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At least 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

TU-FG-209-04: Testing of Digital Image Receptors Using AAPM TG-150’s Draft Recommendations - Investigating the Impact of Different Processing Parameters

Purpose: To evaluate implementation of AAPM TG-150’s draft recommendations via a parameter study for testing the performance of digital image receptors. Methods: Flat field images were acquired from 9 calibrated digital image receptors associated with 9 new portable digital radiography systems (Carestream Health, Inc.) based on the draft recommendations and manufacturer-specified calibration conditions (set of 4 images at input detector air kerma ranging from 1 to 25 µGy). Effects of exposure response function (linearized and logarithmic), ‘Presentation Intent Type’ (‘For Processing’ and ‘For Presentation’), detector orientation with respect to the anode-cathode axis (4 orientations; 900 rotations per iteration), different ROI sizes (5×5–40×40 mm{sup 2}) and elimination of varying dimensions of image border (0 mm i.e., without boundary elimination to 150 mm) on signal, noise, signal-to-noise ratio (SNR) and the associated nonuniformities were evaluated. Images were analyzed in Matlab and quantities were compared using ANOVA. Results: Signal, noise and SNR values averaged over 9 systems with default parameter values in draft recommendations were 4837.2±139.4, 19.7±0.9 and 246.4±10.1 (mean ± standard deviation), respectively (at input detector air kerma: 12.5 µGy). Signal, noise and SNR showed characteristic dependency on exposure response function and on ‘Presentation Intent Type’. These values were not affected by ROI size and detector orientation, but analysis showed that eliminating the edge pixels along the boundary was required for the noise parameter (coefficient of variation range for noise: 72%–106% and 3%–4% without and with boundary elimination; respectively). Local and global nonuniformities showed a similar dependence on the need for boundary elimination. Interestingly, computed non-uniformities showed agreement with manufacturer-reported values except for noise non-uniformities in two units; artifacts were seen in images from these two units highlighting the importance of independent evaluations. Conclusion: The effect of different parameters on performance characterization of digital image receptors was evaluated based on TG-150’s draft recommendations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-H-CAMPUS-IeP3-02: Neurovascular 4D Parametric Imaging Using Co-Registration of Biplane DSA Sequences with 3D Vascular Geometry Obtained From Cone Beam CT

Purpose: To create 4D parametric images using biplane Digital Subtraction Angiography (DSA) sequences co-registered with the 3D vascular geometry obtained from Cone Beam-CT (CBCT). Methods: We investigated a method to derive multiple 4D Parametric Imaging (PI) maps using only one CBCT acquisition. During this procedure a 3D-DSA geometry is stored and used subsequently for all 4D images. Each time a biplane DSA is acquired, we calculate 2D parametric maps of Bolus Arrival Time (BAT), Mean Transit Time (MTT) and Time to Peak (TTP). Arterial segments which are nearly parallel with one of the biplane imaging planes in the 2D parametric maps are co-registered with the 3D geometry. The values in the remaining vascular network are found using spline interpolation since the points chosen for co-registration on the vasculature are discrete and remaining regions need to be interpolated. To evaluate the method we used a patient CT volume data set for 3D printing a neurovascular phantom containing a complete Circle of Willis. We connected the phantom to a flow loop with a peristaltic pump, simulating physiological flow conditions. Contrast media was injected with an automatic injector at 10 ml/sec. Images were acquired with a Toshiba Infinix C-arm and 4D parametric image maps of the vasculature were calculated. Results: 4D BAT, MTT, and TTP parametric image maps of the Circle of Willis were derived. We generated color-coded 3D geometries which avoided artifacts due to vessel overlap or foreshortening in the projection direction. Conclusion: The software was tested successfully and multiple 4D parametric images were obtained from biplane DSA sequences without the need to acquire additional 3D-DSA runs. This can benefit the patient by reducing the contrast media and the radiation dose normally associated with these procedures. Partial support from NIH Grant R01-EB002873 and Toshiba Medical Systems Corp.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-AB-BRA-06: 4DCT-Ventilation: A Novel Imaging Modality for Thoracic Surgical Evaluation

