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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

The potential of positron emission tomography for intratreatment dynamic lung tumor tracking: A phantom study

Purpose: This study aims to evaluate the potential and feasibility of positron emission tomography for dynamic lung tumor tracking during radiation treatment. The authors propose a center of mass (CoM) tumor tracking algorithm using gated-PET images combined with a respiratory monitor and investigate the geometric accuracy of the proposed algorithm. Methods: The proposed PET dynamic lung tumor tracking algorithm estimated the target position information through the CoM of the segmented target volume on gated PET images reconstructed from accumulated coincidence events. The information was continuously updated throughout a scan based on the assumption that real-time processing was supported (actual processing time at each frame ≈10 s). External respiratory motion and list-mode PET data were acquired from a phantom programmed to move with measured respiratory traces (external respiratory motion and internal target motion) from human subjects, for which the ground truth target position was known as a function of time. The phantom was cylindrical with six hollow sphere targets (10, 13, 17, 22, 28, and 37 mm in diameter). The measured respiratory traces consisted of two sets: (1) 1D-measured motion from ten healthy volunteers and (2) 3D-measured motion from four lung cancer patients. The authors evaluated the geometric accuracy of the proposed algorithm by quantifying estimation errors (Euclidean distance) between the actual motion of targets (1D-motion and 3D-motion traces) and CoM trajectories estimated by the proposed algorithm as a function of time. Results: The time-averaged error of 1D-motion traces over all trajectories of all targets was 1.6 mm. The error trajectories decreased with time as coincidence events were accumulated. The overall error trajectory of 1D-motion traces converged to within 2 mm in approximately 90 s. As expected, more accurate results were obtained for larger targets. For example, for the 37 mm target, the average error over all 1D-motion traces was 1.1 mm; and for the 10 mm target, the average error over all 1D-motion traces was 2.8 mm. The overall time-averaged error of 3D-motion traces was 1.6 mm, which was comparable to that of the 1D-motion traces. There were small variations in the errors between the 3D-motion traces, although the motion trajectories were very different. The accuracy of the estimates was consistent for all targets except for the smallest. Conclusions: The authors developed an algorithm for dynamic lung tumor tracking using list-mode PET data and a respiratory motion signal, and demonstrated proof-of-principle for PET-guided lung tumor tracking. The overall tracking error in phantom studies is less than 2 mm.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2012

Methods for increasing the sensitivity of gamma-ray imagers

Methods are presented that increase the position resolution and granularity of double sided segmented semiconductor detectors. These methods increase the imaging resolution capability of such detectors, either used as Compton cameras, or as position sensitive radiation detectors in imagers such as SPECT, PET, coded apertures, multi-pinhole imagers, or other spatial or temporal modulated imagers.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2010

Automatic Feature Extraction from Planetary Images

With the launch of several planetary missions in the last decade, a large amount of planetary images has already been acquired and much more will be available for analysis in the coming years. The image data need to be analyzed, preferably by automatic processing techniques because of the huge amount of data. Although many automatic feature extraction methods have been proposed and utilized for Earth remote sensing images, these methods are not always applicable to planetary data that often present low contrast and uneven illumination characteristics. Different methods have already been presented for crater extraction from planetary images, but the detection of other types of planetary features has not been addressed yet. Here, we propose a new unsupervised method for the extraction of different features from the surface of the analyzed planet, based on the combination of several image processing techniques, including a watershed segmentation and the generalized Hough Transform. The method has many applications, among which image registration and can be applied to arbitrary planetary images.

Troglio, Giulia↗
NASA NTRS1980

Multispectral imaging of the lunar regolith core samples - Preliminary results for 74002

Multispectral images of a lunar core segment are a new form of continuous data along the length of the core. A new laboratory arrangement was developed to obtain these images. Data processing and initial data analysis for core section 74002 were performed. The images are sensitive to variations in mineralogy and/or maturity of the soil and are easily used (1) in stratigraphic studies of the lunar regolith, (2) for sample selection of representative material, and (3) as ground truth for remote sensing studies.

