Browsing by Author "Dong, Xu"

Now showing 1 - 6 of 6
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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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    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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    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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    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.