Browsing by Author "Cao, Guohua"

Now showing 1 - 17 of 17
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    Calculation of the effective atomic number for the iodine contrast agent of the varying concentrations
    Pen, Olga Vladimirovna (Virginia Tech, 2016-07-25)
    The author discusses the difficulties that arise with the determination of the concentration of the iodinated contrast agents in the blood stream via the traditional gray-scale computer tomography and searches for the new imaging modalities that would provide for better sensitivity. The topic of the energy-discriminative color CT is discussed as a potential solution and its suitability is evaluated by performing the experiments on the contrast materials phantom and the phantom containing the iohexol solutions of varying concentrations on the original CT system assembled by the author. A method of the effective atomic number mapping is discussed as a viable alternative to the traditional attenuation-based tomography. The dependency of the effective atomic number of the compound on the energy of the x-ray beam is a phenomenon well recorded in the literature, yet no formal study exists to correctly predict the effective atomic number for a given compound. An extensive physical model is developed based on the previously presented models and adaptations unique to the task in order to determine the effective atomic numbers for exact energies experimentally. The method is tested on different materials. The resultant effective atomic numbers for the water, oil, and iohexol-water solutions of varying concentrations are presented in the study. The effects of the k-edge on both the linear attenuation curve and the effective atomic number curve are discussed. The possible future venues of the research are presented in the final part of the thesis.
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    ComputeCOVID19+: Accelerating COVID-19 Diagnosis and Monitoring via High-Performance Deep Learning on CT Images
    Goel, Garvit; Gondhalekar, Atharva; Qi, Jingyuan; Zhang, Zhicheng; Cao, Guohua; Feng, Wu-chun (ACM, 2021-10-05)
    The COVID-19 pandemic has highlighted the importance of diagnosis and monitoring as early and accurately as possible. However, the reverse-transcription polymerase chain reaction (RT-PCR) test results in two issues: (1) protracted turnaround time from sample collection to testing result and (2) compromised test accuracy, as low as 67%, due to when and how the samples are collected, packaged, and delivered to the lab to conduct the RT-PCR test. Thus, we present ComputeCOVID19+, our computed tomography-based framework to improve the testing speed and accuracy of COVID-19 (plus its variants) via a deep learning-based network for CT image enhancement called DDnet, short for DenseNet and Deconvolution network. To demonstrate its speed and accuracy, we evaluate ComputeCOVID19+ across several sources of computed tomography (CT) images and on many heterogeneous platforms, including multi-core CPU, many-core GPU, and even FPGA. Our results show that ComputeCOVID19+ can significantly shorten the turnaround time from days to minutes and improve the testing accuracy to 91%.
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    Deep Learning Neural Network-based Sinogram Interpolation for Sparse-View CT Reconstruction
    Vekhande, Swapnil Sudhir (Virginia Tech, 2019-06-14)
    Computed Tomography (CT) finds applications across domains like medical diagnosis, security screening, and scientific research. In medical imaging, CT allows physicians to diagnose injuries and disease more quickly and accurately than other imaging techniques. However, CT is one of the most significant contributors of radiation dose to the general population and the required radiation dose for scanning could lead to cancer. On the other hand, a shallow radiation dose could sacrifice image quality causing misdiagnosis. To reduce the radiation dose, sparse-view CT, which includes capturing a smaller number of projections, becomes a promising alternative. However, the image reconstructed from linearly interpolated views possesses severe artifacts. Recently, Deep Learning-based methods are increasingly being used to interpret the missing data by learning the nature of the image formation process. The current methods are promising but operate mostly in the image domain presumably due to lack of projection data. Another limitation is the use of simulated data with less sparsity (up to 75%). This research aims to interpolate the missing sparse-view CT in the sinogram domain using deep learning. To this end, a residual U-Net architecture has been trained with patch-wise projection data to minimize Euclidean distance between the ground truth and the interpolated sinogram. The model can generate highly sparse missing projection data. The results show improvement in SSIM and RMSE by 14% and 52% respectively with respect to the linear interpolation-based methods. Thus, experimental sparse-view CT data with 90% sparsity has been successfully interpolated while improving CT image quality.
