Browsing by Author "Yu, Hengyong"

Now showing 1 - 20 of 25
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    Cardiac computed tomography methods and systems using fast exact/quasi-exact filtered back projection algorithms
    (United States Patent and Trademark Office, 2013-07-09)
    The present invention provides systems, methods, and devices for improved computed tomography (CT). More specifically, the present invention includes methods for improved cone-beam computed tomography (CBCT) resolution using improved filtered back projection (FBP) algorithms, which can be used for cardiac tomography and across other tomographic modalities. Embodiments provide methods, systems, and devices for reconstructing an image from projection data provided by a computed tomography scanner using the algorithms disclosed herein to generate an image with improved temporal resolution.
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    Cone-Beam Composite-Circling Scan and Exact Image Reconstruction for a Quasi-Short Object
    Yu, Hengyong; Wang, Ge (Hindawi, 2008-02-03)
    Here we propose a cone-beam composite-circling mode to solve the quasi-short object problem, which is to reconstruct a short portion of a long object from longitudinally truncated cone-beam data involving the short object. In contrast to the saddle curve cone-beam scanning, the proposed scanning mode requires that the X-ray focal spot undergoes a circular motion in a plane facing the short object, while the X-ray source is rotated in the gantry main plane. Because of the symmetry of the proposed mechanical rotations and the compatibility with the physiological conditions, this new mode has significant advantages over the saddle curve from perspectives of both engineering implementation and clinical applications. As a feasibility study, a backprojection filtration (BPF) algorithm is developed to reconstruct images from data collected along a composite-circling trajectory. The initial simulation results demonstrate the correctness of the proposed exact reconstruction method and the merits of the proposed mode.
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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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    Exact Interior Reconstruction from Truncated Limited-Angle Projection Data
    Ye, Yangbo; Yu, Hengyong; Wang, Ge (Hindawi, 2008-05-06)
    Using filtered backprojection (FBP) and an analytic continuation approach, we prove that exact interior reconstruction is possible and unique from truncated limited-angle projection data, if we assume a prior knowledge on a subregion or subvolume within an object to be reconstructed. Our results show that (i) the interior region-of-interest (ROI) problem and interior volume-of-interest (VOI) problem can be exactly reconstructed from a limited-angle scan of the ROI/VOI and a 180 degree PI-scan of the subregion or subvolume and (ii) the whole object function can be exactly reconstructed from nontruncated projections from a limited-angle scan. These results improve the classical theory of Hamaker et al. (1980).
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    Exact Interior Reconstruction with Cone-Beam CT
    Ye, Yangbo; Yu, Hengyong; Wang, Ge (Hindawi, 2008-01-23)
    Using the backprojection filtration (BPF) and filtered backprojection (FBP) approaches, respectively, we prove that with cone-beam CT the interior problem can be exactly solved by analytic continuation. The prior knowledge we assume is that a volume of interest (VOI) in an object to be reconstructed is known in a subregion of the VOI. Our derivations are based on the so-called generalized PI-segment (chord). The available projection onto convex set (POCS) algorithm and singular value decomposition (SVD) method can be applied to perform the exact interior reconstruction. These results have many implications in the CT field and can be extended to other tomographic modalities, such as SPECT/PET, MRI.
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    Exact local computed tomography based on compressive sampling
    (United States Patent and Trademark Office, 2014-08-19)
    A system and method for tomographic image reconstruction using truncated projection data that allows exact interior reconstruction (interior tomography) of a region of interest (ROI) based on the known sparsity models of the ROI, thereby improving image quality while reducing radiation dosage. In addition, the method includes parallel interior tomography using multiple sources beamed at multiple angles through an ROI and that enables higher temporal resolution.
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    Gel'fand-Graev's Reconstruction Formula in the 3D Real Space - a Framework towards a General Interior Tomography Theory
    Ye, Yangbo; Yu, Hengyong; Wang, Ge (2010-05-31)
    In [1-4], I. M. Gel'fand and M. I. Graev proposed inversion formulas for x-ray transforms in different spaces. In particular, Gel’fand-Graev’s inversion formula [1] is a fundamental relationship linking projection data to the Hilbert transform of an image to be reconstructed. This finding was re-discovered in the CT field; see [5-9]. It has wide applications, including local reconstruction [10-11], backprojection filtration (BPF) [12], interior tomography [13-17], and limited-angle tomography [18]. For a survey, see [19, 20]. Despite its high information density, Gel’fand-Graev’s inversion formula [1] was cast in high dimensions and specialized terms, and difficult to follow for a well-trained engineer. In this poster, we represent this formula and its proof for the 1D x-ray transform in a 3D real space for easy access and further extension.
