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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

SU-F-J-195: On the Performance of Four Dual Energy CT Formalisms for Extracting Proton Stopping Powers

Purpose: Dual energy CT can predict stopping power ratios (SPR) for ion therapy treatment planning. Several approaches have been proposed recently, however accuracy and practicability in a clinical workflow are unaddressed. The aim of this work is to provide a fair comparison of available approaches in a human-like phantom to find the optimal method for tissue characterization in a clinical situation. Methods: The SPR determination accuracy is investigated using simulated DECT images. A virtual human-like phantom is created containing 14 different standard human tissues. SECT (120 kV) and DECT images (100 kV and 140 kV Sn) are simulated using the software ImaSim. The single energy CT (SECT) stoichiometric calibration method and four recently published calibration-based DECT methods are implemented and used to predict the SPRs from simulated images. The difference between SPR predictions and theoretical SPR are compared pixelwize. Mean, standard deviation and skewness of the SPR difference distributions are used as measures for bias, dispersion and symmetry. Results: The average SPR differences and standard deviations are (0.22 ± 1.27)% for SECT, and A) (−0.26 ± 1.30)%, B) (0.08 ± 1.12)%, C) (0.06 ± 1.15)% and D) (−0.05 ± 1.05)% for the four DECT methods. While SPR prediction using SECT is showing a systematic error on SPR, the DECT methods B, C and D are unbiased. The skewness of the SECT distribution is 0.57%, and A) −0.19%, B) −0.56%, C) −0.29% and D) −0.07% for DECT methods respectively. Conclusion: The here presented DECT methods B, C and D outperform the commonly used SECT stoichiometric calibration. These methods predict SPR accurately without a bias and within ± 1.2% (68th percentile). This indicates that DECT potentially improves accuracy of range predictions in proton therapy. A validation of these findings using clinical CT images of real tissues is necessary.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Analysis of the KROTOS KFC test by coupling X-Ray image analysis and MC3D calculations

During a hypothetical severe accident sequence in a Pressurized Water Reactor (PWR), the hot molten materials (corium) issuing from the degraded reactor core may generate a steam explosion if they come in contact with water and may damage the structures and threaten the reactor integrity. The SERENA program is an international OECD project that aims at helping the understanding of this phenomenon also called Fuel Coolant Interaction (FCI) by providing data. CEA takes part in this program by performing tests in its KROTOS facility where steam explosions using prototypic corium can be triggered. Data about the different phases in the premixing are extracted from the KROTOS X-Ray radioscopy images by using KIWI software (KROTOS Image analysis of Water-corium Interaction) currently developed by CEA. The MC3D code, developed by IRSN, is a thermal-hydraulic multiphase code mainly dedicated to FCI studies. It is composed of two applications: premixing and explosion. An overall FCI calculation with MC3D requires a premixing calculation followed by an explosion calculation. The present paper proposes an alternative approach in which all the features of the premixing are extracted from the X-Ray pictures using the KIWI software and transferred to an MC3D dataset for a direct simulation of the explosion. The main hypothesis are discussed as well as the first explosion results obtained with MC3D for the KROTOS KFC test. These results are rather encouraging and are analyzed on the basis of comparisons with the experimental data. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2014

Free software for performing physical analysis of systems for digital radiography and mammography

Purpose: In this paper, the authors present a free software for assisting users in achieving the physical characterization of x-ray digital systems and image quality checks. Methods: The program was developed as a plugin of a well-known public-domain suite ImageJ. The software can assist users in calculating various physical parameters such as the response curve (also termed signal transfer property), modulation transfer function (MTF), noise power spectra (NPS), and detective quantum efficiency (DQE). It also includes the computation of some image quality checks: defective pixel analysis, uniformity, dark analysis, and lag. Results: The software was made available in 2009 and has been used during the last couple of years by many users who gave us valuable feedback for improving its usability. It was tested for achieving the physical characterization of several clinical systems for digital radiography and mammography. Various published papers made use of the outcomes of the plugin. Conclusions: This software is potentially beneficial to a variety of users: physicists working in hospitals, staff working in radiological departments, such as medical physicists, physicians, engineers. The plugin, together with a brief user manual, are freely available and can be found online ( http://www.medphys.it/downloads.htm ). With our plugin users can estimate all three most important parameters used for physical characterization (MTF, NPS, and also DQE). The plugin can run on any operating system equipped with ImageJ suite. The authors validated the software by comparing MTF and NPS curves on a common set of images with those obtained with other dedicated programs, achieving a very good agreement.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

