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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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Updated: Jun 25, 2025

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
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Synthetic CT generation based on CBCT using improved vision transformer CycleGAN.

Yuxin Hu1, Han Zhou2,3, Ning Cao1

  • 1School of Computer and Software, Hohai University, Nanjing, 211100, China.

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Improved vision transformer CycleGAN (IViT-CycleGAN) enhances cone-beam computed tomography (CBCT) image quality for adaptive radiation therapy by addressing artifacts and noise, improving clinical planning.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Radiation Oncology

Background:

  • Cone-beam computed tomography (CBCT) is vital for adaptive radiation therapy but suffers from artifacts and noise, limiting its clinical use.
  • Existing CycleGAN models for CT image synthesis struggle with capturing global features effectively.

Purpose of the Study:

  • To develop an improved unsupervised learning model for synthesizing high-quality CT images from CBCT data.
  • To enhance the clinical utility of CBCT in adaptive radiation therapy through superior image synthesis.

Main Methods:

  • Introduced an improved vision transformer CycleGAN (IViT-CycleGAN) model.
  • Integrated a U-net framework with Vision Transformer (ViT) and augmented feed-forward networks with deep convolutional networks.
  • Enhanced training stability using gradient penalty and an additional generator loss term.

Main Results:

  • The IViT-CycleGAN model generated synthesizing CT (sCT) images with significant advantages over other unsupervised learning models.
  • Experimental results demonstrated the clinical applicability and robustness of the proposed model.
  • The synthesized CT images showed improved quality, reducing artifacts and noise.

Conclusions:

  • The IViT-CycleGAN model effectively addresses limitations of previous methods for CBCT image synthesis.
  • This advanced model shows strong potential for assisting in precise radiotherapy planning in clinical practice.
  • The improved image quality is crucial for enhancing the accuracy and effectiveness of adaptive radiation therapy.