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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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Imaging Studies III: Computed Tomography01:27

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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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Imaging Studies I: CT and MRI01:14

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Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
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Computed Tomography (CT) scan:
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Electron Microscope Tomography and Single-particle Reconstruction01:07

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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.
Electron Tomography
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Imaging Studies for Cardiovascular System V: CT01:28

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Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
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Related Experiment Video

Updated: Aug 19, 2025

A Sectioning, Coring, and Image Processing Guide for High-Throughput Cortical Bone Sample Procurement and Analysis for Synchrotron Micro-CT
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DREAM-Net: Deep Residual Error Iterative Minimization Network for Sparse-View CT Reconstruction.

Yikun Zhang, Dianlin Hu, Shilei Hao

    IEEE Journal of Biomedical and Health Informatics
    |November 30, 2022
    PubMed
    Summary

    Sparse-view Computed Tomography (CT) reconstruction is improved by DREAM-Net, a novel deep learning framework. This method effectively reduces artifacts and enhances image quality from limited projection data.

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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Sparse-view Computed Tomography (CT) offers reduced radiation dose and scan time.
    • Severe streak artifacts in sparse-view CT compromise anatomical information, posing a reconstruction challenge.

    Purpose of the Study:

    • To propose an unrolled Deep Residual Error iterAtive Minimization Network (DREAM-Net) for high-quality sparse-view CT image reconstruction.
    • To synergize deep learning and iterative reconstruction within a novel framework.

    Main Methods:

    • DREAM-Net employs simultaneous deep neural network constraints in projection, residual, and image domains.
    • Key modules include projection inpainting for missing views, residual awareness for error estimation, and image refinement using a non-standard regularizer.
    • Hyper-parameters are implicitly embedded, eliminating empirical weight adjustments.

    Main Results:

    • DREAM-Net demonstrated promising performance in artifact removal.
    • Quantitative and qualitative results confirmed enhanced structural fidelity in reconstructed images.
    • The framework effectively addresses the ill-posed problem of sparse-view CT reconstruction.

    Conclusions:

    • DREAM-Net offers a robust solution for sparse-view CT image reconstruction.
    • The simultaneous multi-domain constraint approach improves artifact reduction and image quality.
    • This deep learning-iterative reconstruction synergy presents a significant advancement in medical imaging.