Pieters, C. M.↗
DOE OSTI2015

Four dimensional magnetic resonance imaging with retrospective k-space reordering: A feasibility study

Purpose: Current four dimensional magnetic resonance imaging (4D-MRI) techniques lack sufficient temporal/spatial resolution and consistent tumor contrast. To overcome these limitations, this study presents the development and initial evaluation of a new strategy for 4D-MRI which is based on retrospective k-space reordering. Methods: We simulated a k-space reordered 4D-MRI on a 4D digital extended cardiac-torso (XCAT) human phantom. A 2D echo planar imaging MRI sequence [frame rate (F) = 0.448 Hz; image resolution (R) = 256 × 256; number of k-space segments (N{sub KS}) = 4] with sequential image acquisition mode was assumed for the simulation. Image quality of the simulated “4D-MRI” acquired from the XCAT phantom was qualitatively evaluated, and tumor motion trajectories were compared to input signals. In particular, mean absolute amplitude differences (D) and cross correlation coefficients (CC) were calculated. Furthermore, to evaluate the data sufficient condition for the new 4D-MRI technique, a comprehensive simulation study was performed using 30 cancer patients’ respiratory profiles to study the relationships between data completeness (C{sub p}) and a number of impacting factors: the number of repeated scans (N{sub R}), number of slices (N{sub S}), number of respiratory phase bins (N{sub P}), N{sub KS}, F, R, and initial respiratory phase at image acquisition (P{sub 0}). As a proof-of-concept, we implemented the proposed k-space reordering 4D-MRI technique on a T2-weighted fast spin echo MR sequence and tested it on a healthy volunteer. Results: The simulated 4D-MRI acquired from the XCAT phantom matched closely to the original XCAT images. Tumor motion trajectories measured from the simulated 4D-MRI matched well with input signals (D = 0.83 and 0.83 mm, and CC = 0.998 and 0.992 in superior–inferior and anterior–posterior directions, respectively). The relationship between C{sub p} and N{sub R} was found best represented by an exponential function (C{sub P}=100(1−e{sup −0.18N{sub R}}), when N{sub S} = 30, N{sub P} = 6). At a C{sub P} value of 95%, the relative error in tumor volume was 0.66%, indicating that N{sub R} at a C{sub P} value of 95% (N{sub R,95%}) is sufficient. It was found that N{sub R,95%} is approximately linearly proportional to N{sub P} (r = 0.99), and nearly independent of all other factors. The 4D-MRI images of the healthy volunteer clearly demonstrated respiratory motion in the diaphragm region with minimal motion induced noise or aliasing. Conclusions: It is feasible to generate respiratory correlated 4D-MRI by retrospectively reordering k-space based on respiratory phase. This new technology may lead to the next generation 4D-MRI with high spatiotemporal resolution and optimal tumor contrast, holding great promises to improve the motion management in radiotherapy of mobile cancers.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1999

Effect of color coding and subtraction on the accuracy of contrast echocardiography

