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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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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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CBCT-based synthetic CT generation using generative adversarial networks with disentangled representation.

Jiwei Liu1, Hui Yan2, Hanlin Cheng1

  • 1School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China.

Quantitative Imaging in Medicine and Surgery
|December 10, 2021
PubMed
Summary

A new deep learning method, sCTGAN, generates high-quality synthetic CT (sCT) images from CBCT scans. This advancement improves image quality for image-guided radiotherapy (IGRT) and adaptive radiotherapy applications.

Keywords:
Cone-beam CTdisentangled representationgenerative adversarial networkimage-guided radiation therapysynthetic CT generation

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Area of Science:

  • Medical Imaging
  • Radiotherapy Technology
  • Artificial Intelligence in Medicine

Background:

  • Cone-beam computed tomography (CBCT) is crucial for image-guided radiotherapy (IGRT).
  • Poor CBCT image quality has limited its clinical use.
  • This study addresses CBCT image quality limitations.

Purpose of the Study:

  • To develop a deep learning approach for CBCT-to-CT image translation.
  • To generate synthetic CT (sCT) images with improved quality from CBCT.
  • To preserve anatomical structures from CBCT in the generated sCT images.

Main Methods:

  • A novel synthetic CT generative adversarial network (sCTGAN) was developed.
  • Disentangled representation was used for CBCT-to-CT translation.
  • The network was trained on 40 patients' CBCT and CT data and tested on 12 patients' data.

Main Results:

  • sCTGAN achieved superior quantitative metrics (PSNR, SSIM, MAE, RMSE) compared to deformed planning CT (dpCT).
  • sCTGAN's RMSE was 60.53 HU, significantly lower than original CBCT (112.13 HU).
  • sCTGAN outperformed three state-of-the-art CycleGAN-based methods in RMSE (60.53 HU vs. 64.93-72.40 HU).

Conclusions:

  • The sCTGAN network generates high-quality sCT images closer to the ground truth (dpCT).
  • This method offers an effective solution for improving CBCT image quality.
  • The generated sCT has broad applications in IGRT and adaptive radiotherapy.