SU-G-JeP4-12: Real-Time Organ Motion Monitoring Using Ultrasound and KV Fluoroscopy During Lung SBRT Delivery

Purpose: Real-time ultrasound monitoring during SBRT is advantageous in understanding and identifying motion irregularities which may cause geometric misses. In this work, we propose to utilize real-time ultrasound to track the diaphragm in conjunction with periodical kV fluoroscopy to monitor motion of tumor or landmarks during SBRT delivery. Methods: Transabdominal Ultrasound (TAUS) b-mode images were collected from 10 healthy volunteers using the Clarity Autoscan System (Elekta). The autoscan transducer, which has a center frequency of 5 MHz, was utilized for the scans. The acquired images were contoured using the Clarity Automatic Fusion and Contouring workstation software. Monitoring sessions of 5 minute length were observed and recorded. The position correlation between tumor and diaphragm could be established with periodic kV fluoroscopy periodically acquired during treatment with Elekta XVI. We acquired data using a tissue mimicking ultrasound phantom with embedded spheres placed on a motion stand using ultrasound and kV Fluoroscopy. MIM software was utilized for image fusion. Correlation of diaphragm and target motion was also validated using 4D-MRI and 4D-CBCT. Results: The diaphragm was visualized as a hyperechoic region on the TAUS b-mode images. Volunteer set-up can be adjusted such that TAUS probe will not interfere with treatment beams. A segment of the diaphragm was contoured and selected as our tracking structure. Successful monitoring sessions of the diaphragm were recorded. For some volunteers, diaphragm motion over 2 times larger than the initial motion has been observed during tracking. For the phantom study, we were able to register the 2D kV Fluoroscopy with the US images for position comparison. Conclusion: We demonstrated the feasibility of tracking the diaphragm using real-time ultrasound. Real-time tracking can help in identifying such irregularities in the respiratory motion which is correlated to tumor motion. We also showed the feasibility of acquiring 2D KV Fluoroscopy and registering the images with Ultrasound.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

SU-E-J-09: Image Quality Comparison and Dose Quantification for 2.5 MV

Purpose: To compare the image quality of the 2.5MV imaging beam (2.5X-IMB) to that of a 6MV beam and to quantify the imaging dose of a 2.5X-IMB for constancy as specified by AAPM TG-142 Methods: The image quality of the 2.5X-IMB was compared to the 6MV imaging beam using the SNC ImagePro MV-QA phantom and the Varian supplied Las Vegas phantom (LVP). High resolution (1280×1280×16, 2 frames at 1.5MU/frame) and low resolution (640×640×16, 2 frames at 0.75MU/frame) images were compared for each phantom. MV-QA phantom images were evaluated quantitatively, and the LVP images were evaluated qualitatively. The imaging dose for 2.5X-IMB was quantified using the procedure outlined in TG51. PTWCC13-31013 chambers were used to measure a percent depth dose (PDD) curve for the 2.5X-IMB. All the factors described in TG51 were calculated using the 2.5X-IMB and a PTW30013 farmer chamber. Results: A comparison between 2.5X-IMB and 6MV image quality was performed both visually and with DoseLab software. The optimal window and level were set for each image of the LVP by the user. Visual inspection showed greater contrast resolution with the 2.5MV beam, but no significant difference with the change in imaging resolution. DoseLab reported similar spatial resolutions between the two energies, but the contrast-to-noise ratio (CNR) was greater for 2.5MV. The PDDx(10cm) for a 10x10cm2 field was measured to be 51.5%. Although this PDD value is off the scale of Figure 4 in TG51, the trend of the curve corresponding to the PTW31003 (equivalent) chamber led to an approximate kQ value of 1.00. Conclusion: When compared to 6MV imaging, 2.5X-IMB results in a better CNR. At low resolution, the DoseLab results for the two energies are comparable, but visual analysis favors the 2.5X-IMB images. Imaging dose was quantified for the 2.5X-IMB after following the TG51 methodology with appropriate approximations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Sparse coding of pathology slides compared to transfer learning with deep neural networks

