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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Eliciting the impacts of cellular noise on metabolic trade-offs by quantitative mass imaging

Metabolic trade-offs between growth and productivity are key constraints in metabolic engineering and adaptive evolution; however, how cellular noise impacts these trade offs and drives the emergence of subpopulations with distinct resource allocation strategies, remains largely unknown. Here, we introduce a single-cell strategy for quantifying the trade-offs between triacylglycerol production and growth in the oleaginous microorganism Yarrowia lipolytica. The strategy relies on high-throughput quantitative-phase imaging and, enabled by nanoscale secondary ion mass spectrometry analyses and dedicated image processing, allows us to image how resources are partitioned between growth and productivity. Enhanced precision over population-averaging biotechnologies and conventional microscopy demonstrates how cellular noise impacts growth and productivity differently. As such, subpopulations with distinct metabolic trade-offs emerge, with notable impacts on strain performance and robustness. By quantifying autophagic catabolic fluxes under nutrient-limiting conditions, we discover an underlying cell-to-cell heterogeneity in protein and fatty-acid recycling, unmasking a potential bet-hedging survival strategy under starvation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Spatiotemporal Learning in Power Modules: Wavelet-Enhanced Forecasting of Thermomechanical Degradation

Detecting internal defects in power electronics packages is critical for their performance and reliability, especially under extreme operating conditions, as these defects can lead to catastrophic failure if not properly addressed. Confocal scanning acoustic microscopy (C-SAM) plays a key role in the nondestructive evaluation of bond layer degradation within a power electronics package by detecting defects such as delamination, voids, and cracks. However, accurately quantifying and predicting these defects from C-SAM images remains a significant challenge due to the low noise-to-signal ratio, which typically arises from both imaging process and bond patterns itself. In this paper, we explore machine learning strategies for processing C-SAM images and providing predictive models of defect growth. We use C-SAM images of sintered copper and sintered silver samples, which are obtained under accelerated thermal experiments, as the representative dataset for our study. We investigate the effect of Fourier transforms and wavelet transforms on these datasets to remove high-frequency noise and address noise across multiple scales with histogram equalization to enhance the contrast and improve the visibility of defects. As a result, defect boundaries can be clearly distinguished, enabling more accurate tracking of their growth over time. We then employ different time-series forecasting algorithms on the denoised images to formulate an image-based lifetime prediction model. Statistical models and deep-learning techniques are trained on images obtained in the early stages of thermal shock, and defect growth in the later stages is predicted. Our work serves as a preliminary attempt to improve the accuracy of lifetime prediction models of power electronics packages, which is critical under extreme operating environments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2013

A framework to measure myocardial extracellular volume fraction using dual-phase low dose CT images

Purpose: Myocardial extracellular volume fraction (ECVF) is a surrogate imaging biomarker of diffuse myocardial fibrosis, a hallmark of pathologic ventricular remodeling. Low dose cardiac CT is emerging as a promising modality to detect diffuse interstitial myocardial fibrosis due to its fast acquisition and low radiation; however, the insufficient contrast in the low dose CT images poses great challenge to measure ECVF from the image. Methods: To deal with this difficulty, the authors present a complete ECVF measurement framework including a point-guided myocardial modeling, a deformable model-based myocardium segmentation, nonrigid registration of pre- and post-CT, and ECVF calculation. Results: The proposed method was evaluated on 20 patients by two observers. Compared to the manually delineated reference segmentations, the accuracy of our segmentation in terms of true positive volume fraction (TPVF), false positive volume fraction (FPVF), and average surface distance (ASD), were 92.18% ± 3.52%, 0.31% ± 0.10%, 0.69 ± 0.14 mm, respectively. The interobserver variability measured by concordance correlation coefficient regarding TPVF, FPVF, and ASD were 0.95, 0.90, 0.94, respectively, demonstrating excellent agreement. Bland-Altman method showed 95% limits of agreement between ECVF at CT and ECVF at MR. Conclusions: The proposed framework demonstrates its efficiency, accuracy, and noninvasiveness in ECVF measurement and dramatically advances the ECVF at cardiac CT toward its clinical use.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