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    A General Formula for Fan-Beam Lambda Tomography
    Yu, Hengyong; Wang, Ge (Hindawi, 2007-08-23)
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    A General Local Reconstruction Approach Based on a Truncated Hilbert Transform
    Ye, Yangbo; Yu, Hengyong; Wei, Yuchuan; Wang, Ge (Hindawi, 2007-06-17)
    Exact image reconstruction from limited projection data has been a central topic in the computed tomography (CT) field. In this paper, we present a general region-of-interest/volume-of-interest (ROI/VOI) reconstruction approach using a truly truncated Hilbert transform on a line-segment inside a compactly supported object aided by partial knowledge on one or both neighboring intervals of that segment. Our approach and associated new data sufficient condition allows the most flexible ROI/VOI image reconstruction from the minimum account of data in both the fan-beam and cone-beam geometry. We also report primary numerical simulation results to demonstrate the correctness and merits of our finding. Our work has major theoretical potentials and innovative practical applications.
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    A General Total Variation Minimization Theorem for Compressed Sensing Based Interior Tomography
    Han, Weimin; Yu, Hengyong; Wang, Ge (Hindawi, 2009-11-17)
    Recently, in the compressed sensing framework we found that a two-dimensional interior region-of-interest (ROI) can be exactly reconstructed via the total variation minimization if the ROI is piecewise constant (Yu and Wang, 2009). Here we present a general theorem charactering a minimization property for a piecewise constant function defined on a domain in any dimension. Our major mathematical tool to prove this result is functional analysis without involving the Dirac delta function, which was heuristically used by Yu and Wang (2009).
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    GPU-Based Acceleration for Interior Tomography
    Liu, Rui; Luo, Yan; Yu, Hengyong (IEEE, 2014)
    The compressive sensing (CS) theory shows that real signals can be exactly recovered from very few samplings. Inspired by the CS theory, the interior problem in computed tomography is proved uniquely solvable by minimizing the region-of-interest's total variation if the imaging object is piecewise constant or polynomial. This is called CS-based interior tomography. However, the CS-based algorithms require high computational cost due to their iterative nature. In this paper, a graphics processing unit (GPU)-based parallel computing technique is applied to accelerate the CS-based interior reconstruction for practical application in both fan-beam and cone-beam geometries. Our results show that the CS-based interior tomography is able to reconstruct excellent volumetric images with GPU acceleration in a few minutes.
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    How to Define the Next Generation Cardiac CT Architecture? - a Contemporary Challenge for Interdisciplinary Collaboration
    Yu, Hengyong; DeMan, Bruno; Carr, Jeff; Frontera, Mark; Zeng, Kai; Bennett, James; Fitzgerald, Paul; Iatrou, Maria; Shen, Haiou; Santago, Peter; Wang, Ge (2010-05-21)
    Cardiovascular diseases are pervasive with high mortality and morbidity at tremendous social and healthcare costs. There are urgent needs for significantly higher fidelity cardiac CT with substantially lower radiation dose, which is currently not possible because of technical limitations. Although cardiac CT technology has improved significantly from 16 to 320 detector rows and from single to dual source, there remain technical challenges in terms of temporal resolution, spatial resolution, radiation dose, and so on. Based on an ideal academic-industrial partnership between Virginia Tech and the GE Global Research Center (GEGR), we are motivated to advance the state-of-the-art in cardiac CT. The overall goal of this project is to develop novel cardiac CT architectures and the associated reconstruction algorithms, and define the next-generation cardiac CT system. The specific aims are to (1) design, analyze and compare novel cardiac CT architectures with novel sources and scanning trajectories; (2) develop analytic and iterative cardiac CT reconstruction algorithms for ROI-oriented scanning and dynamic imaging for the proposed cardiac CT architectures; and (3) evaluate and validate the proposed architectures and algorithms in theoretical studies, numerical simulations, phantom experiments and observer studies. On completion of this project, we will have singled out the most promising cardiac CT architectures and algorithms to achieve 16cm coverage, 50ms temporal resolution, 20lp/cm spatial resolution, 10HU noise level, and 1mSv effective dose simultaneously for the entire examination, with detailed specifications and performance evaluation, setting the stage for prototyping a next-generation cardiac CT system in a Phase-II project. This project will enable significantly better diagnostic performance and bring major therapeutic benefits that affect over 60 million Americans.
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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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    Interior tomography and instant tomography by reconstruction from truncated limited-angle projection data
    (United States Patent and Trademark Office, 2010-04-13)
    A system and method for tomographic image reconstruction using truncated limited-angle projection data that allows exact interior reconstruction (interior tomography) of a region of interest (ROI) based on the linear attenuation coefficient distribution of a subregion within the ROI, thereby improving image quality while reducing radiation dosage. In addition, the method includes parallel interior tomography using multiple sources beamed at multiple angles through an ROI and that enables higher temporal resolution.