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    Development and Applications of Interior Tomography - Multi-source Interior Tomography for Ultrafast Performance
    Wang, Ge; Ritman, Erik; Ye, Yangbo; Katsevich, Alexander; Yu, Hengyong; Cao, Guohua; Zhou, Otto (2010-04-05)
    Conventional tomography allows excellent reconstruction of an object from non-truncated projections. The long-standing interior problem is to reconstruct an interior ROI accurately only from local projection segments. Interior tomography solves the interior problem with practical knowledge such as a known sub-region or a sparsity model using compressive sensing. Advantages of interior tomography include radiation dose reduction (no x-rays go outside an ROI), scattering artifact suppression (no cross-talk from radiation outside the ROI), image quality improvement (with the novel reconstruction approach), large object handling (measurement can be truncated in any direction), and ultrafast imaging performance (with multiple source detector chains tightly integrated targeting the ROI).
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    Fast Projection Algorithm for LIM-Based Simultaneous Algebraic Reconstruction Technique and Its Parallel Implementation on GPU
    Zhang, Shunli; Geng, Guohua; Cao, Guohua; Zhang, Yuhe; Liu, Baodong; Dong, Xu (IEEE, 2018)
    Simultaneous algebraic reconstruction technique (SART) is a well-known iterative method in X-ray computed tomography, which provides better image quality than analytical methods when dealing with incomplete or noisy data. The disadvantage of SART is the slow speed compared with the analytical methods. Since forward projection and backprojection are two major time-consuming operations in iterative reconstruction, we propose an algorithm for fast forward projection and improved backprojection for the line integral model-based SART. Using the proposed algorithm, the SART method was implemented on a GPU platform with NVIDIA's parallel computing architecture. Both computer simulations and physical phantom experiments were carried out, and their results show that our approach is highly efficient and accurate. The computation time for the system matrix using our proposed projector is 10 times faster than that using the Siddon's projector, and our improved backprojection algorithm is 1.5 times faster than Li's method in determining the minimum bounding interval. The GPU-based SART using our proposed projection algorithm can obtain about 7.4 times reconstruction speed-up compared with that using the traditional projection approach, while preserving the accuracy of the results.
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    Hybrid Spectral Micro-CT: System Implementation, Exposure Reduction, K-edge Imaging Optimization, and Content Management
    Bennett, James (Virginia Tech, 2014-02-21)
    Spectral computed tomography (CT) has proven an important development in biomedical imaging, yet there are several limitations to this nascent technology. Near-term implementation of spectral CT imaging can be enhanced using a hybrid architecture that integrates a narrow-beam spectral 'interior' imaging chain integrated with a traditional wide-beam 'global' imaging chain. The first study demonstrates the feasibility of hybrid spectral micro-CT architecture with a first-of-its-kind system implementation and preliminary results showing improved contrast resolution and spatial resolution. The second study seeks to characterize the hybrid spectral micro-CT scan protocol for reduction of radiation exposure. In the third study, the spectral 'interior' imaging chain was optimized for K-edge imaging of high-z elemental contrast agents. In the final study, an open-source, low-cost solution for managing digital content in an academic setting was demonstrated. The results of these studies confirm the merits of a hybrid architecture and warrant further consideration in future pre-clinical and clinical spectral micro-CT and CT scanner design and protocols.