Pulmonary lobe segmentation based on ridge surface sampling and shape model fitting

Purpose: Performing lobe-based quantitative analysis of the lung in computed tomography (CT) scans can assist in efforts to better characterize complex diseases such as chronic obstructive pulmonary disease (COPD). While airways and vessels can help to indicate the location of lobe boundaries, segmentations of these structures are not always available, so methods to define the lobes in the absence of these structures are desirable. Methods: The authors present a fully automatic lung lobe segmentation algorithm that is effective in volumetric inspiratory and expiratory computed tomography (CT) datasets. The authors rely on ridge surface image features indicating fissure locations and a novel approach to modeling shape variation in the surfaces defining the lobe boundaries. The authors employ a particle system that efficiently samples ridge surfaces in the image domain and provides a set of candidate fissure locations based on the Hessian matrix. Following this, lobe boundary shape models generated from principal component analysis (PCA) are fit to the particles data to discriminate between fissure and nonfissure candidates. The resulting set of particle points are used to fit thin plate spline (TPS) interpolating surfaces to form the final boundaries between the lung lobes. Results: The authors tested algorithm performance on 50 inspiratory and 50 expiratory CT scans taken from the COPDGene study. Results indicate that the authors' algorithm performs comparably to pulmonologist-generated lung lobe segmentations and can produce good results in cases with accessory fissures, incomplete fissures, advanced emphysema, and low dose acquisition protocols. Dice scores indicate that only 29 out of 500 (5.85%) lobes showed Dice scores lower than 0.9. Two different approaches for evaluating lobe boundary surface discrepancies were applied and indicate that algorithm boundary identification is most accurate in the vicinity of fissures detectable on CT. Conclusions: The proposed algorithm is effective for lung lobe segmentation in absence of auxiliary structures such as vessels and airways. The most challenging cases are those with mostly incomplete, absent, or near-absent fissures and in cases with poorly revealed fissures due to high image noise. However, the authors observe good performance even in the majority of these cases.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

Novel multimodality segmentation using level sets and Jensen-Rényi divergence

Purpose: Positron emission tomography (PET) is playing an increasing role in radiotherapy treatment planning. However, despite progress, robust algorithms for PET and multimodal image segmentation are still lacking, especially if the algorithm were extended to image-guided and adaptive radiotherapy (IGART). This work presents a novel multimodality segmentation algorithm using the Jensen-Rényi divergence (JRD) to evolve the geometric level set contour. The algorithm offers improved noise tolerance which is particularly applicable to segmentation of regions found in PET and cone-beam computed tomography. Methods: A steepest gradient ascent optimization method is used in conjunction with the JRD and a level set active contour to iteratively evolve a contour to partition an image based on statistical divergence of the intensity histograms. The algorithm is evaluated using PET scans of pharyngolaryngeal squamous cell carcinoma with the corresponding histological reference. The multimodality extension of the algorithm is evaluated using 22 PET/CT scans of patients with lung carcinoma and a physical phantom scanned under varying image quality conditions. Results: The average concordance index (CI) of the JRD segmentation of the PET images was 0.56 with an average classification error of 65%. The segmentation of the lung carcinoma images had a maximum diameter relative error of 63%, 19.5%, and 14.8% when using CT, PET, and combined PET/CT images, respectively. The estimated maximal diameters of the gross tumor volume (GTV) showed a high correlation with the macroscopically determined maximal diameters, with aR{sup 2} value of 0.85 and 0.88 using the PET and PET/CT images, respectively. Results from the physical phantom show that the JRD is more robust to image noise compared to mutual information and region growing. Conclusions: The JRD has shown improved noise tolerance compared to mutual information for the purpose of PET image segmentation. Presented is a flexible framework for multimodal image segmentation that can incorporate a large number of inputs efficiently for IGART.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Dual energy CT for attenuation correction with PET/CT

