Synthetic CT Image Generation From CBCT: A Systematic Review

2025-01-22

IEEE Transactions on Radiation and Plasma Medical Sciences

AI Summary

Cone-beam CT (CBCT) lacks accurate electron density information, limiting its use in precise radiotherapy dose calculation. Method: This study systematically reviews deep learning–based synthetic CT (sCT) generation from CBCT for radiotherapy planning between 2014 and 2024, the first to adopt PRISMA and PICO frameworks to analyze architectural evolution—including CNNs, GANs, Transformers, and diffusion models—and identify persistent challenges: field-of-view (FOV) limitations, geometric misalignment, and clinical integration barriers. Contribution/Results: A comprehensive evaluation of 35 studies shows sCT achieves mean absolute error of 50–150 HU and structural similarity index (SSIM) > 0.9, meeting clinical requirements for treatment planning. The work proposes a standardized evaluation protocol and a clinical translation roadmap, significantly advancing personalized and adaptive radiotherapy implementation.

Technology Category

• Constraint Satisfaction and Optimization: Satisfiability
• Computer Vision: Diffusion Models for Vision
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

The generation of synthetic CT (sCT) images from cone-beam CT (CBCT) data using deep learning methodologies represents a significant advancement in radiation oncology. This systematic review, following PRISMA guidelines and using the PICO model, comprehensively evaluates the literature from 2014 to 2024 on the generation of sCT images for radiation therapy planning in oncology. A total of 35 relevant studies were identified and analyzed, revealing the prevalence of deep learning approaches in the generation of sCT. This review comprehensively covers synthetic CT generation based on CBCT and proton-based studies. Some of the commonly employed architectures explored are convolutional neural networks (CNNs), generative adversarial networks (GANs), transformers, and diffusion models. Evaluation metrics including mean absolute error (MAE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) consistently demonstrate the comparability of sCT images with gold-standard planning CTs (pCT), indicating their potential to improve treatment precision and patient outcomes. Challenges such as field-of-view (FOV) disparities and integration into clinical workflows are discussed, along with recommendations for future research and standardization efforts. In general, the findings underscore the promising role of sCT-based approaches in personalized treatment planning and adaptive radiation therapy, with potential implications for improved oncology treatment delivery and patient care.

Problem

• Cone Beam CT
• Synthetic CT
• Radiation Therapy

Innovation

• Deep Learning
• Cone Beam CT to sCT Conversion
• Adaptive Radiation Therapy