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Angle Projection

The Experts below are selected from a list of 264 Experts worldwide ranked by ideXlab platform

Vasilis Ntziachristos – 1st expert on this subject based on the ideXlab platform

  • in vivo imaging of murine tumors using complete Angle Projection fluorescence molecular tomography
    Journal of Biomedical Optics, 2009
    Co-Authors: Vasilis Ntziachristos, Nikolaos C Deliolanis, Joshua Dunham, Thomas Wurdinger, Joseluiz Figueiredo, Bakhos A Tannous

    Abstract:

    We interrogate the ability of free-space fluorescence tomography to image small animals in vivo using charge-coupled device (CCD) camera measurements over 360-deg noncontact Projections. We demonstrate the performance of normalized dual-wavelength measurements that are essential for in-vivo use, as they account for the heterogeneous distribution of photons in tissue. In-vivo imaging is then showcased on mouse lung and brain tumors cross-validated by x-ray microcomputed tomography and histology.

  • complete Angle Projection diffuse fluorescence molecular tomography with early photons
    Biosilico, 2006
    Co-Authors: Mark Niedre, Gordon M Turner, Vasilis Ntziachristos

    Abstract:

    In this work we demonstrate diffuse fluorescence tomography using early transmitted photons and complete-Angle Projection. We describe instrumentation and reconstruction algorithms and show preliminary images of complex fluorescent phantoms and of mice with implanted fluorophores

  • complete Angle Projection diffuse optical tomography by use of early photons
    Optics Letters, 2005
    Co-Authors: Gordon M Turner, Giannis Zacharakis, Antoine Soubret, Jorge Ripoll, Vasilis Ntziachristos

    Abstract:

    We present the first, to our knowledge, experimental images of complex-shaped phantoms embedded in diffuse media by use of optical tomography. Imaging is based on a complete-Angle Projection tomographic technique that utilizes transmitted early photons. Results are contrasted with measurements obtained at later gates as well as pseudocontinuous-wave data. The scanning system developed employs noncontact illumination and detection technologies that allow for high spatial sampling of transmitted photons. Combining this system with complete-Angle illumination is found to be an important strategy toward improved imaging performance, resulting in a better-posed inversion problem. The appropriateness of reconstruction algorithms similar to those employed in x-ray computed tomography are showcased, and suggestions for model improvements are provided.

Li Zeng – 2nd expert on this subject based on the ideXlab platform

  • Guided Image Filtering Based Limited-Angle CT Reconstruction Algorithm Using Wavelet Frame
    IEEE Access, 2019
    Co-Authors: Jiaxi Wang, Chengxiang Wang, Wei Yu, Li Zeng

    Abstract:

    Computed tomography (CT) has its irreplaceable function in nondestructive testing and medical diagnosis. In some practical CT imaging applications, the limited-Angle scanning is common due to X-ray’s potential harm to human and the limitation of the scanning conditions. Under these circumstances, analytic reconstruction algorithms, like filtered backProjection (FBP), will not obtain satisfactory results because of lacking the Projection data. Iterative reconstruction (IR) methods that can incorporate prior knowledge have attracted attention in many fields, and wavelet frame-based regularization reconstruction algorithms have proven to be a useful means to reduce slope artifacts and noise for limited-Angle CT. However, with the obtained Projection data of the scanned object further reduces, the edge structures and the details of the reconstructed image worsen. For the sake of improving the quality of the reconstructed image from the limited-Angle Projection data, a guided image filtering (GIF)-based limited-Angle CT reconstruction algorithm using wavelet frame was proposed. In each iteration of the proposed algorithm, the reconstructed result constrained by the wavelet frame was used as the guidance image to transfer the important features it contains to the reconstructed result of SART method by GIF. Furthermore, some simulated experiments and real data tests were conducted to evaluate the feasibility and validity of the proposed algorithm, and the qualitative and quantitative indexes indicated that the proposed algorithm was superior to other iterative reconstruction algorithms in artifacts reduction, noise suppression, and structure preservation.

  • Limited-Angle image reconstruction based on Mumford–Shah-like model and wavelet tight frames
    Journal of Optics, 2018
    Co-Authors: Lingli Zhang, Li Zeng, Chengxiang Wang

    Abstract:

    Restricted by the scanning environment and the radiation exposure of computed tomography (CT), the obtained Projection data are sometimes incomplete, which results in an ill-posed problem, such as a limited-Angle image reconstruction. In such circumstance, the commonly used analytic and iterative algorithms, such as filtered back-Projection and simultaneous algebraic reconstruction technique (SART), will not work well. Nowadays, a popular iterative image reconstruction algorithm (
    $${\hbox {SART}}+{\hbox {TV}}$$
    ) solving the optimization model based on the minimization of total variation (TV) of the image applies to the sparse-view reconstruction problem well; it is not effective on small limited-Angle reconstruction problem, especially in aspect of suppressing slope artifacts when the limited-Angle Projection views are severely reduced. In this work, we develop a reconstruction model based on the Mumford–Shah-like model and wavelet tight frames that applies to limited-Angle CT; and the corresponding iterative method is given. Numerical experiments and quantitative analysis demonstrate that our method outperforms SART and $${\hbox {SART}}+{\hbox {TV}}$$
    in suppressing slope artifacts when the limited-Angle Projection views are severely decreased.