Purpose: The authors evaluate the energy dependent noise and bias properties of monoenergetic images synthesized from dual-energy CT (DECT) acquisitions. These monoenergetic images can be used to estimate attenuation coefficients at energies suitable for positron emission tomography (PET) and single-photon emission computed tomography (SPECT) imaging. This is becoming more relevant with the increased use of quantitative imaging by PET/CT and SPECT/CT scanners. There are, however, potential variations in the noise and bias of synthesized monoenergetic images as a function of energy. Methods: The authors used analytic approximations and simulations to estimate the noise and bias of synthesized monoenergetic images of water-filled cylinders with different shapes and the NURBS-based cardiac-torso (NCAT) phantom from 40 to 520 keV, the range of SPECT and PET energies. The dual-kVp spectra were based on the GE Lightspeed VCT scanner at 80 and 140 kVp with added filtration of 0.5 mm Cu. The authors evaluated strategies of noise suppression with sinogram smoothing and dose minimization with reduction of tube currents at the two kVp settings. The authors compared the impact of DECT-based attenuation correction with single-kVp CT-based attenuation correction on PET quantitation for the NCAT phantom for soft tissue and high-Z materials of bone and iodine contrast enhancement. Results: Both analytic calculations and simulations displayed the expected minimum noise value for a synthesized monoenergetic image at an energy between the mean energies of the two spectra. In addition the authors found that the normalized coefficient of variation in the synthesized attenuation map increased with energy but reached a plateau near 160 keV, and then remained constant with increasing energy up to 511 keV and beyond. The bias was minimal, as the linear attenuation coefficients of the synthesized monoenergetic images were within 2.4% of the known true values across the entire energy range. Compared with no sinogram smoothing, sinogram smoothing can dramatically reduce noise in the DECT-derived attenuation map. Through appropriate selection of tube currents for high and low kVp scans, DECT can deliver roughly the same amount of radiation dose as that of a single kVp CT scan, but could be used for PET attenuation correction with reduced bias in contrast agent regions by a factor of ∼2.6 and slightly reduced RMSE for the total image. Conclusions: When DECT is used for attenuation correction at higher energies, there is a noise amplification that is dependent on the energy of the synthesized monoenergetic image of linear attenuation coefficients. Sinogram smoothing reduces the noise amplification in DECT-derived attenuation maps without increasing bias. With an appropriate selection of CT techniques, a DECT scan with the same radiation dose as a single CT scan can result in a PET image with improved quantitative accuracy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

The impact of low-Z and high-Z metal implants in IMRT: A Monte Carlo study of dose inaccuracies in commercial dose algorithms

Purpose: The aim of the study was to evaluate the dosimetric impact of low-Z and high-Z metallic implants on IMRT plans. Methods: Computed tomography (CT) scans of three patients were analyzed to study effects due to the presence of Titanium (low-Z), Platinum and Gold (high-Z) inserts. To eliminate artifacts in CT images, a sinogram-based metal artifact reduction algorithm was applied. IMRT dose calculations were performed on both the uncorrected and corrected images using a commercial planning system (convolution/superposition algorithm) and an in-house Monte Carlo platform. Dose differences between uncorrected and corrected datasets were computed and analyzed using gamma index (Pγ{sub <1}) and setting 2 mm and 2% as distance to agreement and dose difference criteria, respectively. Beam specific depth dose profiles across the metal were also examined. Results: Dose discrepancies between corrected and uncorrected datasets were not significant for low-Z material. High-Z materials caused under-dosage of 20%–25% in the region surrounding the metal and over dosage of 10%–15% downstream of the hardware. Gamma index test yielded Pγ{sub <1}>99% for all low-Z cases; while for high-Z cases it returned 91% < Pγ{sub <1}< 99%. Analysis of the depth dose curve of a single beam for low-Z cases revealed that, although the dose attenuation is altered inside the metal, it does not differ downstream of the insert. However, for high-Z metal implants the dose is increased up to 10%–12% around the insert. In addition, Monte Carlo method was more sensitive to the presence of metal inserts than superposition/convolution algorithm. Conclusions: The reduction in terms of dose of metal artifacts in CT images is relevant for high-Z implants. In this case, dose distribution should be calculated using Monte Carlo algorithms, given their superior accuracy in dose modeling in and around the metal. In addition, the knowledge of the composition of metal inserts improves the accuracy of the Monte Carlo dose calculation significantly.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2013

