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Synthetic CT generation from CBCT using double-chain-CycleGAN.

Liwei Deng1, Yufei Ji1, Sijuan Huang2

  • 1Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin, 150080, China.

Computers in Biology and Medicine
|May 27, 2023
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Summary
This summary is machine-generated.

This study enhances Cone-Beam CT (CBCT) image quality for adaptive radiotherapy by improving CycleGAN. The new model reduces noise and artifacts, improving synthetic CT (sCT) image quality for clinical use.

Keywords:
CBCTCycleGANRadio therapyScatter correctionSynthetic CT

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Cone-beam CT (CBCT) offers advantages like lower cost and radiation dose but suffers from noise and artifacts, limiting its use in adaptive radiotherapy.
  • Artifacts such as bone and metal significantly degrade CBCT image quality, posing challenges for accurate treatment planning.

Purpose of the Study:

  • To enhance the quality of synthetic CT (sCT) images generated from CBCT data for improved adaptive radiotherapy applications.
  • To address noise and artifact issues in CBCT by developing an improved CycleGAN model.

Main Methods:

  • An enhanced CycleGAN architecture was developed, incorporating a Diversity Branch Block (DBB) for semantic information and an adaptive learning rate adjustment strategy (Alras) for training stability.
  • Total Variation Loss (TV loss) was integrated into the generator loss function to promote image smoothness and reduce noise.
  • The model was trained to generate higher-quality sCT images from CBCT data.

Main Results:

  • The enhanced model significantly reduced image artifacts and noise compared to standard CBCT.
  • Quantitative metrics showed substantial improvements: Root Mean Square Error (RMSE) decreased by 27.97, Mean Absolute Error (MAE) improved from 43.2 to 32.05, Peak Signal-to-Noise Ratio (PSNR) increased by 1.61, Structural Similarity Index Measure (SSIM) improved from 0.948 to 0.963, and Gradient Magnitude Similarity Deviation (GMSD) decreased from 12.98 to 9.33.
  • Generalization experiments confirmed the model's superior performance over standard CycleGAN and respath-CycleGAN.

Conclusions:

  • The proposed CycleGAN improvement effectively generates high-quality sCT images from CBCT, mitigating noise and artifacts.
  • This advancement holds significant potential for the clinical application of CBCT in adaptive radiotherapy, enabling more accurate and reliable treatments.
  • The model demonstrates robust performance and superior image quality compared to existing methods.