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Synthetic CT generation from CBCT based on structural constraint cycle-EEM-GAN.

Qianhong Lu1, Feng Luo1, Juntian Shi2

  • 1Key Laboratory of Atomic and Subatomic Structure and Quantum Control (Ministry of Education), Guangdong Basic Research Center of Excellence for Structure and Fundamental Interactions of Matter, School of Physics, South China Normal University, Guangzhou, 510006, People's Republic of China.

Biomedical Physics & Engineering Express
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PubMed
Summary

This study introduces Cycle-EEM-GAN, an enhanced generative model that improves synthetic CT image quality from CBCT scans. The new method addresses scaling issues and enhances structural details for better radiotherapy applications.

Keywords:
CBCT synthetic CTcycle consistent generative adversarial networkstructure consistency

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Radiotherapy Physics

Background:

  • Cone beam CT (CBCT) suffers from artifacts and inaccurate Hounsfield Unit (HU) values, limiting its use in radiotherapy.
  • Generative Adversarial Networks (GANs), specifically Cycle-GAN, have been explored to improve CBCT image quality.
  • Existing GAN approaches face challenges with image scaling due to patient size and slice variations, and require quality improvements.

Purpose of the Study:

  • To develop an improved generative model for synthesizing high-quality CT images from CBCT data.
  • To address the scaling problem in GAN-based CBCT image synthesis.
  • To enhance the applicability of CBCT in radiation medicine through improved image quality.

Main Methods:

  • Introduction of the Enhanced Edge and Mask (EEM) approach within a Cycle-GAN framework, termed Cycle-EEM-GAN.
  • Utilizing structural constraints to solve image scaling issues during synthetic CT (sCT) generation.
  • Training and validation using pelvic CT scan data from sixty patients.

Main Results:

  • Cycle-EEM-GAN significantly improved image quality metrics: Mean Absolute Error (MAE) decreased from 53.09 to 37.74, Root Mean Square Error (RMSE) from 185.22 to 146.63, and Spatial Nonuniformity (SNU) from 0.38 to 0.35.
  • Peak Signal to Noise Ratio (PSNR) increased from 24.68 to 32.33, and Structural Similarity Index (SSIM) improved from 0.624 to 0.981.
  • Visual evaluation and loss metrics demonstrated superior performance of Cycle-EEM-GAN over the standard Cycle-GAN.

Conclusions:

  • The Cycle-EEM-GAN effectively enhances CBCT image quality, producing clearer structural details.
  • The EEM approach successfully mitigates image scaling problems inherent in CBCT data.
  • This advancement promotes wider adoption and improved application of CBCT in radiotherapy planning and delivery.