Purpose: The current standard-of-care imaging used to evaluate lung cancer patients for surgical resection is nuclear-medicine ventilation. Surgeons use nuclear-medicine images along with pulmonary function tests (PFT) to calculate percent predicted postoperative (%PPO) PFT values by estimating the amount of functioning lung that would be lost with surgery. 4DCT-ventilation is an emerging imaging modality developed in radiation oncology that uses 4DCT data to calculate lung ventilation maps. We perform the first retrospective study to assess the use of 4DCT-ventilation for pre-operative surgical evaluation. The purpose of this work was to compare %PPO-PFT values calculated with 4DCT-ventilation and nuclear-medicine imaging. Methods: 16 lung cancer patients retrospectively reviewed had undergone 4DCTs, nuclear-medicine imaging, and had Forced Expiratory Volume in 1 second (FEV1) acquired as part of a standard PFT. For each patient, 4DCT data sets, spatial registration, and a density-change based model were used to compute 4DCT-ventilation maps. Both 4DCT and nuclear-medicine images were used to calculate %PPO-FEV1 using %PPO-FEV1=pre-operative FEV1*(1-fraction of total ventilation of resected lung). Fraction of ventilation resected was calculated assuming lobectomy and pneumonectomy. The %PPO-FEV1 values were compared between the 4DCT-ventilation-based calculations and the nuclear-medicine-based calculations using correlation coefficients and average differences. Results: The correlation between %PPO-FEV1 values calculated with 4DCT-ventilation and nuclear-medicine were 0.81 (p<0.01) and 0.99 (p<0.01) for pneumonectomy and lobectomy respectively. The average difference between the 4DCT-ventilation based and the nuclear-medicine-based %PPO-FEV1 values were small, 4.1±8.5% and 2.9±3.0% for pneumonectomy and lobectomy respectively. Conclusion: The high correlation results provide a strong rationale for a clinical trial translating 4DCT-ventilation to the surgical domain. Compared to nuclear-medicine, 4DCT-ventilation is cheaper, does not require a radioactive contrast agent, provides a faster imaging procedure, and has improved spatial resolution. 4DCT-ventilation can reduce the cost and imaging time for patients while providing improved spatial accuracy and quantitative results for surgeons. YV discloses grant from State of Colorado.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-AB-BRA-11: Improved Imaging of Permanent Prostate Brachytherapy Seed Implants by Combining an Endorectal X-Ray Sensor with a CT Scanner

Purpose: To test feasibility of the use of a digital endorectal x-ray sensor for improved image resolution of permanent brachytherapy seed implants compared to conventional CT. Methods: Two phantoms simulating the male pelvic region were used to test the capabilities of a digital endorectal x-ray sensor for imaging permanent brachytherapy seed implants. Phantom 1 was constructed from acrylic plastic with cavities milled in the locations of the prostate and the rectum. The prostate cavity was filled a Styrofoam plug implanted with 10 training seeds. Phantom 2 was constructed from tissue-equivalent gelatins and contained a prostate phantom implanted with 18 strands of training seeds. For both phantoms, an intraoral digital dental x-ray sensor was placed in the rectum within 2 cm of the seed implants. Scout scans were taken of the phantoms over a limited arc angle using a CT scanner (80 kV, 120–200 mA). The dental sensor was removed from the phantoms and normal helical CT and scout (0 degree) scans using typical parameters for pelvic CT (120 kV, auto-mA) were collected. A shift-and add tomosynthesis algorithm was developed to localize seed plane location normal to detector face. Results: The endorectal sensor produced images with improved resolution compared to CT scans. Seed clusters and individual seed geometry were more discernable using the endorectal sensor. Seed 3D locations, including seeds that were not located in every projection image, were discernable using the shift and add algorithm. Conclusion: This work shows that digital endorectal x-ray sensors are a feasible method for improving imaging of permanent brachytherapy seed implants. Future work will consist of optimizing the tomosynthesis technique to produce higher resolution, lower dose images of 1) permanent brachytherapy seed implants for post-implant dosimetry and 2) fine anatomic details for imaging and managing prostatic disease compared to CT images. Funding: LSU Faculty Start-up Funding. Disclosure: XDR Radiography has loaned our research group the digital x-ray detector used in this work. CoI: None.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-AB-207A-03: A CBCT Head Scanner for Point-Of-Care Imaging of Intracranial Hemorrhage