BACKGROUND: Contrast echocardiography may be used to assess myocardial perfusion. However, gray scale assessment of myocardial contrast echocardiography (MCE) is difficult because of variations in regional backscatter intensity, difficulties in distinguishing varying shades of gray, and artifacts or attenuation. We sought to determine whether the assessment of rest myocardial perfusion by MCE could be improved with subtraction and color coding. METHODS AND RESULTS: MCE was performed in 31 patients with previous myocardial infarction with a 2nd generation agent (NC100100, Nycomed AS), using harmonic triggered or continuous imaging and gain settings were kept constant throughout the study. Digitized images were post processed by subtraction of baseline from contrast data and colorized to reflect the intensity of myocardial contrast. Gray scale MCE alone, MCE images combined with baseline and subtracted colorized images were scored independently using a 16 segment model. The presence and severity of myocardial contrast abnormalities were compared with perfusion defined by rest MIBI-SPECT. Segments that were not visualized by continuous (17%) or triggered imaging (14%) after color processing were excluded from further analysis. The specificity of gray scale MCE alone (56%) or MCE combined with baseline 2D (47%) was significantly enhanced by subtraction and color coding (76%, p<0.001) of triggered images. The accuracy of the gray scale approaches (respectively 52% and 47%) was increased to 70% (p<0.001). Similarly, for continuous images, the specificity of gray scale MCE with and without baseline comparison was 23% and 42% respectively, compared with 60% after post processing (p<0.001). The accuracy of colorized images (59%) was also significantly greater than gray scale MCE (43% and 29%, p<0.001). The sensitivity of MCE for both acquisitions was not altered by subtraction. CONCLUSION: Post-processing with subtraction and color coding significantly improves the accuracy and specificity of MCE for detection of perfusion defects.

Multicenter Study↗
DOE OSTI2020

Tree, Shrub, and Grass Classification Using Only RGB Images

In this work, a semantic segmentation-based deep learning method, DeepLabV3+, is applied to classify three vegetation land covers, which are tree, shrub, and grass using only three band color (RGB) images. DeepLabV3+’s detection performance has been studied on low and high resolution datasets that both contain tree, shrub, and grass and some other land cover types. The two datasets are heavily imbalanced where shrub pixels are much fewer than tree and grass pixels. A simple weighting strategy known as median frequency weighting was incorporated into DeepLabV3+ to mitigate the data imbalance issue, which originally used uniform weights. The tree, shrub, grass classification performances are compared when all land cover types are included in the classification and also when classification is limited to the three vegetation classes with both uniform and median frequency weights. Among the three vegetation types, shrub is found to be the most challenging one to classify correctly whereas correct classification accuracy was highest for tree. It is observed that even though the median frequency weighting did not improve the overall accuracy, it resulted in better classification accuracy for the underrepresented classes such as shrub in our case and it also significantly increased the average class accuracy. The classification performance and computation time comparison of DeepLabV3+ with two other pixel-based classification methods on sampled pixels of the three vegetation classes showed that DeepLabV3+ achieves significantly higher accuracy than these methods with a trade-off for longer model training time.

58 GEOSCIENCES↗
DOE OSTI2015

TU-F-CAMPUS-I-02: Contrast Enhanced Cone Beam CT Imaging with Dual- Gantry Image Acquisition and Constrained Iterative Reconstruction-a Simulation Study for Liver Imaging Application

Purpose: Contrast time-density curves may help differentiate malignant tumors from normal tissues or benign tumors. Repetitive scans using conventional CT or cone beam CT techniques, which Result in unacceptably high dose, may not achieve the desired temporal resolution. In this study we describe and demonstrate a 4D imaging technique for imaging and quantifying contrast flows requiring only one or two 360° scans. Methods: A dual-gantry system is used to simultaneously acquire two projection images at orthogonal orientations. Following the scan, each or both of the two 360° projection sets are used to reconstruct an average contrast enhanced image set which is then segmented to form a 3D contrast map. Alternatively, a pre-injection scan may be made and used to reconstruct a pre-injection image set which is subtracted from the post-injection image set to form the 3D contrast map. Each of the two 360° projection sets is divided into 12 subsets, thus creating 12 pairs of 30° limited angle projection sets, each corresponding to a time spanning over 1/12 of the scanning time. Each pair of the projection sets are reconstructed as a time specific 3D image set with the maximum likelihood estimation iterative algorithm using the contrast map as the constraint. As a demonstration, a 4D abdominal phantom was constructed from clinical CT images with blood flow through the normal tissue and a tumor modeled and imaging process simulated. Results: We have successfully generated a 4D image phantom, and calculated the projection images. The time density curves derived from the reconstructed image set matched well with the flow model used to generate the phantom. Conclusion: Dual-gantry image acquisition and constrained iterative reconstruction algorithm may help to obtain time-density curves of contrast agents in blood flows, which may help differentiate malignant tumors from normal tissues or benign tumors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-CD-202-05: DECT Based Tissue Segmentation as Input to Monte Carlo Simulations for Proton Treatment Verification Using PET Imaging

