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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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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.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Limited-Angle Computed Tomography Reconstruction using Combined FDK-Based Neural Network and U-Net.

Yiying Wang, Tao Yang, Weimin Huang

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
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    Summary

    Limited-angle cone-beam CT (CBCT) reconstruction is improved using a novel neural network pipeline. This method reduces artifacts and preserves features in 3D images from incomplete projection data, outperforming previous techniques.

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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Computational Science

    Background:

    • Limited-angle cone-beam Computed Tomography (CBCT) is crucial for C-arm clinical diagnosis due to cost-effectiveness and reduced radiation dose.
    • Conventional reconstruction algorithms like Feldkamp, Davis and Kres (FDK) produce significant artifacts and feature loss with incomplete projection data (<180 degrees).

    Purpose of the Study:

    • To enhance 3D reconstruction quality for limited-angle CBCT using a novel neural network pipeline.
    • To mitigate artifacts and preserve essential features in reconstructions from sinograms with less than 180 degrees of projection data.

    Main Methods:

    • Developed a pipeline combining a revisited FDK-based neural network with an image domain U-Net.
    • Tested the pipeline on simulated projections from real-scan CT data.
    • Evaluated reconstruction quality using peak signal-to-noise ratio (PSNR).

    Main Results:

    • The proposed neural network pipeline effectively reduced major artifacts caused by limited angular views.
    • Key image features were preserved in the reconstructed 3D CT images.
    • Achieved a 16.60% improvement in PSNR compared to Würfl et al.'s work.

    Conclusions:

    • The novel neural network pipeline significantly enhances 3D reconstruction quality in limited-angle CBCT.
    • This approach offers a promising solution for improving diagnostic accuracy in CBCT imaging with reduced data acquisition.