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Computed Tomography

Computed Tomography

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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Related Experiment Video

Updated: Aug 11, 2025

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

Published on: October 27, 2023

Image-based scatter correction for cone-beam CT using flip swin transformer U-shape network.

Xueren Zhang¹, Yangkang Jiang², Chen Luo^3,4

¹Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong, China.

Medical Physics

|February 3, 2023

Summary

This study introduces the Flip Swin Transformer U-shape network (FSTUNet) to correct scatter in Cone Beam Computed Tomography (CBCT) images. The FSTUNet significantly improves image quality for radiation therapy by reducing artifacts and enhancing structural consistency.

Keywords:

CBCT CNN scatter correction swin transformer

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Area of Science:

Medical Imaging
Artificial Intelligence in Radiation Therapy
Image Processing

Background:

Cone Beam Computed Tomography (CBCT) is crucial for image-guided radiation therapy.
Excessive scatter contamination degrades CBCT image quality, particularly in abdominal imaging.
This degradation limits the application of CBCT in radiation therapy.

Purpose of the Study:

To develop an effective scatter correction algorithm for low-quality CBCT images.
To leverage the strengths of Convolutional Neural Networks (CNN) and Swin Transformer for scatter removal.
To restore CBCT images by addressing scatter signal contamination.

Main Methods:

Proposed the Flip Swin Transformer U-shape network (FSTUNet) model for CBCT scatter correction.
Integrated CNN for texture detail extraction and Swin Transformer for global correlation analysis.
Developed a novel Flip Swin Transformer Block to enhance inter-window association extraction.

Main Results:

Reduced root mean square error from over 100 HU to approximately 7 HU on Monte Carlo simulated data.
Achieved structural similarity index measure (SSIM) and universal quality index (UQI) values close to 1.
Demonstrated superior performance of FSTUNet over other leading methods like UNet and Pix2pixGAN in qualitative and quantitative evaluations.

Conclusions:

Effective feature extraction across different levels is key to reconstructing high-quality, scatter-free images.
The FSTUNet model offers a robust solution for CBCT scatter correction.
This method has the potential to enhance the accuracy of CBCT image-guided radiation therapy.