable performance in some predefined sceneries, they tend to fail when interferences from the background are strong and unpredictable. Particularly, for atomic-resolution STEM images, so far there is no well-established algorithm that is robust enough to segment or detect all atomic columns when there is large thickness variation in a recorded image. Herein, we report the development of a training library and a deep learning method that can perform robust and precise atom segmentation, localization, denoising, and super-resolution processing of experimental images. Despite using simulated images as training datasets, the deep-learning model can self-adapt to experimental STEM images and shows outstanding performance in atom detection and localization in challenging contrast conditions and the precision consistently outperforms the state-of-the-art two-dimensional Gaussian fit method. Taking a step further, we have deployed our deep-learning models to a desktop app with a graphical user interface and the app is free and open-source. We have also built a TEM ImageNet project website for easy browsing and downloading of the training data.

25 ENERGY STORAGE↗
DOE OSTI2021

An Artificial Intelligence-Assisted Method for Dementia Detection Using Images from the Clock Drawing Test

Background: Widespread dementia detection could increase clinical trial candidates and enable appropriate interventions. Since the Clock Drawing Test (CDT) can be potentially used for diagnosing dementia-related disorders, it can be leveraged to develop a computer-aided screening tool. Objective: To evaluate if a machine learning model that uses images from the CDT can predict mild cognitive impairment or dementia. Methods: Images of an analog clock drawn by 3,263 cognitively intact and 160 impaired subjects were collected during in-person dementia evaluations by the Framingham Heart Study. We processed the CDT images, participant’s age, and education level using a deep learning algorithm to predict dementia status. Results: When only the CDT images were used, the deep learning model predicted dementia status with an area under the receiver operating characteristic curve (AUC) of 81.3% ± 4.3%. A composite logistic regression model using age, level of education, and the predictions from the CDT-only model, yielded an average AUC and average F1 score of 91.9% ±1.1% and 94.6% ±0.4%, respectively. Conclusion: Our modeling framework establishes a proof-of-principle that deep learning can be applied on images derived from the CDT to predict dementia status. When fully validated, this approach can offer a cost-effective and easily deployable mechanism for detecting cognitive impairment.

Neurosciences & Neurology↗

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