Purpose: This work reports the design, development, and first technical assessment of a cone-beam CT (CBCT) scanner developed specifically for imaging of acute intracranial hemorrhage (ICH) at the point of care, with target applications in diagnosis and monitoring of traumatic brain injury, stroke, and postsurgical hemorrhage. Methods: System design employed a task-based image quality model to quantify the influence of factors such as additive noise and high-gain (HG) detector readout on ICH detectability. Three bowtie filters with varying bare-beam attenuation strength and curvature were designed to enable HG readout without detector saturation, and a polyenergetic gain correction was developed to minimize artifacts from bowtie flood-field calibration. Image reconstruction used an iterative penalized weighted least squares (PWLS) method with artifact correction including Monte Carlo scatter estimation, Joseph-Spital beam hardening correction, and spatiotemporal deconvolution of detector glare and lag. Radiation dose was characterized for half-scan and full-scan protocols at various kV, and imaging performance was assessed in a head phantom presenting simulated ICH with diameter ranging 2–12 mm. Results: The image quality model guided system design and was validated by measurements on a CBCT imaging bench. Compared to low-gain readout without a bowtie filter, the combination of HG readout and a modest bowtie improved the contrast-to-noise ratio (CNR per unit square-root dose) by 20% in the center of the image but degraded noise performance near the periphery (20% reduction in CNR). Low-frequency bowtie artifacts (∼100 HU magnitude) were corrected by the polyenergetic gain correction. Image reconstructions on the prototype scanner demonstrate clear visibility of the smallest ICH insert (2 mm diameter) in both HG readout (with a bowtie) and dual-gain readout (without bowtie). Conclusion: Technical assessment of the prototype scanner suggests the capability for reliable visualization of small (2 mm), low-contrast (50 HU) ICH at <20 mGy dose and motivates translation to clinical studies, now underway. Xiaohui Wang and David Foos (co-authors) are employees of Carestream Health. Research funding support from Carestream Health.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-AB-207A-11: Respiratory Motion Guided 4DCBCT - A Step Towards Controlling 4DCBCT Image Quality

Purpose: We have developed a method, called respiratory motion guided 4DCBCT (RMG-4DCBCT), in which the gantry speed and projection frequency are varied in response to the patient’s real-time respiratory signal to eliminate streaking artifacts and to suppress duplicate projections in 4DCBCT images. In 2015, we realized RMG-4DCBCT on an Elekta Synergy linear accelerator with a mechanical relay to suppress projections and a potentiometer to adjust the gantry speed in response to the patient’s real-time respiratory signal. The aim of this study was to analyse the image quality to determine what can and cannot be controlled. Methods: Using RMG-4DCBCT, we acquired 40 (RMG-4DCBCT-40) and 60 (RMG-4DCBCT-60) equally spaced projections per respiratory phase of the CIRS dynamic thorax phantom with breathing periods from 2s to 8s and two breathing traces from lung cancer patients. The contrast to noise ratio (CNR) and edge response width (ERW) were used to compare image quality between RMG-4DCBCT and conventional 4DCBCT. Results: Regardless of the breathing period, for RMG-4DCBCT, the CNR is approximately 7 and 9 with RMG-4DCBCT-40 and RMG-4DCBCT-60 respectively. Conventional 4DCBCT has a CNR dropping from 20 down to 6 as the breathing period drops from 2s to 8s. With RMG-4DCBCT, the ERW, in the direction of phantom motion, ranges from 2.1mm to 2.5mm as the breathing period drops from 2s to 8s which compares to a higher range of 2.0mm to 2.5mm with conventional 4DCBCT. Images with similar quality to conventional 4DCBCT can be acquired with RMG-4DCBCT-40 which has a 70% reduction in imaging dose. Conclusion: The image contrast can be controlled with RMG-4DCBCT regardless of the patients breathing rate. However, although the image sharpness is better with RMG-4DCBCT, image sharpness has a small dependence on the breathing period; the accuracy of registration and segmentation will therefore vary with the patient’s breathing period. This project was supported by a National Health and Medical Research Council (NHMRC) project grant 1034060 and Cancer Australia grant number 1084566.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-H-207A-06: Hypoxia Quantification in Static PET Images: The Signal in the Noise