Purpose: To improve agreement of predicted and measured positron emitter yields in patients, after proton irradiation for PET-based treatment verification, using a novel dual energy CT (DECT) tissue segmentation approach, overcoming known deficiencies from single energy CT (SECT). Methods: DECT head scans of 5 trauma patients were segmented and compared to existing decomposition methods with a first focus on the brain. For validation purposes, three brain equivalent solutions [water, white matter (WM) and grey matter (GM) – equivalent with respect to their reference carbon and oxygen contents and CT numbers at 90kVp and 150kVp] were prepared from water, ethanol, sucrose and salt. The activities of all brain solutions, measured during a PET scan after uniform proton irradiation, were compared to Monte Carlo simulations. Simulation inputs were various solution compositions obtained from different segmentation approaches from DECT, SECT scans, and known reference composition. Virtual GM solution salt concentration corrections were applied based on DECT measurements of solutions with varying salt concentration. Results: The novel tissue segmentation showed qualitative improvements in %C for patient brain scans (ground truth unavailable). The activity simulations based on reference solution compositions agree with the measurement within 3–5% (4–8Bq/ml). These reference simulations showed an absolute activity difference between WM (20%C) and GM (10%C) to H2O (0%C) of 43 Bq/ml and 22 Bq/ml, respectively. Activity differences between reference simulations and segmented ones varied from −6 to 1 Bq/ml for DECT and −79 to 8 Bq/ml for SECT. Conclusion: Compared to the conventionally used SECT segmentation, the DECT based segmentation indicates a qualitative and quantitative improvement. In controlled solutions, a MC input based on DECT segmentation leads to better agreement with the reference. Future work will address the anticipated improvement of quantification accuracy in patients, comparing different tissue decomposition methods with an MR brain segmentation. Acknowledgement: DFG-MAP and HIT-Heidelberg Deutsche Forschungsgemeinschaft (MAP); Bundesministerium fur Bildung und Forschung (01IB13001)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Improving microstructures segmentation via pretraining with synthetic data

Image analysis of material microstructures through microscopy is an integral capability in the field of materials science. The topological and chemical information obtained through microscopy allow us to draw vital connections between material microstructures, properties, and processing. While scanning electron microscopy (SEM) is able to yield a considerable wealth of information interpretable by the intuition of experts, there has been considerable interest in using machine learning, convolutional neural networks (CNNs) in particular, for such image analysis task. Training CNNs for an image analysis task requires a large annotated dataset. However, in many materials science applications, obtaining a large annotated dataset is cost and labor intensive. In this work, we study the use of synthetic data to enlarge the available annotated experimental data of uranium oxide. We utilize a modified Potts model to simulate uranium oxide particles with morphologies similar to those observed experimentally. We then leverage an image-to-image translation model to synthesize the simulated particles as if they are acquired with SEM. Through this process, we obtain pairs of particle images and their corresponding SEM representations, which corresponds to pairs of annotations and images. Unlike previous works, we leverage synthetic data for pretraining a CNN model prior, and finetune that model further with experimental data. We experimentally demonstrate that using synthetic data as incremental learning process benefits the overall performance compared to training a model on combined synthetic and experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specifc contextual clues and requires no training. This approach generalizes across diferent modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal refectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fdelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufcient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Publisher Correction: Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufficient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively.