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    Improving Molecular Sensitivity in X-Ray Fluorescence Molecular Imaging (XFMI) of Iodine Distribution in Mouse-Sized Phantoms via Excitation Spectrum Optimization
    Dong, Xu; Chen, Cheng; Cao, Guohua (IEEE, 2018-10-25)
    X-ray fluorescence molecular imaging (XFMI) has shown great promise as a low-cost molecular imaging modality for clinical and pre-clinical applications with high sensitivity. Recently, progress has been made in enabling the XFMI technique with laboratory X-ray sources for various biomedical applications. However, the sensitivity of XFMI still needs to be improved for in vivo biomedical applications at a reasonably low radiation dose. In laboratory X-ray source-based XFMI, the main factor that limits the molecular sensitivity of XFMI is the scatter X-rays that coincide with the fluorescence X-rays from the targeted material. In this paper, we experimentally investigated the effects of excitation beam spectrum on the molecular sensitivity of XFMI, by quantitatively deriving minimum detectable concentration (MDC) under a xed surface entrance dose of 200 mR at three different excitation beam spectra. XFMI experiments were carried out on two customized mouse-sized phantoms. The result shows that the MDC can be readily increased by a factor of 5.26 via excitation spectrum optimization. Furthermore, a numerical model was developed and validated by the experimental data. The numerical model can be used to optimize XFMI system configurations to further improve the molecular sensitivity. Findings from this investigation could nd applications for in vivo pre-clinical small-animal XFMI in the future.
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    Isometric spiracular scaling in scarab beetles—implications for diffusive and advective oxygen transport
    Wagner, Julian M.; Klok, C. Jaco; Duell, Meghan E.; Socha, John J.; Cao, Guohua; Gong, Hao; Harrison, Jon F. (eLife Sciences, 2022-09-01)
    The scaling of respiratory structures has been hypothesized to be a major driving factor in the evolution of many aspects of animal physiology. Here, we provide the first assessment of the scaling of the spiracles in insects using 10 scarab beetle species differing 180× in mass, including some of the most massive extant insect species. Using X-ray microtomography, we measured the cross-sectional area and depth of all eight spiracles, enabling the calculation of their diffusive and advective capacities. Each of these metrics scaled with geometric isometry. Because diffu-sive capacities scale with lower slopes than metabolic rates, the largest beetles measured require 10-fold higher PO2 gradients across the spiracles to sustain metabolism by diffusion compared to the smallest species. Large beetles can exchange sufficient oxygen for resting metabolism by diffusion across the spiracles, but not during flight. In contrast, spiracular advective capacities scale similarly or more steeply than metabolic rates, so spiracular advective capacities should match or exceed respiratory demands in the largest beetles. These data illustrate a general principle of gas exchange: scaling of respiratory transport structures with geometric isometry diminishes the potential for diffu-sive gas exchange but enhances advective capacities; combining such structural scaling with muscle-driven ventilation allows larger animals to achieve high metabolic rates when active.
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    Joint CT-MRI Image Reconstruction
    Cui, Xuelin (Virginia Tech, 2018-11-28)
    Modern clinical diagnoses and treatments have been increasingly reliant on medical imaging techniques. In return, medical images are required to provide more accurate and detailed information than ever. Aside from the evolution of hardware and software, multimodal imaging techniques offer a promising solution to produce higher quality images by fusing medical images from different modalities. This strategy utilizes more structural and/or functional image information, thereby allowing clinical results to be more comprehensive and better interpreted. Since their inception, multimodal imaging techniques have received a great deal of attention for achieving enhanced imaging performance. In this work, a novel joint reconstruction framework using sparse computed tomography (CT) and magnetic resonance imaging (MRI) data is developed and evaluated. The method proposed in this study is part of the planned joint CT-MRI system which assembles CT and MRI subsystems into a single entity. The CT and MRI images are synchronously acquired and registered from the hybrid CT-MRI platform. However, since their image data are highly undersampled, analytical methods, such as filtered backprojection, are unable to generate images of sufficient quality. To overcome this drawback, we resort to compressed sensing techniques, which employ sparse priors that result from an application of L₁-norm minimization. To utilize multimodal information, a projection distance is introduced and is tuned to tailor the texture and pattern of final images. Specifically CT and MRI images are alternately reconstructed using the updated multimodal results that are calculated at the latest step of the iterative optimization algorithm. This method exploits the structural similarities shared by the CT and MRI images to achieve better reconstruction quality. The improved performance of the proposed approach is demonstrated using a pair of undersampled CT-MRI body images and a pair of undersampled CT-MRI head images. These images are tested using joint reconstruction, analytical reconstruction, and independent reconstruction without using multimodal imaging information. Results show that the proposed method improves about 5dB in signal-to-noise ratio (SNR) and nearly 10% in structural similarity measurements compared to independent reconstruction methods. It offers a similar quality as fully sampled analytical reconstruction, yet requires as few as 25 projections for CT and a 30% sampling rate for MRI. It is concluded that structural similarities and correlations residing in images from different modalities are useful to mutually promote the quality of image reconstruction.