Purpose: Quantification of hypoxia from PET images is of considerable clinical interest. In the absence of dynamic PET imaging the hypoxic fraction (HF) of a tumor has to be estimated from voxel values of activity concentration of a radioactive hypoxia tracer. This work is part of an effort to standardize quantification of tumor hypoxic fraction from PET images. Methods: A simple hypoxia imaging model in the tumor was developed. The distribution of the tracer activity was described as the sum of two different probability distributions, one for the normoxic (and necrotic), the other for the hypoxic voxels. The widths of the distributions arise due to variability of the transport, tumor tissue inhomogeneity, tracer binding kinetics, and due to PET image noise. Quantification of HF was performed for various levels of variability using two different methodologies: a) classification thresholds between normoxic and hypoxic voxels based on a non-hypoxic surrogate (muscle), and b) estimation of the (posterior) probability distributions based on maximizing likelihood optimization that does not require a surrogate. Data from the hypoxia imaging model and from 27 cervical cancer patients enrolled in a FAZA PET study were analyzed. Results: In the model, where the true value of HF is known, thresholds usually underestimate the value for large variability. For the patients, a significant uncertainty of the HF values (an average intra-patient range of 17%) was caused by spatial non-uniformity of image noise which is a hallmark of all PET images. Maximum likelihood estimation (MLE) is able to directly optimize for the weights of both distributions, however, may suffer from poor optimization convergence. For some patients, MLE-based HF values showed significant differences to threshold-based HF-values. Conclusion: HF-values depend critically on the magnitude of the different sources of tracer uptake variability. A measure of confidence should also be reported.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-AB-207A-05: A Fully-Automated Pipeline for Generating CT Images Across a Range of Doses and Reconstruction Methods

Purpose: To evaluate the robustness of CAD or Quantitative Imaging methods, they should be tested on a variety of cases and under a variety of image acquisition and reconstruction conditions that represent the heterogeneity encountered in clinical practice. The purpose of this work was to develop a fully-automated pipeline for generating CT images that represent a wide range of dose and reconstruction conditions. Methods: The pipeline consists of three main modules: reduced-dose simulation, image reconstruction, and quantitative analysis. The first two modules of the pipeline can be operated in a completely automated fashion, using configuration files and running the modules in a batch queue. The input to the pipeline is raw projection CT data; this data is used to simulate different levels of dose reduction using a previously-published algorithm. Filtered-backprojection reconstructions are then performed using FreeCT-wFBP, a freely-available reconstruction software for helical CT. We also added support for an in-house, model-based iterative reconstruction algorithm using iterative coordinate-descent optimization, which may be run in tandem with the more conventional recon methods. The reduced-dose simulations and image reconstructions are controlled automatically by a single script, and they can be run in parallel on our research cluster. The pipeline was tested on phantom and lung screening datasets from a clinical scanner (Definition AS, Siemens Healthcare). Results: The images generated from our test datasets appeared to represent a realistic range of acquisition and reconstruction conditions that we would expect to find clinically. The time to generate images was approximately 30 minutes per dose/reconstruction combination on a hybrid CPU/GPU architecture. Conclusion: The automated research pipeline promises to be a useful tool for either training or evaluating performance of quantitative imaging software such as classifiers and CAD algorithms across the range of acquisition and reconstruction parameters present in the clinical environment. Funding support: NIH U01 CA181156; Disclosures (McNitt-Gray): Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-CD-202-04: Evaluation of Virtual Non-Contrast Images From a Novel Split-Filter Dual-Energy CT Technique

Purpose: To compare the ability of two dual-energy CT techniques, a novel split-filter single-source technique of superior temporal resolution against an established sequential-scan technique, to remove iodine contrast from images with minimal impact on CT number accuracy. Methods: A phantom containing 8 tissue substitute materials and vials of varying iodine concentrations (1.7–20.1 mg I /mL) was imaged using a Siemens Edge CT scanner. Dual-energy virtual non-contrast (VNC) images were generated using the novel split-filter technique, in which a 120kVp spectrum is filtered by tin and gold to create high- and low-energy spectra with < 1 second temporal separation between the acquisition of low- and high-energy data. Additionally, VNC images were generated with the sequential-scan technique (80 and 140kVp) for comparison. CT number accuracy was evaluated for all materials at 15, 25, and 35mGy CTDIvol. Results: The spectral separation was greater for the sequential-scan technique than the split-filter technique with dual-energy ratios of 2.18 and 1.26, respectively. Both techniques successfully removed iodine contrast, resulting in mean CT numbers within 60HU of 0HU (split-filter) and 40HU of 0HU (sequential-scan) for all iodine concentrations. Additionally, for iodine vials of varying diameter (2–20 mm) with the same concentration (9.9 mg I /mL), the system accurately detected iodine for all sizes investigated. Both dual-energy techniques resulted in reduced CT numbers for bone materials (by >400HU for the densest bone). Increasing the imaging dose did not improve the CT number accuracy for bone in VNC images. Conclusion: VNC images from the split-filter technique successfully removed iodine contrast. These results demonstrate a potential for improving dose calculation accuracy and reducing patient imaging dose, while achieving superior temporal resolution in comparison sequential scans. For both techniques, inaccuracies in CT numbers for bone materials necessitate consideration for radiation therapy treatment planning.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

