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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
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Image Rendering Techniques in Postmortem Computed Tomography: Evaluation of Biological Health and Profile in Stranded Cetaceans
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Optimization-based image reconstruction in computed tomography by alternating direction method with ordered subsets.

Ailong Cai, Linyuan Wang, Lei Li

    Journal of X-Ray Science and Technology
    |February 4, 2017
    PubMed
    Summary
    This summary is machine-generated.

    A new framework, OS-ADM, accelerates computed tomography (CT) image reconstruction using alternating direction method (ADM) and ordered subsets (OS). This efficient tool aids in designing advanced CT reconstruction algorithms for diverse applications.

    Keywords:
    Image reconstructionalternating direction methodfast working algorithm designoptimization-based algorithm designframeworkordered subsets

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    Area of Science:

    • Medical Imaging
    • Computational Imaging
    • Algorithm Design

    Background:

    • Computed tomography (CT) image reconstruction faces challenges due to diverse task-specific applications.
    • Efficient algorithm design tools are crucial for developing advanced CT reconstruction methods.

    Purpose of the Study:

    • To propose a fast and efficient algorithm design framework for CT image reconstruction.
    • To combine alternating direction method (ADM) with ordered subsets (OS) for solving convex optimizations in CT.

    Main Methods:

    • The proposed framework, OS-ADM, integrates ADM and OS principles.
    • Standardized procedures include model mapping, sub-problem division, solving, OS level setting, and algorithm evaluation.
    • Reconstruction problems are modeled as convex optimizations, including least-square and total variation (TV) minimization.

    Main Results:

    • Efficient algorithms for various CT reconstruction problems were derived using the OS-ADM framework.
    • TV-based algorithms demonstrated state-of-the-art performance in simulations and real CT projections.
    • The framework successfully generated working algorithms for diverse CT reconstruction tasks.

    Conclusions:

    • The OS-ADM framework provides a promising approach for practical CT image reconstruction applications.
    • The developed algorithms show high performance, validating the framework's efficacy.
    • This framework facilitates the design of efficient CT image reconstruction algorithms.