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    Inverse Fourier Transform in the Gamma Coordinate System
    Wei, Yuchuan; Yu, Hengyong; Wang, Ge (Hindawi, 2010-10-26)
    This paper provides auxiliary results for our general scheme of computed tomography. In 3D parallel-beam geometry, we first demonstrate that the inverse Fourier transform in different coordinate systems leads to different reconstruction formulas and explain why the Radon formula cannot directly work with truncated projection data. Also, we introduce a gamma coordinate system, analyze its properties, compute the Jacobian of the coordinate transform, and define weight functions for the inverse Fourier transform assuming a simple scanning model. Then, we generate Orlov's theorem and a weighted Radon formula from the inverse Fourier transform in the new system. Furthermore, we present the motion equation of the frequency plane and the conditions for sharp points of the instantaneous rotation axis. Our analysis on the motion of the frequency plane is related to the Frenet-Serret theorem in the differential geometry.
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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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    Line-Source Based X-Ray Tomography
    Bharkhada, Deepak; Yu, Hengyong; Liu, Hong; Plemmons, Robert; Wang, Ge (Hindawi, 2009-04-27)
    Current computed tomography (CT) scanners, including micro-CT scanners, utilize a point x-ray source. As we target higher and higher spatial resolutions, the reduced x-ray focal spot size limits the temporal and contrast resolutions achievable. To overcome this limitation, in this paper we propose to use a line-shaped x-ray source so that many more photons can be generated, given a data acquisition interval. In reference to the simultaneous algebraic reconstruction technique (SART) algorithm for image reconstruction from projection data generated by an x-ray point source, here we develop a generalized SART algorithm for image reconstruction from projection data generated by an x-ray line source. Our numerical simulation results demonstrate the feasibility of our novel line-source based x-ray CT approach and the proposed generalized SART algorithm.
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    Methods for improved single photon emission computed tomography using exact and stable region of interest reconstructions
    (United States Patent and Trademark Office, 2015-02-10)
    The present invention provides systems, methods, and devices for improved computed tomography (CT) and, more specifically, to methods for improved single photon computed tomography (SPECT) using exact and stable region of interest (ROI) reconstructions. This technology can be extended across all tomographic modalities. Embodiments provide a method and a system for reconstructing an image from projection data provided by a single photon emission computed tomography scanner comprising: identifying a region of interest in an object; defining an attenuation coefficient and object boundary; computing the generalized Hilbert transform of the data through the defined region of interest and a known subregion; and reconstructing the image with improved temporal resolution at lower radiation doses, wherein the reconstructing comprises performing a reconstruction method that yields an exact and stable reconstruction. Embodiments also provide a method and a system for reconstructing an image from projection data provided by a single photon emission computed tomography scanner comprising: identifying a region of interest in an object; defining an attenuation coefficient and object boundary; and reconstructing the images by minimizing the high order total variation while minimizing the data discrepancy.
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    Piecewise-Constant-Model-Based Interior Tomography Applied to Dentin Tubules
    He, Peng; Wei, Biao; Wang, Steve; Stock, Stuart R.; Yu, Hengyong; Wang, Ge (Hindawi Publishing Corporation, 2013)
    Dentin is a hierarchically structured biomineralized composite material, and dentin's tubules are difficult to study in situ. Nano-CT provides the requisite resolution, but the field of view typically contains only a few tubules. Using a plate-like specimen allows reconstruction of a volume containing specific tubules from a number of truncated projections typically collected over an angular range of about 140 degrees, which is practically accessible. Classical computed tomography (CT) theory cannot exactly reconstruct an object only from truncated projections, needless to say a limited angular range. Recently, interior tomography was developed to reconstruct a region-of-interest (ROI) from truncated data in a theoretically exact fashion via the total variation (TV) minimization under the condition that the ROI is piecewise constant. In this paper, we employ a TV minimization interior tomography algorithm to reconstruct interior microstructures in dentin from truncated projections over a limited angular range. Compared to the filtered backprojection (FBP) reconstruction, our reconstruction method reduces noise and suppresses artifacts. Volume rendering confirms the merits of our method in terms of preserving the interior microstructure of the dentin specimen.
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    SART-Type Image Reconstruction from a Limited Number of Projections with the Sparsity Constraint
    Yu, Hengyong; Wang, Ge (Hindawi, 2010-04-26)
    Based on the recent mathematical findings on solving the linear inverse problems with sparsity constraints by Daubechiesx et al., here we adapt a simultaneous algebraic reconstruction technique (SART) for image reconstruction from a limited number of projections subject to a sparsity constraint in terms of an invertible compression transform. The algorithm is implemented with an exemplary Haar wavelet transform and tested with a modified Shepp-Logan phantom. Our preliminary results demonstrate that the sparsity constraint helps effectively improve the quality of reconstructed images and reduce the number of necessary projections.