3D exemplar-based random walks for tooth segmentation from cone-beam computed tomography images

Purpose: Tooth segmentation is an essential step in acquiring patient-specific dental geometries from cone-beam computed tomography (CBCT) images. Tooth segmentation from CBCT images is still a challenging task considering the comparatively low image quality caused by the limited radiation dose, as well as structural ambiguities from intercuspation and nearby alveolar bones. The goal of this paper is to present and discuss the latest accomplishments in semisupervised tooth segmentation with adaptive 3D shape constraints. Methods: The authors propose a 3D exemplar-based random walk method of tooth segmentation from CBCT images. The proposed method integrates semisupervised label propagation and regularization by 3D exemplar registration. To begin with, the pure random walk method is to get an initial segmentation of the teeth, which tends to be erroneous because of the structural ambiguity of CBCT images. And then, as an iterative refinement, the authors conduct a regularization by using 3D exemplar registration, as well as label propagation by random walks with soft constraints, to improve the tooth segmentation. In the first stage of the iteration, 3D exemplars with well-defined topologies are adapted to fit the tooth contours, which are obtained from the random walks based segmentation. The soft constraints on voxel labeling are defined by shape-based foreground dentine probability acquired by the exemplar registration, as well as the appearance-based probability from a support vector machine (SVM) classifier. In the second stage, the labels of the volume-of-interest (VOI) are updated by the random walks with soft constraints. The two stages are optimized iteratively. Instead of the one-shot label propagation in the VOI, an iterative refinement process can achieve a reliable tooth segmentation by virtue of exemplar-based random walks with adaptive soft constraints. Results: The proposed method was applied for tooth segmentation of twenty clinically captured CBCT images. Three metrics, including the Dice similarity coefficient (DSC), the Jaccard similarity coefficient (JSC), and the mean surface deviation (MSD), were used to quantitatively analyze the segmentation of anterior teeth including incisors and canines, premolars, and molars. The segmentation of the anterior teeth achieved a DSC up to 98%, a JSC of 97%, and an MSD of 0.11 mm compared with manual segmentation. For the premolars, the average values of DSC, JSC, and MSD were 98%, 96%, and 0.12 mm, respectively. The proposed method yielded a DSC of 95%, a JSC of 89%, and an MSD of 0.26 mm for molars. Aside from the interactive definition of label priors by the user, automatic tooth segmentation can be achieved in an average of 1.18 min. Conclusions: The proposed technique enables an efficient and reliable tooth segmentation from CBCT images. This study makes it clinically practical to segment teeth from CBCT images, thus facilitating pre- and interoperative uses of dental morphologies in maxillofacial and orthodontic treatments.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Automatic segmentation of the glenohumeral cartilages from magnetic resonance images

