Synthetic CT image generation from CBCT: A Systematic Review

TY - JOUR

T1 - Synthetic CT image generation from CBCT

T2 - A Systematic Review

AU - Altalib, Alzahra'

AU - McGregor, Scott

AU - Li, Chunhui

AU - Perelli, Alessandro

PY - 2025

Y1 - 2025

N2 - The generation of synthetic Computed Tomography (sCT) images from cone-beam CT (CBCT) data using deep learning (DL) 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 DL approaches in the generation of sCT. This review comprehensively covers sCT 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.

AB - The generation of synthetic Computed Tomography (sCT) images from cone-beam CT (CBCT) data using deep learning (DL) 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 DL approaches in the generation of sCT. This review comprehensively covers sCT 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.

KW - Computed tomography (CT)

KW - cone beam CT (CBCT)

KW - deep learning (DL)

KW - synthetic CT (sCT)

UR - https://arxiv.org/abs/2501.13972

U2 - 10.1109/TRPMS.2025.3533749

DO - 10.1109/TRPMS.2025.3533749

M3 - Article

SN - 2469-7311

VL - 9

SP - 691

EP - 707

JO - IEEE Transactions on Radiation and Plasma Medical Sciences

JF - IEEE Transactions on Radiation and Plasma Medical Sciences

IS - 6

ER -