Unsupervised Deep Learning for Synthetic CT Generation from CBCT Images for Proton and Carbon Ion Therapy for Paediatric Patients
Image-guided treatment adaptation is a game changer in oncological particle therapy (PT), especially for younger patients. The purpose of this study is to present a cycle generative adversarial network (CycleGAN)-based method for synthetic computed tomography (sCT) generation from cone beam CT (CBCT...
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2024-11-01
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| author | Matteo Pepa Siavash Taleghani Giulia Sellaro Alfredo Mirandola Francesca Colombo Sabina Vennarini Mario Ciocca Chiara Paganelli Ester Orlandi Guido Baroni Andrea Pella |
| author_facet | Matteo Pepa Siavash Taleghani Giulia Sellaro Alfredo Mirandola Francesca Colombo Sabina Vennarini Mario Ciocca Chiara Paganelli Ester Orlandi Guido Baroni Andrea Pella |
| author_sort | Matteo Pepa |
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| description | Image-guided treatment adaptation is a game changer in oncological particle therapy (PT), especially for younger patients. The purpose of this study is to present a cycle generative adversarial network (CycleGAN)-based method for synthetic computed tomography (sCT) generation from cone beam CT (CBCT) towards adaptive PT (APT) of paediatric patients. Firstly, 44 CBCTs of 15 young pelvic patients were pre-processed to reduce ring artefacts and rigidly registered on same-day CT scans (i.e., verification CT scans, vCT scans) and then inputted to the CycleGAN network (employing either Res-Net and U-Net generators) to synthesise sCT. In particular, 36 and 8 volumes were used for training and testing, respectively. Image quality was evaluated qualitatively and quantitatively using the structural similarity index metric (SSIM) and the peak signal-to-noise ratio (PSNR) between registered CBCT (rCBCT) and vCT and between sCT and vCT to evaluate the improvements brought by CycleGAN. Despite limitations due to the sub-optimal input image quality and the small field of view (FOV), the quality of sCT was found to be overall satisfactory from a quantitative and qualitative perspective. Our findings indicate that CycleGAN is promising to produce sCT scans with acceptable CT-like image texture in paediatric settings, even when CBCT with narrow fields of view (FOV) are employed. |
| format | Article |
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| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
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| spelling | doaj-art-bc947236dbf04a5e987a4205c1bb72932024-12-13T16:31:36ZengMDPI AGSensors1424-82202024-11-012423746010.3390/s24237460Unsupervised Deep Learning for Synthetic CT Generation from CBCT Images for Proton and Carbon Ion Therapy for Paediatric PatientsMatteo Pepa0Siavash Taleghani1Giulia Sellaro2Alfredo Mirandola3Francesca Colombo4Sabina Vennarini5Mario Ciocca6Chiara Paganelli7Ester Orlandi8Guido Baroni9Andrea Pella10Bioengineering Unit, Clinical Department, CNAO National Centre for Oncological Hadrontherapy, 27100 Pavia, ItalyDepartment of Electronics, Information and Bioengineering, Politecnico di Milano (POLIMI), 20133 Milan, ItalyBioengineering Unit, Clinical Department, CNAO National Centre for Oncological Hadrontherapy, 27100 Pavia, ItalyMedical Physics Unit, Clinical Department, CNAO National Centre for Oncological Hadrontherapy, 27100 Pavia, ItalyRadiation Oncology Unit, Clinical Department, CNAO National Centre for Oncological Hadrontherapy, 27100 Pavia, ItalyPaediatric Radiotherapy Unit, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), 20133 Milan, ItalyMedical Physics Unit, Clinical Department, CNAO National Centre for Oncological Hadrontherapy, 27100 Pavia, ItalyDepartment of Electronics, Information and Bioengineering, Politecnico di Milano (POLIMI), 20133 Milan, ItalyRadiation Oncology Unit, Clinical Department, CNAO National Centre for Oncological Hadrontherapy, 27100 Pavia, ItalyDepartment of Electronics, Information and Bioengineering, Politecnico di Milano (POLIMI), 20133 Milan, ItalyBioengineering Unit, Clinical Department, CNAO National Centre for Oncological Hadrontherapy, 27100 Pavia, ItalyImage-guided treatment adaptation is a game changer in oncological particle therapy (PT), especially for younger patients. The purpose of this study is to present a cycle generative adversarial network (CycleGAN)-based method for synthetic computed tomography (sCT) generation from cone beam CT (CBCT) towards adaptive PT (APT) of paediatric patients. Firstly, 44 CBCTs of 15 young pelvic patients were pre-processed to reduce ring artefacts and rigidly registered on same-day CT scans (i.e., verification CT scans, vCT scans) and then inputted to the CycleGAN network (employing either Res-Net and U-Net generators) to synthesise sCT. In particular, 36 and 8 volumes were used for training and testing, respectively. Image quality was evaluated qualitatively and quantitatively using the structural similarity index metric (SSIM) and the peak signal-to-noise ratio (PSNR) between registered CBCT (rCBCT) and vCT and between sCT and vCT to evaluate the improvements brought by CycleGAN. Despite limitations due to the sub-optimal input image quality and the small field of view (FOV), the quality of sCT was found to be overall satisfactory from a quantitative and qualitative perspective. Our findings indicate that CycleGAN is promising to produce sCT scans with acceptable CT-like image texture in paediatric settings, even when CBCT with narrow fields of view (FOV) are employed.https://www.mdpi.com/1424-8220/24/23/7460proton therapycarbon ion therapyadaptive particle therapypaediatric oncologyCBCTsynthetic CT |
| spellingShingle | Matteo Pepa Siavash Taleghani Giulia Sellaro Alfredo Mirandola Francesca Colombo Sabina Vennarini Mario Ciocca Chiara Paganelli Ester Orlandi Guido Baroni Andrea Pella Unsupervised Deep Learning for Synthetic CT Generation from CBCT Images for Proton and Carbon Ion Therapy for Paediatric Patients Sensors proton therapy carbon ion therapy adaptive particle therapy paediatric oncology CBCT synthetic CT |
| title | Unsupervised Deep Learning for Synthetic CT Generation from CBCT Images for Proton and Carbon Ion Therapy for Paediatric Patients |
| title_full | Unsupervised Deep Learning for Synthetic CT Generation from CBCT Images for Proton and Carbon Ion Therapy for Paediatric Patients |
| title_fullStr | Unsupervised Deep Learning for Synthetic CT Generation from CBCT Images for Proton and Carbon Ion Therapy for Paediatric Patients |
| title_full_unstemmed | Unsupervised Deep Learning for Synthetic CT Generation from CBCT Images for Proton and Carbon Ion Therapy for Paediatric Patients |
| title_short | Unsupervised Deep Learning for Synthetic CT Generation from CBCT Images for Proton and Carbon Ion Therapy for Paediatric Patients |
| title_sort | unsupervised deep learning for synthetic ct generation from cbct images for proton and carbon ion therapy for paediatric patients |
| topic | proton therapy carbon ion therapy adaptive particle therapy paediatric oncology CBCT synthetic CT |
| url | https://www.mdpi.com/1424-8220/24/23/7460 |
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