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    Material-Specific Computed Tomography for Molecular X-Imaging in Biomedical Research
    Dong, Xu (Virginia Tech, 2019-04-08)
    X-ray Computed Tomography (CT) imaging has been playing a central role in clinical practice since it was invented in 1972. However, the traditional x-ray CT technique fails to distinguish different materials with similar density, especially for biological tissues. The lack of a quantitative imaging representation has constrained the application of CT technique from a broadening application such as personal or precision medicine. Therefore, my major thesis statement is to develop novel material-specific CT imaging techniques for molecular imaging in biological bodies. To achieve the goal, comprehensive studies were conducted to investigate three different techniques: x-ray fluorescence molecular imaging, material identification (specification) from photon counting CT, and photon counting CT data distortion correction approach based on deep learning. X-ray fluorescence molecular imaging (XFMI) has shown great promise as a low-cost molecular imaging modality for clinical and pre-clinical applications with high sensitivity. In this study, the effects of excitation beam spectrum on the molecular sensitivity of XFMI were experimentally investigated, by quantitatively deriving minimum detectable concentration (MDC) under a fixed surface entrance dose of 200 mR at three different excitation beam spectra. The result shows that the MDC can be readily increased by a factor of 5.26 via excitation spectrum optimization. Furthermore, a numerical model was developed and validated by the experimental data (≥0.976). The numerical model can be used to optimize XFMI system configurations to further improve the molecular sensitivity. Findings from this investigation could find applications for in vivo pre-clinical small-animal XFMI in the future. PCCT is an emerging technique that has the ability to distinguish photon energy and generate much richer image data that contains x-ray spectral information compared to conventional CT. In this study, a physics model was developed based on x-ray matter interaction physics to calculate the effective atomic number () and effective electron density () from PCCT image data for material identification. As the validation of the physics model, the and were calculated under various energy conditions for many materials. The relative standard deviations are mostly less than 1% (161 out of 168) shows that the developed model obtains good accuracy and robustness to energy conditions. To study the feasibility of applying the model with PCCT image data for material identification, both PCCT system numerical simulation and physical experiment were conducted. The result shows different materials can be clearly identified in the − map (with relative error ≤8.8%). The model has the value to serve as a material identification scheme for PCCT system for practical use in the future. As PCCT appears to be a significant breakthrough in CT imaging field, there exists severe data distortion problem in PCCT, which greatly limits the application of PCCT in practice. Lately, deep learning (DL) neural network has demonstrated tremendous success in medical imaging field. In this study, a deep learning neural network based PCCT data distortion correction method was proposed. When applying the algorithm to process the test dataset data, the accuracy of the PCCT data can be greatly improved (RMSE improved 73.7%). Compared with traditional data correction approaches such as maximum likelihood, the deep learning approach demonstrate superiority in terms of RMSE, SSIM, PSNR, and most importantly, runtime (4053.21 sec vs. 1.98 sec). The proposed method has the potential to facilitate the PCCT studies and applications in practice.