Inter-slice bidirectional registration-based segmentation of the prostate gland in MR and CT image sequences

Purpose: Accurate segmentation and volume estimation of the prostate gland in magnetic resonance (MR) and computed tomography (CT) images are necessary steps in diagnosis, treatment, and monitoring of prostate cancer. This paper presents an algorithm for the prostate gland volume estimation based on the semiautomated segmentation of individual slices in T2-weighted MR and CT image sequences. Methods: The proposedInter-Slice Bidirectional Registration-based Segmentation (iBRS) algorithm relies on interslice image registration of volume data to segment the prostate gland without the use of an anatomical atlas. It requires the user to mark only three slices in a given volume dataset, i.e., the first, middle, and last slices. Next, the proposed algorithm uses a registration algorithm to autosegment the remaining slices. We conducted comprehensive experiments to measure the performance of the proposed algorithm using three registration methods (i.e., rigid, affine, and nonrigid techniques). Results: The results with the proposed technique were compared with manual marking using prostate MR and CT images from 117 patients. Manual marking was performed by an expert user for all 117 patients. The median accuracies for individual slices measured using the Dice similarity coefficient (DSC) were 92% and 91% for MR and CT images, respectively. The iBRS algorithm was also evaluated regarding user variability, which confirmed that the algorithm was robust to interuser variability when marking the prostate gland. Conclusions: The proposed algorithm exploits the interslice data redundancy of the images in a volume dataset of MR and CT images and eliminates the need for an atlas, minimizing the computational cost while producing highly accurate results which are robust to interuser variability.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Realistic simulation of reduced-dose CT with noise modeling and sinogram synthesis using DICOM CT images

Purpose: Reducing the patient dose while maintaining the diagnostic image quality during CT exams is the subject of a growing number of studies, in which simulations of reduced-dose CT with patient data have been used as an effective technique when exploring the potential of various dose reduction techniques. Difficulties in accessing raw sinogram data, however, have restricted the use of this technique to a limited number of institutions. Here, we present a novel reduced-dose CT simulation technique which provides realistic low-dose images without the requirement of raw sinogram data. Methods: Two key characteristics of CT systems, the noise equivalent quanta (NEQ) and the algorithmic modulation transfer function (MTF), were measured for various combinations of object attenuation and tube currents by analyzing the noise power spectrum (NPS) of CT images obtained with a set of phantoms. Those measurements were used to develop a comprehensive CT noise model covering the reduced x-ray photon flux, object attenuation, system noise, and bow-tie filter, which was then employed to generate a simulated noise sinogram for the reduced-dose condition with the use of a synthetic sinogram generated from a reference CT image. The simulated noise sinogram was filtered with the algorithmic MTF and back-projected to create a noise CT image, which was then added to the reference CT image, finally providing a simulated reduced-dose CT image. The simulation performance was evaluated in terms of the degree of NPS similarity, the noise magnitude, the bow-tie filter effect, and the streak noise pattern at photon starvation sites with the set of phantom images. Results: The simulation results showed good agreement with actual low-dose CT images in terms of their visual appearance and in a quantitative evaluation test. The magnitude and shape of the NPS curves of the simulated low-dose images agreed well with those of real low-dose images, showing discrepancies of less than +/−3.2% in terms of the noise power at the peak height and +/−1.2% in terms of the spatial frequency at the peak height. The magnitudes of the noise measured for 12 different combinations the phantom size, tube current, and reconstruction kernel for the simulated and real low-dose images were very similar, with differences of 0.1 to 4.7%. Thep value for a statistical testing of the difference in the noise magnitude ranged from 0.99 to 0.11, showing that there was no difference statistically between the noise magnitudes of the real and simulated low-dose images using our method. The strength and pattern of the streak noise in an anthropomorphic phantom was also consistent with expectations. Conclusions: A novel reduced-dose CT simulation technique was developed which uses only CT images while not requiring raw sinogram data. Our method can provide realistic simulation results under reduced-dose conditions both in terms of the noise magnitude and the textual appearance. This technique has the potential to promote clinical research for patient dose reductions.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Computerized detection of breast cancer on automated breast ultrasound imaging of women with dense breasts