Background Histopathology images of tumor biopsies present unique challenges for applying machine learning to the diagnosis and treatment of cancer. The pathology slides are high resolution, often exceeding 1GB, have non-uniform dimensions, and often contain multiple tissue slices of varying sizes surrounded by large empty regions. The locations of abnormal or cancerous cells, which may constitute a small portion of any given tissue sample, are not annotated. Cancer image datasets are also extremely imbalanced, with most slides being associated with relatively common cancers. Since deep representations trained on natural photographs are unlikely to be optimal for classifying pathology slide images, which have different spectral ranges and spatial structure, we here describe an approach for learning features and inferring representations of cancer pathology slides based on sparse coding. Results We show that conventional transfer learning using a state-of-the-art deep learning architecture pre-trained on ImageNet (RESNET) and fine tuned for a binary tumor/no-tumor classification task achieved between 85% and 86% accuracy. However, when all layers up to the last convolutional layer in RESNET are replaced with a single feature map inferred via a sparse coding using a dictionary optimized for sparse reconstruction of unlabeled pathology slides, classification performance improves to over 93%, corresponding to a 54% error reduction. Conclusions We conclude that a feature dictionary optimized for biomedical imagery may in general support better classification performance than does conventional transfer learning using a dictionary pre-trained on natural images.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

SU-E-J-129: A Strategy to Consolidate the Image Database of a VERO Unit Into a Radiotherapy Management System

Purpose: To present a strategy to integrate the imaging database of a VERO unit with a treatment management system (TMS) to improve clinical workflow and consolidate image data to facilitate clinical quality control and documentation. Methods: A VERO unit is equipped with both kV and MV imaging capabilities for IGRT treatments. It has its own imaging database behind a firewall. It has been a challenge to transfer images on this unit to a TMS in a radiation therapy clinic so that registered images can be reviewed remotely with an approval or rejection record. In this study, a software system, iPump-VERO, was developed to connect VERO and a TMS in our clinic. The patient database folder on the VERO unit was mapped to a read-only folder on a file server outside VERO firewall. The application runs on a regular computer with the read access to the patient database folder. It finds the latest registered images and fuses them in one of six predefined patterns before sends them via DICOM connection to the TMS. The residual image registration errors will be overlaid on the fused image to facilitate image review. Results: The fused images of either registered kV planar images or CBCT images are fully DICOM compatible. A sentinel module is built to sense new registered images with negligible computing resources from the VERO ExacTrac imaging computer. It takes a few seconds to fuse registered images and send them to the TMS. The whole process is automated without any human intervention. Conclusion: Transferring images in DICOM connection is the easiest way to consolidate images of various sources in your TMS. Technically the attending does not have to go to the VERO treatment console to review image registration prior delivery. It is a useful tool for a busy clinic with a VERO unit.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-J-224: Multimodality Segmentation of Head and Neck Tumors

Purpose: Develop an algorithm that is able to automatically segment tumor volume in Head and Neck cancer by integrating information from CT, PET and MR imaging simultaneously. Methods: Twenty three patients that were recruited under an adaptive radiotherapy protocol had MR, CT and PET/CT scans within 2 months prior to start of radiotherapy. The patients had unresectable disease and were treated either with chemoradiotherapy or radiation therapy alone. Using the Velocity software, the PET/CT and MR (T1 weighted+contrast) scans were registered to the planning CT using deformable and rigid registration respectively. The PET and MR images were then resampled according to the registration to match the planning CT. The resampled images, together with the planning CT, were fed into a multi-channel segmentation algorithm, which is based on Gaussian mixture models and solved with the expectation-maximization algorithm and Markov random fields. A rectangular region of interest (ROI) was manually placed to identify the tumor area and facilitate the segmentation process. The auto-segmented tumor contours were compared with the gross tumor volume (GTV) manually defined by the physician. The volume difference and Dice similarity coefficient (DSC) between the manual and autosegmented GTV contours were calculated as the quantitative evaluation metrics. Results: The multimodality segmentation algorithm was applied to all 23 patients. The volumes of the auto-segmented GTV ranged from 18.4cc to 32.8cc. The average (range) volume difference between the manual and auto-segmented GTV was −42% (−32.8%–63.8%). The average DSC value was 0.62, ranging from 0.39 to 0.78. Conclusion: An algorithm for the automated definition of tumor volume using multiple imaging modalities simultaneously was successfully developed and implemented for Head and Neck cancer. This development along with more accurate registration algorithms can aid physicians in the efforts to interpret the multitude of imaging information available in radiotherapy today. This project was supported by a grant by Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

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

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

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Efficient reconstruction and validation of heterogeneous microstructures for energy applications

The digital reconstruction of microstructures is necessary for simulations in fields ranging from geology to electrochemistry, but the state-of-the-art digital reconstruction techniques often compromise between resolution and field of view. It is challenging to retain detailed microstructure information in large-scale reconstructions. Here, this study investigates different aspects of the Yeong-Torquato algorithm based on correlation functions to make it more efficient. We achieve this goal by reducing the computational complexity of the chord-length distribution function and the two-point correlation function, applying the random sphere-packing method as the initial condition, and restricting potential voxel swaps to interfaces. In addition, a novel superposition parallel scheme is introduced to aid in searching for potential voxel swaps. The algorithm proposed is validated by comparing the pore-size distributions of reconstructed 3D custom battery electrodes from a sample dataset obtained from transmission X-ray microscopy. From a sample image with 200 x 200 pixels, the code can reconstruct a 300 x 300 x 300 structure in under 22 h and reconstruct a 400 x 400 x 400 structure in 43 h with eight cores.