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    A problematic animal fossil from the early Cambrian Hetang Formation, South China
    Tang, Qing; Hu, Jie; Xie, Guwei; Yuan, Xunlai; Wan, Bin; Zhou, Cuanming; Dong, Xu; Cao, Guohua; Lieberman, Bruce S.; Leys, Sally P.; Xiao, Shuhai (Cambridge University Press, 2019-11-01)
    The lower-middle Hetang Formation (Cambrian Stage 2-3) deposited in slope-basinal facies in South China is well known for its preservation of the earliest articulated sponge fossils, providing an important taphonomic window into the Cambrian Explosion. However, the Hetang Formation also hosts a number of problematic animal fossils that have not been systematically described. This omission results in an incomplete picture of the Hetang biota and limits its contribution to the understanding of the early evolution of animals. Here we describe a new animal taxon, Cambrowania ovata Tang and Xiao, new genus new species, from the middle Hetang Formation in the Lantian area of southern Anhui Province, South China. Specimens are preserved as carbonaceous compressions, although some are secondarily mineralized. A comprehensive analysis using reflected light microscopy, scanning electron microscopy, energy-dispersive X-ray spectroscopy, and micro-CT reveals that the new species is characterized by a spheroidal to fusoidal truss-like structure consisting of rafter-like crossbars, some of which are secondarily baritized and may have been internally hollow. Some specimens have aperture-like structures that are broadly similar to oscula of sponges, whereas others show evidence of a medial split reminiscent of gaping carapaces. While the phylogenetic affinity of Cambrowania ovata Tang and Xiao, n. gen. n. sp. remains problematic, we propose that it may represent carapaces of bivalved arthropods or more likely sponges in early life stages. Along with other problematic metazoan fossils such as hyolithids and sphenothallids, Cambrowania ovata Tang and Xiao, n. gen. n. sp. adds to the diversity of the sponge-dominated Hetang biota in an early Cambrian deepwater slope-basinal environment. UUID: http://zoobank.org/44de9472-7e3f-42d1-9554-7b3434df91d9
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    A problematic animal fossil from the early Cambrian Hetang Formation, South China - A reply
    Tang, Qing; Hu, Jie; Xie, Guwei; Yuan, Xunlai; Wan, Bin; Zhou, Chuanming; Dong, Xu; Cao, Guohua; Lieberman, Bruce S.; Leys, Sally P.; Xiao, Shuhai (Cambridge University Press, 2019-11-01)
    We recently reported Cambrowania ovata Tang and Xiao in Tang et al., 2019, from the early Cambrian Hetang Formation in South China and interpreted it as a problematic animal fossil, possibly related to either sponges or bivalved arthropods (Tang et al., 2019). Slater and Budd (2019) contested our taxonomic identification and phylogenetic interpretation; instead, they claimed that Cambrowania ovata is a large acritarch referable to morphotaxon Leiosphaeridia Eisenack, 1958, and thus is not an animal. Here we refute their criticisms, clarify the differences between Cambrowania and Leiosphaeridia and other acritarchs, and reiterate why an animal affinity for Cambrowania cannot be ruled out.
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    Real-Time Computed Tomography-based Medical Diagnosis Using Deep Learning
    Goel, Garvit (Virginia Tech, 2022-02-24)
    Computed tomography has been widely used in medical diagnosis to generate accurate images of the body's internal organs. However, cancer risk is associated with high X-ray dose CT scans, limiting its applicability in medical diagnosis and telemedicine applications. CT scans acquired at low X-ray dose generate low-quality images with noise and streaking artifacts. Therefore we develop a deep learning-based CT image enhancement algorithm for improving the quality of low-dose CT images. Our algorithm uses a convolution neural network called DenseNet and Deconvolution network (DDnet) to remove noise and artifacts from the input image. To evaluate its advantages in medical diagnosis, we use DDnet to enhance chest CT scans of COVID-19 patients. We show that image enhancement can improve the accuracy of COVID-19 diagnosis (~5% improvement), using a framework consisting of AI-based tools. For training and inference of the image enhancement AI model, we use heterogeneous computing platform for accelerating the execution and decreasing the turnaround time. Specifically, we use multiple GPUs in distributed setup to exploit batch-level parallelism during training. We achieve approximately 7x speedup with 8 GPUs running in parallel compared to training DDnet on a single GPU. For inference, we implement DDnet using OpenCL and evaluate its performance on multi-core CPU, many-core GPU, and FPGA. Our OpenCL implementation is at least 2x faster than analogous PyTorch implementation on each platform and achieves comparable performance between CPU and FPGA, while FPGA operated at a much lower frequency.
