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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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Related Experiment Video

Updated: Oct 16, 2025

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Generative adversarial networks improve interior computed tomography angiography reconstruction.

Juuso H J Ketola1,2, Helinä Heino1, Mikael A K Juntunen1,3

  • 1Research Unit of Medical Imaging, Physics and Technology, University of Oulu, FI-90014, Finland.

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|October 21, 2021
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Summary

This study introduces a novel deep learning method for interior computed tomography (CT) imaging, significantly reducing artifacts in cardiac and dental scans. The developed double generative adversarial network (DGAN) model enhances image quality and extends the field-of-view, offering improved diagnostic capabilities.

Keywords:
computed tomographyconvolutional neural networksgenerative adversarial networksimage reconstructioninterior tomographysinogram extension

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Radiology

Background:

  • Interior computed tomography (CT) uses a limited field-of-view (FOV) to reduce radiation dose to adjacent organs.
  • Traditional reconstruction algorithms like filtered back-projection (FBP) produce severe truncation artifacts with limited FOV data.
  • Cardiac and dentomaxillofacial imaging can benefit from interior CT for dose reduction, but artifact mitigation is crucial.

Purpose of the Study:

  • To develop and evaluate a deep learning (DL) method for generating artifact-free interior CT images.
  • To improve image quality in interior CT angiography by addressing truncation artifacts.
  • To assess the capability of the proposed method to extend the reconstructed field-of-view.

Main Methods:

  • A two-stage deep learning approach using Pix2Pix generative adversarial networks (GANs), termed double GAN (DGAN).
  • Stage 1: An extended sinogram is generated from a truncated sinogram using a GAN.
  • Stage 2: Another GAN processes the FBP reconstruction from the extended sinogram to enhance interior image quality. The model was trained on 10,000 simulated truncated sinograms.

Main Results:

  • The DGAN method achieved competitive performance with state-of-the-art DL methods, showing excellent root-mean-squared error (RMSE) and structural similarity index (SSIM) values.
  • Quantitative analysis demonstrated superior performance of DGAN compared to other methods, including adaptive de-truncation and total variation regularization.
  • The DGAN approach successfully extended the reconstructed FOV by up to 20% while maintaining high image quality (low RMSE, high PSNR and SSIM).

Conclusions:

  • The proposed DGAN method effectively reconstructs interior CT regions with significantly improved image quality.
  • The DL-based approach successfully mitigates truncation artifacts inherent in limited-FOV CT reconstructions.
  • The DGAN model demonstrates potential for dose reduction in specific CT applications by enabling extended FOV reconstructions with high fidelity.