42 ENGINEERING↗
DOE OSTI2020

Development of a predictive capability of short-pulse laser-driven broadband x-ray radiography

High intensity, short-pulse laser interaction with a solid metal target produces broadband hard x-rays potentially for various applications of x-ray radiography. In this work, experimental benchmarking of numerical modelling for short-pulse laser-driven broadband x-ray radiography is presented. Angular dependent x-ray spectra are first calculated with a hybrid particle-in-cell code, Large Scale Plasma (LSP), using fast electron parameters inferred from an analysis of measured bremsstrahlung signals. Subsequently, a calculated x-ray spectrum in the direction of radiography is used in photon transport calculations using a Monte Carlo code, Particle and Heavy Ion Transport code System (PHITS), to simulate a radiographic image including a modelled 3D test object, an x-ray attenuation filter and an image plate detector. Simulated radiographic images are compared with measurements obtained in an experiment using a 50-TW Leopard short-pulse laser at the University of Nevada Reno. Results show that simulations reproduce the experimental images well for three different attenuation filters (plastic, aluminium, and brass), while 1D transmission profiles for the plastic and aluminium filters are quantitatively in good agreement. The modelling approach established in this work could be used as a predictive tool to simulate radiographic images of complex 3D solid objects at any arbitrary angular position or to optimize experimental components such as the source spectrum, x-ray attenuation filters and a detector type depending on a radiographic object without carrying out radiographic experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

Iterative Reconstruction of Coded Source Neutron Radiographs

Use of a coded source facilitates high-resolution neutron imaging through magnifications but requires that the radiographic data be deconvolved. A comparison of direct deconvolution with two different iterative algorithms has been performed. One iterative algorithm is based on a maximum likelihood estimation (MLE)-like framework and the second is based on a geometric model of the neutron beam within a least squares formulation of the inverse imaging problem. Simulated data for both uniform and Gaussian shaped source distributions was used for testing to understand the impact of non-uniformities present in neutron beam distributions on the reconstructed images. Results indicate that the model based reconstruction method will match resolution and improve on contrast over convolution methods in the presence of non-uniform sources. Additionally, the model based iterative algorithm provides direct calculation of quantitative transmission values while the convolution based methods must be normalized base on known values.

Santos-Villalobos, Hector J↗
DOE OSTI2023

Feature Interpretability

The feature interpretability code is a python module that interprets and analyzes neural networks trained on hydrodynamic simulation output in the form of numpy arrays. The code takes trained neural networks and extracts internal model states in the form of images. Additionally, tools for covariance analysis of network weights and predictions are provided. This code is built on the TensorFlow and PyTorch python libraries, and includes trained networks and example input data for demonstration purposes.

Callis, Skylar↗
DOE OSTI2017

The evolution of star formation activity in cluster galaxies over 0.15 < z < 1.5

We explore 7.5 billion years of evolution in the star formation activity of massive (M{sub ⋆}>10{sup 10.1} M{sub ⊙}) cluster galaxies using a sample of 25 clusters over 0.15<z<1 from the Cluster Lensing And Supernova survey with Hubble and 11 clusters over 1<z<1.5 from the IRAC Shallow Cluster Survey. Galaxy morphologies are determined visually using high-resolution Hubble Space Telescope images. Using the spectral energy distribution fitting code Code Investigating GALaxy Emission, we measure star formation rates, stellar masses, and 4000 Å break strengths. The latter are used to separate quiescent and star-forming galaxies (SFGs). From z∼1.3 to z∼0.2, the specific star formation rate (sSFR) of cluster SFGs and quiescent galaxies decreases by factors of three and four, respectively. Over the same redshift range, the sSFR of the entire cluster population declines by a factor of 11, from 0.48±0.06 Gyr{sup −1} to 0.043±0.009 Gyr{sup −1}. This strong overall sSFR evolution is driven by the growth of the quiescent population over time; the fraction of quiescent cluster galaxies increases from 28{sub −19}{sup +8}% to 88{sub −4}{sup +5}% over z ∼ 1.3 to 0.2. The majority of the growth occurs at z≳0.9, where the quiescent fraction increases by 0.41. While the sSFR of the majority of star-forming cluster galaxies is at the level of the field, a small subset of cluster SFGs have low field-relative star formation activity, suggestive of long-timescale quenching. The large increase in the fraction of quiescent galaxies above z∼0.9, coupled with the field-level sSFRs of cluster SFGs, suggests that higher-redshift cluster galaxies are likely being quenched quickly. Assessing those timescales will require more accurate stellar population ages and star formation histories.