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    A Scheme for Ultra-Fast Computed Tomography Based on Stationary Multi-Beam X-ray Sources
    Gong, Hao (Virginia Tech, 2017-02-16)
    The current cardiac computed tomography (CT) technology is mainly limited by motion blurring and radiation dose. The conceptual multi-source interior CT scheme has provided a potential solution to reduce motion artifacts and radiation exposure. This dissertation work conducted multi-facet investigations on a novel multi-source interior CT architecture (G. Cao, et. al, IEEE Access, 2014;2:1263-71) which employs distributed stationary multi-beam Carbon-nanotube (CNT) X-ray sources and simultaneously operates multiple source-detector chains to improve temporal resolution. The collimation based interior CT is integrated in each imaging chain, to suppress radiation dose. The central thesis statement is: Compared to conventional CT design, this distributed source array based multi-source interior CT architecture shall provide ultra-fast CT scan of region-of-interest (ROI) inside body with comparable image quality at lower radiation dose. Comprehensive studies were conducted to separately investigate three critical aspects of multi-source interior CT: interior CT mode, X-ray scattering, and scatter correction methods. First, a single CNT X-ray source based interior micro-CT was constructed to serve as a down-scaled experimental verification platform for interior CT mode. Interior CT mode demonstrated comparable contrast-noise-ratio (CNR) and image structural similarity to the standard global CT mode, while inducing a significant radiation dose reduction (< 83.9%). Second, the data acquisition of multi-source interior CT was demonstrated at clinical geometry, via numerical simulation and physical experiments. The simultaneously operated source-detector chains induced significant X-ray forward / cross scattering and thus caused severe CNR reduction (< 68.5%) and CT number error (< 1122 HU). To address the scatter artifacts, a stationary beam-stopper-array (BSA) based and a source-trigger-sequence (STS) based scatter correction methods were proposed to enable the online scatter measurement / correction with further radiation dose reduction (< 50%). Moreover, a deterministic physics model was also developed to iteratively remove the scatter-artifacts in the multi-source interior CT, without the need for modifications in imaging hardware or protocols. The three proposed scatter correction methods improved CNR (< 94.0%) and suppressed CT number error (< 48 HU). With the dedicated scatter correction methods, the multi-source interior CT could provide ROI-oriented imaging with acceptable image quality at significantly reduced radiation dose.
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    Segmenting Skin Lesion Attributes in Dermoscopic Images Using Deep Learing Algorithm for Melanoma Detection
    Dong, Xu (Virginia Tech, 2018-09)
    Melanoma is the most deadly form of skin cancer worldwide, which causes the 75% of deaths related to skin cancer. National Cancer Institute estimated that 91,270 new case and 9,320 deaths are expected in 2018 caused by melanoma. Early detection of melanoma is the key for the treatment. The image technique to diagnose skin cancer is dermoscopy, which leads to improved diagnose accuracy compared to traditional ABCD criteria. But reading and examining dermoscopic images is a time-consuming and complex process. Therefore, computerized analysis methods of dermoscopic images have been developed to assist the visual interpretation of dermoscopic images. The automatic segmentation of skin lesion attributes is a key step in computerized analysis of dermoscopic images. The International Skin Imaging Collaboration (ISIC) hosted the 2018 Challenges to help the diagnosis of melanoma based on dermoscopic images. In this thesis, I develop a deep learning based approach to automatically segment the attributes from dermoscopic skin lesion images. The approach described in the thesis achieved the Jaccard index of 0.477 on the official test dataset, which ranked 5th place in the challenge.