Purpose: Magnetic resonance (MR) imaging plays a key role in investigating early degenerative disorders and traumatic injuries of the glenohumeral cartilages. Subtle morphometric and biochemical changes of potential relevance to clinical diagnosis, treatment planning, and evaluation can be assessed from measurements derived from in vivo MR segmentation of the cartilages. However, segmentation of the glenohumeral cartilages, using approaches spanning manual to automated methods, is technically challenging, due to their thin, curved structure and overlapping intensities of surrounding tissues. Automatic segmentation of the glenohumeral cartilages from MR imaging is not at the same level compared to the weight-bearing knee and hip joint cartilages despite the potential applications with respect to clinical investigation of shoulder disorders. In this work, the authors present a fully automated segmentation method for the glenohumeral cartilages using MR images of healthy shoulders. Methods: The method involves automated segmentation of the humerus and scapula bones using 3D active shape models, the extraction of the expected bone–cartilage interface, and cartilage segmentation using a graph-based method. The cartilage segmentation uses localization, patient specific tissue estimation, and a model of the cartilage thickness variation. The accuracy of this method was experimentally validated using a leave-one-out scheme on a database of MR images acquired from 44 asymptomatic subjects with a true fast imaging with steady state precession sequence on a 3 T scanner (Siemens Trio) using a dedicated shoulder coil. The automated results were compared to manual segmentations from two experts (an experienced radiographer and an experienced musculoskeletal anatomist) using the Dice similarity coefficient (DSC) and mean absolute surface distance (MASD) metrics. Results: Accurate and precise bone segmentations were achieved with mean DSC of 0.98 and 0.93 for the humeral head and glenoid fossa, respectively. Mean DSC scores of 0.74 and 0.72 were obtained for the humeral and glenoid cartilage volumes, respectively. The manual interobserver reliability evaluated by DSC was 0.80 ± 0.03 and 0.76 ± 0.04 for the two cartilages, implying that the automated results were within an acceptable 10% difference. The MASD between the automatic and the corresponding manual cartilage segmentations was less than 0.4 mm (previous studies reported mean cartilage thickness of 1.3 mm). Conclusions: This work shows the feasibility of volumetric segmentation and separation of the glenohumeral cartilages from MR images. To their knowledge, this is the first fully automated algorithm for volumetric segmentation of the individual glenohumeral cartilages from MR images. The approach was validated against manual segmentations from experienced analysts. In future work, the approach will be validated on imaging datasets acquired with various MR contrasts in patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

A Locally Adapting Technique for Edge Detection using Image Segmentation

Rapid growth in the field of quantitative digital image analysis is paving the way for scientific researchers to make precise measurements about objects in an image. To compute quantities from an image such as the density of compressed materials or the velocity of a shockwave, object boundaries must first be determined. Images containing regions that each have a spatial trend in intensity are of particular interest here. For edge detection, we present a supervised, statistical image segmentation method that incorporates spatial information to locate boundaries between regions with overlapping intensity histograms, specifically for images where the regions are known but precise boundary locations are unknown. The segmentation of a pixel is determined by comparing its intensity to distributions from nearby pixel intensities, and a gradient of the segmented image indicates edge locations. Because of the statistical nature of the algorithm, we use maximum likelihood estimation to quantify uncertainty about each boundary. We demonstrate the success of this algorithm at locating boundaries and providing uncertainty bands on a radiograph of a multicomponent cylinder and on an optical image of a laser-induced shockwave.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Chlorophyll fluorescence imaging captures photochemical efficiency of grain sorghum (Sorghum bicolor) in a field setting

Abstract Background Photosynthesis is one of the most important biological reactions and forms the basis of crop productivity and yield on which a growing global population relies. However, to develop improved plant cultivars that are capable of increased productivity, methods that can accurately and quickly quantify photosynthetic efficiency in large numbers of genotypes under field conditions are needed. Chlorophyll fluorescence imaging is a rapid, non-destructive measurement that can provide insight into the efficiency of the light-dependent reactions of photosynthesis. Results To test and validate a field-deployed fluorescence imaging system on the TERRA-REF field scanalyzer, leaves of potted sorghum plants were treated with a photosystem II inhibitor, DCMU, to reduce photochemical efficiency (F V /F M ). The ability of the fluorescence imaging system to detect changes in fluorescence was determined by comparing the image-derived values with a handheld fluorometer. This study demonstrated that the imaging system was able to accurately measure photochemical efficiency (F V /F M ) and was highly correlated ( r = 0.92) with the handheld fluorometer values. Additionally, the fluorescence imaging system was able to track the decrease in photochemical efficiency due to treatment of DCMU over a 7 day period. Conclusions The system’s ability to capture the temporal dynamics of the plants’ response to this induced stress, which has comparable dynamics to abiotic and biotic stressors found in field environments, indicates the system is operating correctly. With the validation of the fluorescence imaging system, physiological and genetic studies can be undertaken that leverage the fluorescence imaging capabilities and throughput of the field scanalyzer.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Initial experience with a SiPM-based PET/CT scanner: influence of acquisition time on image quality