79 ASTRONOMY AND ASTROPHYSICS↗
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 OSTI2017

Comparison of Two Coronal Magnetic Field Models to Reconstruct a Sigmoidal Solar Active Region with Coronal Loops

Magnetic field extrapolation is an important tool to study the three-dimensional (3D) solar coronal magnetic field, which is difficult to directly measure. Various analytic models and numerical codes exist, but their results often drastically differ. Thus, a critical comparison of the modeled magnetic field lines with the observed coronal loops is strongly required to establish the credibility of the model. Here we compare two different non-potential extrapolation codes, a nonlinear force-free field code (CESE–MHD–NLFFF) and a non-force-free field (NFFF) code, in modeling a solar active region (AR) that has a sigmoidal configuration just before a major flare erupted from the region. A 2D coronal-loop tracing and fitting method is employed to study the 3D misalignment angles between the extrapolated magnetic field lines and the EUV loops as imaged by SDO /AIA. It is found that the CESE–MHD–NLFFF code with preprocessed magnetogram performs the best, outputting a field that matches the coronal loops in the AR core imaged in AIA 94 Å with a misalignment angle of ∼10°. This suggests that the CESE–MHD–NLFFF code, even without using the information of the coronal loops in constraining the magnetic field, performs as good as some coronal-loop forward-fitting models. For the loops as imaged by AIA 171 Å in the outskirts of the AR, all the codes including the potential field give comparable results of the mean misalignment angle (∼30°). Thus, further improvement of the codes is needed for a better reconstruction of the long loops enveloping the core region.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

The SRT reconstruction algorithm for semiquantification in PET imaging

Purpose: The spline reconstruction technique (SRT) is a new, fast algorithm based on a novel numerical implementation of an analytic representation of the inverse Radon transform. The mathematical details of this algorithm and comparisons with filtered backprojection were presented earlier in the literature. In this study, the authors present a comparison between SRT and the ordered-subsets expectation–maximization (OSEM) algorithm for determining contrast and semiquantitative indices of {sup 18}F-FDG uptake. Methods: The authors implemented SRT in the software for tomographic image reconstruction (STIR) open-source platform and evaluated this technique using simulated and real sinograms obtained from the GE Discovery ST positron emission tomography/computer tomography scanner. All simulations and reconstructions were performed in STIR. For OSEM, the authors used the clinical protocol of their scanner, namely, 21 subsets and two iterations. The authors also examined images at one, four, six, and ten iterations. For the simulation studies, the authors analyzed an image-quality phantom with cold and hot lesions. Two different versions of the phantom were employed at two different hot-sphere lesion-to-background ratios (LBRs), namely, 2:1 and 4:1. For each noiseless sinogram, 20 Poisson realizations were created at five different noise levels. In addition to making visual comparisons of the reconstructed images, the authors determined contrast and bias as a function of the background image roughness (IR). For the real-data studies, sinograms of an image-quality phantom simulating the human torso were employed. The authors determined contrast and LBR as a function of the background IR. Finally, the authors present plots of contrast as a function of IR after smoothing each reconstructed image with Gaussian filters of six different sizes. Statistical significance was determined by employing the Wilcoxon rank-sum test. Results: In both simulated and real studies, SRT exhibits higher contrast and lower bias than OSEM at the cold lesions. This improvement is achieved at the expense of increasing the noise in the reconstructed images. For the hot lesions, SRT exhibits a small improvement in contrast and LBR over OSEM with 21 subsets and two iterations; however, this improvement is not statistically significant. As the number of iterations increases, the performance of OSEM improves over SRT but again without statistical significance. The curves of contrast and LBR as a function of IR after Gaussian blurring indicate that the advantage of SRT in the cold regions is maintained even after decreasing the noise level by Gaussian blurring. Conclusions: SRT, at the expense of slightly increased noise in the reconstructed images, reconstructs images of higher contrast and lower bias than the clinical protocol of OSEM. This improvement is particularly evident for images involving cold regions. Thus, it appears that SRT should be particularly useful for the quantification of low-count and cold regions.

60 APPLIED LIFE SCIENCES↗