Synthetic Computed Tomography generation using deep-learning for female pelvic radiotherapy planning
Synthetic Computed Tomography (sCT) is required to provide electron density information for MR-only radiotherapy. Deep-learning (DL) methods for sCT generation show improved dose congruence over other sCT generation methods (e.g. bulk density). Using 30 female pelvis datasets to train a cycleGAN-ins...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Physics and Imaging in Radiation Oncology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405631625000247 |
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| author | Rachael Tulip Sebastian Andersson Robert Chuter Spyros Manolopoulos |
| author_facet | Rachael Tulip Sebastian Andersson Robert Chuter Spyros Manolopoulos |
| author_sort | Rachael Tulip |
| collection | DOAJ |
| description | Synthetic Computed Tomography (sCT) is required to provide electron density information for MR-only radiotherapy. Deep-learning (DL) methods for sCT generation show improved dose congruence over other sCT generation methods (e.g. bulk density). Using 30 female pelvis datasets to train a cycleGAN-inspired DL model, this study found mean dose differences between a deformed planning CT (dCT) and sCT were 0.2 % (D98 %). Three Dimensional Gamma analysis showed a mean of 90.4 % at 1 %/1mm. This study showed accurate sCTs (dose) can be generated from routinely available T2 spin echo sequences without the need for additional specialist sequences. |
| format | Article |
| id | doaj-art-6bc706886b3e49919e38e1c5de63e3f1 |
| institution | Kabale University |
| issn | 2405-6316 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Physics and Imaging in Radiation Oncology |
| spelling | doaj-art-6bc706886b3e49919e38e1c5de63e3f12025-02-08T05:00:40ZengElsevierPhysics and Imaging in Radiation Oncology2405-63162025-01-0133100719Synthetic Computed Tomography generation using deep-learning for female pelvic radiotherapy planningRachael Tulip0Sebastian Andersson1Robert Chuter2Spyros Manolopoulos3Northern Centre for Cancer Care – North Cumbria, Newcastle upon Tyne Hospitals NHS Foundation Trust, Carlisle, Cumbria CA2 7HY, UK; Corresponding author.RaySearch Laboratories, Stockholm, SwedenChristie Medical Physics and Engineering (CMPE), The Christie NHS Foundation Trust, Wilmslow Road, Manchester M20 4BX, UK; Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UKNorthern Centre for Cancer Care – North Cumbria, Newcastle upon Tyne Hospitals NHS Foundation Trust, Carlisle, Cumbria CA2 7HY, UKSynthetic Computed Tomography (sCT) is required to provide electron density information for MR-only radiotherapy. Deep-learning (DL) methods for sCT generation show improved dose congruence over other sCT generation methods (e.g. bulk density). Using 30 female pelvis datasets to train a cycleGAN-inspired DL model, this study found mean dose differences between a deformed planning CT (dCT) and sCT were 0.2 % (D98 %). Three Dimensional Gamma analysis showed a mean of 90.4 % at 1 %/1mm. This study showed accurate sCTs (dose) can be generated from routinely available T2 spin echo sequences without the need for additional specialist sequences.http://www.sciencedirect.com/science/article/pii/S2405631625000247Synthetic CTMR-onlyDose validation |
| spellingShingle | Rachael Tulip Sebastian Andersson Robert Chuter Spyros Manolopoulos Synthetic Computed Tomography generation using deep-learning for female pelvic radiotherapy planning Physics and Imaging in Radiation Oncology Synthetic CT MR-only Dose validation |
| title | Synthetic Computed Tomography generation using deep-learning for female pelvic radiotherapy planning |
| title_full | Synthetic Computed Tomography generation using deep-learning for female pelvic radiotherapy planning |
| title_fullStr | Synthetic Computed Tomography generation using deep-learning for female pelvic radiotherapy planning |
| title_full_unstemmed | Synthetic Computed Tomography generation using deep-learning for female pelvic radiotherapy planning |
| title_short | Synthetic Computed Tomography generation using deep-learning for female pelvic radiotherapy planning |
| title_sort | synthetic computed tomography generation using deep learning for female pelvic radiotherapy planning |
| topic | Synthetic CT MR-only Dose validation |
| url | http://www.sciencedirect.com/science/article/pii/S2405631625000247 |
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