Comparison of the suitability of CBCT- and MR-based synthetic CTs for daily adaptive proton therapy in head and neck patients
Cone-beam computed tomography (CBCT)- and magnetic resonance (MR)-images allow a daily observation of patient anatomy but are not directly suited for accurate proton dose calculations. This can be overcome by creating synthetic CTs (sCT) using deep convolutional neural networks. In this study, we co...
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| Hauptverfasser: | , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
27 November 2020
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| In: |
Physics in medicine and biology
Year: 2020, Jahrgang: 65, Heft: 23 |
| ISSN: | 1361-6560 |
| DOI: | 10.1088/1361-6560/abb1d6 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1088/1361-6560/abb1d6 Verlag, lizenzpflichtig, Volltext: https://iopscience.iop.org/article/10.1088/1361-6560/abb1d6 
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| Verfasserangaben: | Adrian Thummerer, Bas A. de Jong, Paolo Zaffino, Arturs Meijers, Gabriel Guterres Marmitt, Joao Seco, Roel J.H.M. Steenbakkers, Johannes A. Langendijk, Stefan Both, Maria F. Spadea and Antje C. Knopf |
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| 245 | 1 | 0 | |a Comparison of the suitability of CBCT- and MR-based synthetic CTs for daily adaptive proton therapy in head and neck patients |c Adrian Thummerer, Bas A. de Jong, Paolo Zaffino, Arturs Meijers, Gabriel Guterres Marmitt, Joao Seco, Roel J.H.M. Steenbakkers, Johannes A. Langendijk, Stefan Both, Maria F. Spadea and Antje C. Knopf |
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| 520 | |a Cone-beam computed tomography (CBCT)- and magnetic resonance (MR)-images allow a daily observation of patient anatomy but are not directly suited for accurate proton dose calculations. This can be overcome by creating synthetic CTs (sCT) using deep convolutional neural networks. In this study, we compared sCTs based on CBCTs and MRs for head and neck (H&N) cancer patients in terms of image quality and proton dose calculation accuracy. A dataset of 27 H&N-patients, treated with proton therapy (PT), containing planning CTs (pCTs), repeat CTs, CBCTs and MRs were used to train two neural networks to convert either CBCTs or MRs into sCTs. Image quality was quantified by calculating mean absolute error (MAE), mean error (ME) and Dice similarity coefficient (DSC) for bones. The dose evaluation consisted of a systematic non-clinical analysis and a clinical recalculation of actually used proton treatment plans. Gamma analysis was performed for non-clinical and clinical treatment plans. For clinical treatment plans also dose to targets and organs at risk (OARs) and normal tissue complication probabilities (NTCP) were compared. CBCT-based sCTs resulted in higher image quality with an average MAE of 40 ± 4 HU and a DSC of 0.95, while for MR-based sCTs a MAE of 65 ± 4 HU and a DSC of 0.89 was observed. Also in clinical proton dose calculations, sCTCBCT achieved higher average gamma pass ratios (2%/2 mm criteria) than sCTMR (96.1% vs. 93.3%). Dose-volume histograms for selected OARs and NTCP-values showed a very small difference between sCTCBCT and sCTMR and a high agreement with the reference pCT. CBCT- and MR-based sCTs have the potential to enable accurate proton dose calculations valuable for daily adaptive PT. Significant image quality differences were observed but did not affect proton dose calculation accuracy in a similar manner. Especially the recalculation of clinical treatment plans showed high agreement with the pCT for both sCTCBCT and sCTMR. | ||
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| 700 | 1 | |a Marmitt, Gabriel Guterres |e VerfasserIn |4 aut | |
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