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    A Stationary-Sources and Rotating-Detectors Computed Tomography Architecture for Higher Temporal Resolution and Lower Radiation Dose
    Cao, Guohua; Liu, Baodong; Gong, Hao; Yu, Hengyong; Wang, Ge (IEEE, 2014)
    In current computed tomography (CT) architecture, both X-ray tubes and X-ray detectors are rotated mechanically around an object to collect a sufficient number of projections. This architecture has been shown to not be fast enough for patients with high or irregular heart rates. Furthermore, both X-ray beams and detectors of the current architecture are made wide enough, so that the entire object is covered in the lateral direction without data truncation. Although novel acquisition protocols have recently been developed to reduce a radiation exposure, the high radiation dose from CT imaging remains a heightened public concern (especially for cardiac CT). The current CT architecture is a major bottleneck to further increase the temporal resolution and reduce the radiation dose. To overcome these problems, we present an innovative stationary-sources rotating-detectors CT (SSRD-CT) architecture based on the three stationary distributed X-ray sources and three smaller rotating X-ray detectors. Each distributed X-ray source has similar to 100 distinctive X-ray focal spots, and each detector has a narrower width compared with the conventional CT detectors. The SSRD-CT will have a field-of-view of 200 mm in diameter at isocenter, which is large enough to image many internal organs, including hearts. X-rays from the distributed sources are activated electronically to simulate the mechanical spinning of conventional single-beam X-ray sources with a high speed. The activation of individual X-ray beam will be synchronized to the corresponding rotating detector at the opposite end. Three source-detector chains can work in parallel to acquire three projections simultaneously and improve temporal resolution. Lower full-body radiation dose is expected for the proposed SSRD-CT because X-rays are restricted to irradiate a local smaller region. Taken together, the proposed SSRD-CT architecture will enable <= 50-ms temporal resolution and reduce radiation dose significantly.
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    Two-Phase Flow Measurement using Fast X-ray Line Detector System
    Song, Kyle Seregay (Virginia Tech, 2019-11-25)
    Void fraction is an essential parameter for understanding the interfacial structure, and heat and mass transfer mechanisms in various gas-liquid flow systems. It becomes critically important to accurately measure void fraction as advanced high fidelity two-phase flow models require high-quality validation data. However, void fraction measurement remains a challenging task to date due to the complexity and rapid-changing characteristic of the gas-liquid boundary flow structure. This study aims to develop an advanced void fraction measurement system based on x-ray and fast line detector technologies. The dissertation has covered the major components necessary to develop a complete measurement system. Spectral analysis of x-ray attenuation in two-phase flow has been performed, and a new void fraction model is developed based on the analysis. The newly developed pixel-to-radial conversion algorithm is capable of converting measured void fraction along with the detector array to the radial distribution in a circular pipe for a wide range of void fraction conditions. The x-ray system attains the radial distributions of key measurable factors such as void fraction and gas velocity. The data are compared with the double-sensor conductivity probe and gas flowmeter for various flow conditions. The results show reasonable agreements between the x-ray and the other measurement techniques. Finally, various 2-D tomography algorithms are implemented for the non-axisymmetric two-phase flow reconstruction. A comprehensive summary of classical absorption tomography for the two-phase flow study is provided. An in-depth sensitivity study is carried out using synthetic bubbles, aiming to investigate the effect of various uncertainty factors such as background noise, off-center shift, void profile effect, etc. The sensitivity study provides a general guideline for the performance of existing 2-D reconstruction algorithms.