  • Wavelet tight frame and prior image-based image reconstruction from limited-Angle Projection data
    Inverse Problems and Imaging, 2017
    Co-Authors: Chengxiang Wang, Li Zeng, Lingli Zhang

    Abstract:

    The limited-Angle Projection data of an object, in some practical applications of computed tomography (CT), are obtained due to the restriction of scanning condition. In these situations, since the Projection data are incomplete, some limited-Angle artifacts will be presented near the edges of reconstructed image using some classical reconstruction algorithms, such as filtered backProjection (FBP). The reconstructed image can be fine approximated by sparse coefficients under a proper wavelet tight frame, and the quality of reconstructed image can be improved by an available prior image. To deal with limited-Angle CT reconstruction problem, we propose a minimization model that is based on wavelet tight frame and a prior image, and perform this minimization problem efficiently by iteratively minimizing separately. Moreover, we show that each bounded sequence, which is generated by our method, converges to a critical or a stationary point. The experimental results indicate that our algorithm can efficiently suppress artifacts and noise and preserve the edges of reconstructed image, what’s more, the introduced prior image will not miss the important information that is not included in the prior image.

Chengxiang Wang – 3rd expert on this subject based on the ideXlab platform

  • Guided Image Filtering Based Limited-Angle CT Reconstruction Algorithm Using Wavelet Frame
    IEEE Access, 2019
    Co-Authors: Jiaxi Wang, Chengxiang Wang, Wei Yu, Li Zeng

    Abstract:

    Computed tomography (CT) has its irreplaceable function in nondestructive testing and medical diagnosis. In some practical CT imaging applications, the limited-Angle scanning is common due to X-ray’s potential harm to human and the limitation of the scanning conditions. Under these circumstances, analytic reconstruction algorithms, like filtered backProjection (FBP), will not obtain satisfactory results because of lacking the Projection data. Iterative reconstruction (IR) methods that can incorporate prior knowledge have attracted attention in many fields, and wavelet frame-based regularization reconstruction algorithms have proven to be a useful means to reduce slope artifacts and noise for limited-Angle CT. However, with the obtained Projection data of the scanned object further reduces, the edge structures and the details of the reconstructed image worsen. For the sake of improving the quality of the reconstructed image from the limited-Angle Projection data, a guided image filtering (GIF)-based limited-Angle CT reconstruction algorithm using wavelet frame was proposed. In each iteration of the proposed algorithm, the reconstructed result constrained by the wavelet frame was used as the guidance image to transfer the important features it contains to the reconstructed result of SART method by GIF. Furthermore, some simulated experiments and real data tests were conducted to evaluate the feasibility and validity of the proposed algorithm, and the qualitative and quantitative indexes indicated that the proposed algorithm was superior to other iterative reconstruction algorithms in artifacts reduction, noise suppression, and structure preservation.

  • Limited-Angle image reconstruction based on Mumford–Shah-like model and wavelet tight frames
    Journal of Optics, 2018
    Co-Authors: Lingli Zhang, Li Zeng, Chengxiang Wang

    Abstract:

    Restricted by the scanning environment and the radiation exposure of computed tomography (CT), the obtained Projection data are sometimes incomplete, which results in an ill-posed problem, such as a limited-Angle image reconstruction. In such circumstance, the commonly used analytic and iterative algorithms, such as filtered back-Projection and simultaneous algebraic reconstruction technique (SART), will not work well. Nowadays, a popular iterative image reconstruction algorithm (
    $${\hbox {SART}}+{\hbox {TV}}$$
    ) solving the optimization model based on the minimization of total variation (TV) of the image applies to the sparse-view reconstruction problem well; it is not effective on small limited-Angle reconstruction problem, especially in aspect of suppressing slope artifacts when the limited-Angle Projection views are severely reduced. In this work, we develop a reconstruction model based on the Mumford–Shah-like model and wavelet tight frames that applies to limited-Angle CT; and the corresponding iterative method is given. Numerical experiments and quantitative analysis demonstrate that our method outperforms SART and $${\hbox {SART}}+{\hbox {TV}}$$
    in suppressing slope artifacts when the limited-Angle Projection views are severely decreased.

  • Wavelet tight frame and prior image-based image reconstruction from limited-Angle Projection data
    Inverse Problems and Imaging, 2017
    Co-Authors: Chengxiang Wang, Li Zeng, Lingli Zhang

    Abstract:

    The limited-Angle Projection data of an object, in some practical applications of computed tomography (CT), are obtained due to the restriction of scanning condition. In these situations, since the Projection data are incomplete, some limited-Angle artifacts will be presented near the edges of reconstructed image using some classical reconstruction algorithms, such as filtered backProjection (FBP). The reconstructed image can be fine approximated by sparse coefficients under a proper wavelet tight frame, and the quality of reconstructed image can be improved by an available prior image. To deal with limited-Angle CT reconstruction problem, we propose a minimization model that is based on wavelet tight frame and a prior image, and perform this minimization problem efficiently by iteratively minimizing separately. Moreover, we show that each bounded sequence, which is generated by our method, converges to a critical or a stationary point. The experimental results indicate that our algorithm can efficiently suppress artifacts and noise and preserve the edges of reconstructed image, what’s more, the introduced prior image will not miss the important information that is not included in the prior image.