Background: A newly introduced PET/CT scanner (Discovery Meaningful Insights—DMI, GE Healthcare) includes the silicon photomultiplier (SiPM) with timeof-flight (TOF) technology first used in the GE SIGNA PET/MRI. In this study, we investigated the impact of various acquisition times on image quality using this SiPM-based PET/CT. Methods: We reviewed data from 58 participants with cancer who were scanned using the DMI PET/CT scanner. The administered dosages ranged 295.3–429.9 MBq (mean ± SD 356.3 ± 37.4) and imaging started at 71–142 min (mean ± SD 101.41 ± 17. 52) after administration of the radiopharmaceutical. The patients’ BMI ranged 19.79–46. 16 (mean ± SD 26.55 ± 5.53). We retrospectively reconstructed the raw TOF data at 30, 60, 90, and 120 s/bed and at the standard image acquisition time per clinical protocol (180 or 210 s/bed depending on BMI). Each reconstruction was reviewed blindly by two nuclear medicine physicians and scored 1–5 (1—poor, 5—excellent quality). The liver signal-to-noise ratio (SNR) was used as a quantitative measure of image quality. Results: The average scores ± SD of the readers were 2.61 ± 0.83, 3.70 ± 0.92, 4.36 ± 0.82, 4.82 ± 0.39, and 4.91 ± 0.91 for the 30, 60, 90, and 120 s/bed and at standard acquisition time, respectively. Inter-reader agreement on image quality assessment was good, with a weighted kappa of 0.80 (95% CI 0.72–0.81). In the evaluation of the effects of time per bed acquisition on semi-quantitative measurements, we found that the only time point significantly different from the standard time were 30 and 60 s (both with P < 0.001). The effects of dose and BMI were not statistically significant (P = 0.195 and 0.098, respectively). There was a significant positive effect of time on SNR (P < 0.001), as well as a significant negative effect of weight (P < 0.001). Conclusions: Our results suggest that despite significant delays from injection to imaging (due to comparison with standard PET/CT) compared to standard clinical operations and even in a population with average BMI > 25, images can be acquired as fast as 90 s/bed using the SiPM PET/CT and still result in very good image quality (average score > 4).

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS1997

STRIPE: Remote Driving Using Limited Image Data

Driving a vehicle, either directly or remotely, is an inherently visual task. When heavy fog limits visibility, we reduce our car's speed to a slow crawl, even along very familiar roads. In teleoperation systems, an operator's view is limited to images provided by one or more cameras mounted on the remote vehicle. Traditional methods of vehicle teleoperation require that a real time stream of images is transmitted from the vehicle camera to the operator control station, and the operator steers the vehicle accordingly. For this type of teleoperation, the transmission link between the vehicle and operator workstation must be very high bandwidth (because of the high volume of images required) and very low latency (because delayed images can cause operators to steer incorrectly). In many situations, such a high-bandwidth, low-latency communication link is unavailable or even technically impossible to provide. Supervised TeleRobotics using Incremental Polyhedral Earth geometry, or STRIPE, is a teleoperation system for a robot vehicle that allows a human operator to accurately control the remote vehicle across very low bandwidth communication links, and communication links with large delays. In STRIPE, a single image from a camera mounted on the vehicle is transmitted to the operator workstation. The operator uses a mouse to pick a series of 'waypoints' in the image that define a path that the vehicle should follow. These 2D waypoints are then transmitted back to the vehicle, where they are used to compute the appropriate steering commands while the next image is being transmitted. STRIPE requires no advance knowledge of the terrain to be traversed, and can be used by novice operators with only minimal training. STRIPE is a unique combination of computer and human control. The computer must determine the 3D world path designated by the 2D waypoints and then accurately control the vehicle over rugged terrain. The human issues involve accurate path selection, and the prevention of disorientation, a common problem across all types of teleoperation systems. STRIPE is the only semi-autonomous teleoperation system that can accurately follow paths designated in monocular images on varying terrain. The thesis describes the STRIPE algorithm for tracking points using the incremental geometry model, insight into the design and redesign of the interface, an analysis of the effects of potential errors, details of the user studies, and hints on how to improve both the algorithm and interface for future designs.

