Towards MR-Only Radiotherapy in Head and Neck: Generation of Synthetic CT from Zero-TE MRI Using Deep Learning

Towards MR-Only Radiotherapy in Head and Neck: Generation of Synthetic CT from Zero-TE MRI Using Deep Learning

This study investigates the generation of synthetic CT (sCT) images from zero echo time (ZTE) MRI to support MR-only radiotherapy, which can reduce image registration errors and lower treatment planning costs. Since MRI lacks the electron density data required for accurate dose calculations, generat...

Full description

Saved in:
Bibliographic Details
Main Authors: Souha Aouadi, Mojtaba Barzegar, Alla Al-Sabahi, Tarraf Torfeh, Satheesh Paloor, Mohamed Riyas, Palmira Caparrotti, Rabih Hammoud, Noora Al-Hammadi
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Information
Subjects:
synthetic CT
deep learning
Zero-TE MRI
MRI-only radiotherapy
head and neck
Online Access:https://www.mdpi.com/2078-2489/16/6/477
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This study investigates the generation of synthetic CT (sCT) images from zero echo time (ZTE) MRI to support MR-only radiotherapy, which can reduce image registration errors and lower treatment planning costs. Since MRI lacks the electron density data required for accurate dose calculations, generating reliable sCTs is essential. ZTE MRI, offering high bone contrast, was used with two deep learning models: attention deep residual U-Net (ADR-Unet) and derived conditional generative adversarial network (cGAN). Data from 17 head and neck cancer patients were used to train and evaluate the models. ADR-Unet was enhanced with deep residual blocks and attention mechanisms to improve learning and reconstruction quality. Both models were implemented in-house and compared to standard U-Net and Unet++ architectures using image quality metrics, visual inspection, and dosimetric analysis. Volumetric modulated arc therapy (VMAT) planning was performed on both planning CT and generated sCTs. ADR-Unet achieved a mean absolute error of 55.49 HU and a Dice score of 0.86 for bone structures. All the models demonstrated Gamma pass rates above 99.4% and dose deviations within 2–3%, confirming clinical acceptability. These results highlight ADR-Unet and cGAN as promising solutions for accurate sCT generation, enabling effective MR-only radiotherapy.
ISSN:2078-2489

Similar Items