Shahbazi, Ali↗
NASA NTRS1989

Optimal wavefront control for adaptive segmented mirrors

A ground-based astronomical telescope with a segmented primary mirror will suffer image-degrading wavefront aberrations from at least two sources: (1) atmospheric turbulence and (2) segment misalignment or figure errors of the mirror itself. This paper describes the derivation of a mirror control feedback matrix that assumes the presence of both types of aberration and is optimum in the sense that it minimizes the mean-squared residual wavefront error. Assumptions of the statistical nature of the wavefront measurement errors, atmospheric phase aberrations, and segment misalignment errors are made in the process of derivation. Examples of the degree of correlation are presented for three different types of wavefront measurement data and compared to results of simple corrections.

Downie, John D.↗
NASA NTRS2010

Fine alignment of a large segmented mirror

A system for aligning a segmented mirror includes a source of radiation directed along a first axis to the segmented mirror and a beamsplitter removably inserted along the first axis for redirecting radiation from the first axis to a second axis, substantially perpendicular to the first axis. An imaging array is positioned along the second axis for imaging the redirected radiation, and a knife-edge configured for cutting the redirected radiation is serially positioned to occlude and not occlude the redirected radiation, effectively providing a variable radiation pattern detected by the imaging array for aligning the segmented mirror.

Dey, Thomas William↗
DOE OSTI2012

Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation

Purpose: The amount of fibroglandular tissue content in the breast as estimated mammographically, commonly referred to as breast percent density (PD%), is one of the most significant risk factors for developing breast cancer. Approaches to quantify breast density commonly focus on either semiautomated methods or visual assessment, both of which are highly subjective. Furthermore, most studies published to date investigating computer-aided assessment of breast PD% have been performed using digitized screen-film mammograms, while digital mammography is increasingly replacing screen-film mammography in breast cancer screening protocols. Digital mammography imaging generates two types of images for analysis, raw (i.e., 'FOR PROCESSING') and vendor postprocessed (i.e., 'FOR PRESENTATION'), of which postprocessed images are commonly used in clinical practice. Development of an algorithm which effectively estimates breast PD% in both raw and postprocessed digital mammography images would be beneficial in terms of direct clinical application and retrospective analysis. Methods: This work proposes a new algorithm for fully automated quantification of breast PD% based on adaptive multiclass fuzzy c-means (FCM) clustering and support vector machine (SVM) classification, optimized for the imaging characteristics of both raw and processed digital mammography images as well as for individual patient and image characteristics. Our algorithm first delineates the breast region within the mammogram via an automated thresholding scheme to identify background air followed by a straight line Hough transform to extract the pectoral muscle region. The algorithm then applies adaptive FCM clustering based on an optimal number of clusters derived from image properties of the specific mammogram to subdivide the breast into regions of similar gray-level intensity. Finally, a SVM classifier is trained to identify which clusters within the breast tissue are likely fibroglandular, which are then aggregated into a final dense tissue segmentation that is used to compute breast PD%. Our method is validated on a group of 81 women for whom bilateral, mediolateral oblique, raw and processed screening digital mammograms were available, and agreement is assessed with both continuous and categorical density estimates made by a trained breast-imaging radiologist. Results: Strong association between algorithm-estimated and radiologist-provided breast PD% was detected for both raw (r= 0.82, p < 0.001) and processed (r= 0.85, p < 0.001) digital mammograms on a per-breast basis. Stronger agreement was found when overall breast density was assessed on a per-woman basis for both raw (r= 0.85, p < 0.001) and processed (0.89, p < 0.001) mammograms. Strong agreement between categorical density estimates was also seen (weighted Cohen's {kappa}{>=} 0.79). Repeated measures analysis of variance demonstrated no statistically significant differences between the PD% estimates (p > 0.1) due to either presentation of the image (raw vs processed) or method of PD% assessment (radiologist vs algorithm). Conclusions: The proposed fully automated algorithm was successful in estimating breast percent density from both raw and processed digital mammographic images. Accurate assessment of a woman's breast density is critical in order for the estimate to be incorporated into risk assessment models. These results show promise for the clinical application of the algorithm in quantifying breast density in a repeatable manner, both at time of imaging as well as in retrospective studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Vision 20/20: Positron emission tomography in radiation therapy planning, delivery, and monitoring