Purpose: Develop a computer-aided detection method and investigate its feasibility for detection of breast cancer in automated 3D ultrasound images of women with dense breasts. Methods: The HIPAA compliant study involved a dataset of volumetric ultrasound image data, “views,” acquired with an automated U-Systems Somo•V{sup ®} ABUS system for 185 asymptomatic women with dense breasts (BI-RADS Composition/Density 3 or 4). For each patient, three whole-breast views (3D image volumes) per breast were acquired. A total of 52 patients had breast cancer (61 cancers), diagnosed through any follow-up at most 365 days after the original screening mammogram. Thirty-one of these patients (32 cancers) had a screening-mammogram with a clinically assigned BI-RADS Assessment Category 1 or 2, i.e., were mammographically negative. All software used for analysis was developed in-house and involved 3 steps: (1) detection of initial tumor candidates, (2) characterization of candidates, and (3) elimination of false-positive candidates. Performance was assessed by calculating the cancer detection sensitivity as a function of the number of “marks” (detections) per view. Results: At a single mark per view, i.e., six marks per patient, the median detection sensitivity by cancer was 50.0% (16/32) ± 6% for patients with a screening mammogram-assigned BI-RADS category 1 or 2—similar to radiologists’ performance sensitivity (49.9%) for this dataset from a prior reader study—and 45.9% (28/61) ± 4% for all patients. Conclusions: Promising detection sensitivity was obtained for the computer on a 3D ultrasound dataset of women with dense breasts at a rate of false-positive detections that may be acceptable for clinical implementation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Improved UTE-based attenuation correction for cranial PET-MR using dynamic magnetic field monitoring

Purpose: Ultrashort echo time (UTE) MRI has been proposed as a way to produce segmented attenuation maps for PET, as it provides contrast between bone, air, and soft tissue. However, UTE sequences require samples to be acquired during rapidly changing gradient fields, which makes the resulting images prone to eddy current artifacts. In this work it is demonstrated that this can lead to misclassification of tissues in segmented attenuation maps (AC maps) and that these effects can be corrected for by measuring the true k-space trajectories using a magnetic field camera. Methods: The k-space trajectories during a dual echo UTE sequence were measured using a dynamic magnetic field camera. UTE images were reconstructed using nominal trajectories and again using the measured trajectories. A numerical phantom was used to demonstrate the effect of reconstructing with incorrect trajectories. Images of an ovine leg phantom were reconstructed and segmented and the resulting attenuation maps were compared to a segmented map derived from a CT scan of the same phantom, using the Dice similarity measure. The feasibility of the proposed method was demonstrated inin vivo cranial imaging in five healthy volunteers. Simulated PET data were generated for one volunteer to show the impact of misclassifications on the PET reconstruction. Results: Images of the numerical phantom exhibited blurring and edge artifacts on the bone–tissue and air–tissue interfaces when nominal k-space trajectories were used, leading to misclassification of soft tissue as bone and misclassification of bone as air. Images of the tissue phantom and thein vivo cranial images exhibited the same artifacts. The artifacts were greatly reduced when the measured trajectories were used. For the tissue phantom, the Dice coefficient for bone in MR relative to CT was 0.616 using the nominal trajectories and 0.814 using the measured trajectories. The Dice coefficients for soft tissue were 0.933 and 0.934 for the nominal and measured cases, respectively. For air the corresponding figures were 0.991 and 0.993. Compared to an unattenuated reference image, the mean error in simulated PET uptake in the brain was 9.16% when AC maps derived from nominal trajectories was used, with errors in the SUV{sub max} for simulated lesions in the range of 7.17%–12.19%. Corresponding figures when AC maps derived from measured trajectories were used were 0.34% (mean error) and −0.21% to +1.81% (lesions). Conclusions: Eddy current artifacts in UTE imaging can be corrected for by measuring the true k-space trajectories during a calibration scan and using them in subsequent image reconstructions. This improves the accuracy of segmented PET attenuation maps derived from UTE sequences and subsequent PET reconstruction.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