REMOTE CONTROL↗
NASA NTRS1995

Trainable Cataloging for Digital Image Libraries with Applications to Volcano Detection

Users of digital image libraries are often not interested in image data per se but in derived products such as catalogs of objects of interest. Converting an image database into a usable catalog is typically carried out manually at present. For many larger image databases the purely manual approach is completely impractical. In this paper we describe the development of a trainable cataloging system: the user indicates the location of the objects of interest for a number of training images and the system learns to detect and catalog these objects in the rest of the database. In particular we describe the application of this system to the cataloging of small volcanoes in radar images of Venus. The volcano problem is of interest because of the scale (30,000 images, order of 1 million detectable volcanoes), technical difficulty (the variability of the volcanoes in appearance) and the scientific importance of the problem. The problem of uncertain or subjective ground truth is of fundamental importance in cataloging problems of this nature and is discussed in some detail. Experimental results are presented which quantify and compare the detection performance of the system relative to human detection performance. The paper concludes by discussing the limitations of the proposed system and the lessons learned of general relevance to the development of digital image libraries.

digital image libraries, trainable cataloging↗
NASA NTRSDate not supplied

Enabling Space Biological Knowledge Discovery Through Image and Video Data Sharing

Increased biomedical risks and challenges associated with deep space missions and experiments (cis-Lunar, Mars transit/surface) require new knowledge discovery and development of novel ecosystems. Supporting distant and long-duration missions and experiments requires biological data (from yeast, microbes, fruit flies, C. elegans, plants, crops, rodents, humans) be findable, accessible, interoperable, reusable (FAIR), and maximally open-access. As data-intensive, bioinformatic, meta-analytical, and computer-assisted approaches continue to be a centerpiece of modern research, the NASA Biological and Physical Sciences division is expanding its Open Science capabilities beyond NASA GeneLab. The NASA Ames Life Sciences Data Archive (ALSDA) is a repository which is responsible for collecting and access to space biological imagery and video, alongside tabular and environmental data. In this presentation, we will discuss strategies dealing with archiving, curating, and accessibility of images from very distinct imaging modalities (e.g., micro-computed tomography, magnetic resonance imaging, photographic images of plants, fluorescence microscopy, behavioral videos, etc.). There are two main challenges: 1. Open-source data storage and 2. Metadata related to the imagery-video. Both have been solved by leveraging two existing open-source systems. For data storage, ALSDA is utilizing components through the Open Microscopy Environment (OME), which can read most imaging proprietary formats and display on a web interface complex multidimensional images (Z stack, multi-channel, temporal, spectral). Most technical metadata from imaging modalities are captured seamlessly. For metadata capturing experimental details, ALSDA (like GeneLab) uses the ISA-Tab specification which relies on the ISA data model to order and classify metadata. The ISA data model uses a tree structure with three files to capture the metadata: The top layer is the Investigations file, the second layer is the Study file(s), and the last layer is the Assay file(s). We believe such an approach may be useful for other types of image research data from other investigators in the AGU community.

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NASA NTRSDate not supplied

From the Solar Limb and Out: Results from the Wide-Field EUV Image Campaigns with GOES/SUVI

Traditional approaches to tracking solar outflows for space weather forecasting rely primarily on coronagraph images, which generally observe the solar corona above a minimum height of about 2.5 solar radii. EUV images have been widely used to characterize features on the solar disk, but the limited fields of view of most current EUV imagers have prevented their use for tracking outflows through the inner and middle coronae. A series of off-point campaigns with the GOES 16-18 Solar Ultraviolet Imager (SUVI) between 2018 and 2022 from three Flight Models have provided an opportunity to assess the value of extended EUV images for space weather forecasting applications. These new results demonstrate that wide field-of-view EUV images are useful for characterizing the early onset of eruptive events and tracking smaller outflow into the solar wind. They also reveal the origins of shocks that are known to accelerate particles and drive solar energetic particle (SEP) events. Because CMEs generally experience the bulk of their acceleration below the height of white light coronagraphic observations, these images provide information about the origins of these events that has not been available traditionally. Together with coronagraphic measurements, EUV images provide the continuous views needed to connect CMEs back to their source regions. Here, we present these new SUVI observations and discuss their potential use in space weather operations.

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