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...
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MDPI AG
2025-06-01
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| author | Souha Aouadi Mojtaba Barzegar Alla Al-Sabahi Tarraf Torfeh Satheesh Paloor Mohamed Riyas Palmira Caparrotti Rabih Hammoud Noora Al-Hammadi |
| author_facet | Souha Aouadi Mojtaba Barzegar Alla Al-Sabahi Tarraf Torfeh Satheesh Paloor Mohamed Riyas Palmira Caparrotti Rabih Hammoud Noora Al-Hammadi |
| author_sort | Souha Aouadi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b5d3ed2b1f6f46719481040dbbe3d404 |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-b5d3ed2b1f6f46719481040dbbe3d4042025-08-20T03:27:29ZengMDPI AGInformation2078-24892025-06-0116647710.3390/info16060477Towards MR-Only Radiotherapy in Head and Neck: Generation of Synthetic CT from Zero-TE MRI Using Deep LearningSouha Aouadi0Mojtaba Barzegar1Alla Al-Sabahi2Tarraf Torfeh3Satheesh Paloor4Mohamed Riyas5Palmira Caparrotti6Rabih Hammoud7Noora Al-Hammadi8Department of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, Doha 3050, QatarDepartment of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, Doha 3050, QatarDepartment of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, Doha 3050, QatarDepartment of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, Doha 3050, QatarDepartment of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, Doha 3050, QatarDepartment of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, Doha 3050, QatarDepartment of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, Doha 3050, QatarDepartment of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, Doha 3050, QatarDepartment of Radiation Oncology, National Center for Cancer Care and Research, Hamad Medical Corporation, Doha 3050, QatarThis 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.https://www.mdpi.com/2078-2489/16/6/477synthetic CTdeep learningZero-TE MRIMRI-only radiotherapyhead and neck |
| spellingShingle | Souha Aouadi Mojtaba Barzegar Alla Al-Sabahi Tarraf Torfeh Satheesh Paloor Mohamed Riyas Palmira Caparrotti Rabih Hammoud Noora Al-Hammadi Towards MR-Only Radiotherapy in Head and Neck: Generation of Synthetic CT from Zero-TE MRI Using Deep Learning Information synthetic CT deep learning Zero-TE MRI MRI-only radiotherapy head and neck |
| title | Towards MR-Only Radiotherapy in Head and Neck: Generation of Synthetic CT from Zero-TE MRI Using Deep Learning |
| title_full | Towards MR-Only Radiotherapy in Head and Neck: Generation of Synthetic CT from Zero-TE MRI Using Deep Learning |
| title_fullStr | Towards MR-Only Radiotherapy in Head and Neck: Generation of Synthetic CT from Zero-TE MRI Using Deep Learning |
| title_full_unstemmed | Towards MR-Only Radiotherapy in Head and Neck: Generation of Synthetic CT from Zero-TE MRI Using Deep Learning |
| title_short | Towards MR-Only Radiotherapy in Head and Neck: Generation of Synthetic CT from Zero-TE MRI Using Deep Learning |
| title_sort | towards mr only radiotherapy in head and neck generation of synthetic ct from zero te mri using deep learning |
| topic | synthetic CT deep learning Zero-TE MRI MRI-only radiotherapy head and neck |
| url | https://www.mdpi.com/2078-2489/16/6/477 |
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