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

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

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Prior-based artifact correction (PBAC) in computed tomography

Purpose: Image quality in computed tomography (CT) often suffers from artifacts which may reduce the diagnostic value of the image. In many cases, these artifacts result from missing or corrupt regions in the projection data, e.g., in the case of metal, truncation, and limited angle artifacts. The authors propose a generalized correction method for different kinds of artifacts resulting from missing or corrupt data by making use of available prior knowledge to perform data completion. Methods: The proposed prior-based artifact correction (PBAC) method requires prior knowledge in form of a planning CT of the same patient or in form of a CT scan of a different patient showing the same body region. In both cases, the prior image is registered to the patient image using a deformable transformation. The registered prior is forward projected and data completion of the patient projections is performed using smooth sinogram inpainting. The obtained projection data are used to reconstruct the corrected image. Results: The authors investigate metal and truncation artifacts in patient data sets acquired with a clinical CT and limited angle artifacts in an anthropomorphic head phantom data set acquired with a gantry-based flat detector CT device. In all cases, the corrected images obtained by PBAC are nearly artifact-free. Compared to conventional correction methods, PBAC achieves better artifact suppression while preserving the patient-specific anatomy at the same time. Further, the authors show that prominent anatomical details in the prior image seem to have only minor impact on the correction result. Conclusions: The results show that PBAC has the potential to effectively correct for metal, truncation, and limited angle artifacts if adequate prior data are available. Since the proposed method makes use of a generalized algorithm, PBAC may also be applicable to other artifacts resulting from missing or corrupt data.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

EM reconstruction of dual isotope PET using staggered injections and prompt gamma positron emitters

Purpose: The aim of dual isotope positron emission tomography (DIPET) is to create two separate images of two coinjected PET radiotracers. DIPET shortens the duration of the study, reduces patient discomfort, and produces perfectly coregistered images compared to the case when two radiotracers would be imaged independently (sequential PET studies). Reconstruction of data from such simultaneous acquisition of two PET radiotracers is difficult because positron decay of any isotope creates only 511 keV photons; therefore, the isotopes cannot be differentiated based on the detected energy. Methods: Recently, the authors have proposed a DIPET technique that uses a combination of radiotracer A which is a pure positron emitter (such as{sup 18}F or {sup 11}C) and radiotracer B in which positron decay is accompanied by the emission of a high-energy (HE) prompt gamma (such as {sup 38}K or {sup 60}Cu). Events that are detected as triple coincidences of HE gammas with the corresponding two 511 keV photons allow the authors to identify the lines-of-response (LORs) of isotope B. These LORs are used to separate the two intertwined distributions, using a dedicated image reconstruction algorithm. In this work the authors propose a new version of the DIPET EM-based reconstruction algorithm that allows the authors to include an additional, independent estimate of radiotracer A distribution which may be obtained if radioisotopes are administered using a staggered injections method. In this work the method is tested on simple simulations of static PET acquisitions. Results: The authors’ experiments performed using Monte-Carlo simulations with static acquisitions demonstrate that the combined method provides better results (crosstalk errors decrease by up to 50%) than the positron-gamma DIPET method or staggered injections alone. Conclusions: The authors demonstrate that the authors’ new EM algorithm which combines information from triple coincidences with prompt gammas and staggered injections improves the accuracy of DIPET reconstructions for static acquisitions so they reach almost the benchmark level calculated for perfectly separated tracers.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Searching for Subsecond Stellar Variability with Wide-field Star Trails and Deep Learning

We present here a method that enables wide-field ground-based telescopes to scan the sky for subsecond stellar variability. The method has operational and image processing components. The operational component takes star trail images. Each trail serves as a light curve for its corresponding source and facilitates subexposure photometry. We train a deep neural network to identify stellar variability in wide-field star trail images. We use the Large Synoptic Survey Telescope Photon Simulator to generate simulated star trail images and include transient bursts as a proxy for variability. The network identifies transient bursts on timescales down to 10 ms. We argue that there are multiple fields of astrophysics that can be advanced by the unique combination of time resolution and observing throughput that our method offers.

79 ASTRONOMY AND ASTROPHYSICS↗