Positron emission tomography (PET) is increasingly considered as an effective imaging method to support several stages of radiation therapy. The combined usage of functional and morphological imaging in state-of-the-art PET/CT scanners is rapidly emerging to support the treatment planning process in terms of improved tumor delineation, and to assess the tumor response in follow-up investigations after or even during the course of fractionated therapy. Moreover, active research is being pursued on new tracers capable of providing different insights into tumor function, in order to identify areas of the planning volume which may require additional dosage for improved probability of tumor control. In this respect, major progresses in the next years will likely concern the development and clinical investigation of novel tracers and image processing techniques for reliable thresholding and segmentation, of treatment planning and beam delivery approaches integrating the PET imaging information, as well as improved multimodal clinical instrumentation such as PET/MR. But especially in the rapidly emerging case of ion beam therapy, the usage of PET is not only limited to the imaging of external tracers injected to the patient. In fact, a minor amount of positron emitters is formed in nuclear fragmentation reactions between the impinging ions and the tissue, bearing useful information for confirmation of the delivered treatment during or after therapeutic irradiation. Different implementations of unconventional PET imaging for therapy monitoring are currently being investigated clinically, and major ongoing research aims at new dedicated detector technologies and at challenging applications such as real-time imaging and time-resolved in vivo verification of motion compensated beam delivery. This paper provides an overview of the different areas of application of PET in radiation oncology and discusses the most promising perspectives in the years to come for radiation therapy planning, delivery, and monitoring.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Physically motivated global alignment method for electron tomography

Electron tomography is widely used for nanoscale determination of 3-D structures in many areas of science. Determining the 3-D structure of a sample from electron tomography involves three major steps: acquisition of sequence of 2-D projection images of the sample with the electron microscope, alignment of the images to a common coordinate system, and 3-D reconstruction and segmentation of the sample from the aligned image data. The resolution of the 3-D reconstruction is directly influenced by the accuracy of the alignment, and therefore, it is crucial to have a robust and dependable alignment method. In this paper, we develop a new alignment method which avoids the use of markers and instead traces the computed paths of many identifiable ‘local’ center-of-mass points as the sample is rotated. Compared with traditional correlation schemes, the alignment method presented here is resistant to cumulative error observed from correlation techniques, has very rigorous mathematical justification, and is very robust since many points and paths are used, all of which inevitably improves the quality of the reconstruction and confidence in the scientific results.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2011

Performance Improvements to the Neutron Imaging System at the National Ignition Facility

A team headed by LANL and including many members from LLNL and NSTec LO and NSTec LAO fielded a neutron imaging system (NIS) at the National Ignition Facility at the start of 2011. The NIS consists of a pinhole array that is located 32.5 cm from the source and that creates an image of the source in a segmented scintillator 28 m from the source. The scintillator is viewed by two gated, optical imaging systems: one that is fiber coupled, and one that is lens coupled. While there are a number of other pieces to the system related to pinhole alignment, collimation, shielding and data acquisition, those pieces are discussed elsewhere and are not relevant here. The system is operational and has successfully obtained data on more that ten imaging shots. This remainder of this whitepaper is divided in five main sections. In Section II, we identify three critical areas of improvement that we believe should be pursued to improve the performance of the system for future experiments: spatial resolution, temporal response and signal-to-noise ratio. In Section III, we discuss technologies that could be used to improve these critical performance areas. In Section IV, we describe a path to evolve the current system to achieve improved performance with minimal impact on the ability of the system to operate on shots. In Section V, we discuss the abilities, scope and timescales of the current teams and the Commissariat energie atomique (CEA). In Section VI, we summarize and make specific recommendations for collaboration on improvements to the NIS.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