Deep learning based synthetic-CT generation in radiotherapy and PET: a review
Recently,deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinic...
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| Main Authors: | , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
18 August 2021
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| In: |
Medical physics
Year: 2021, Volume: 48, Issue: 11, Pages: 6537-6566 |
| ISSN: | 2473-4209 |
| DOI: | 10.1002/mp.15150 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1002/mp.15150 Verlag, lizenzpflichtig, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/mp.15150 
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| Author Notes: | Maria Francesca Spadea, Matteo Maspero, Paolo Zaffino, Joao Seco |
| Summary: | Recently,deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: (i) to replace computed tomography in magnetic resonance (MR) based treatment planning, (ii) facilitate cone-beam computed tomography based image-guided adaptive radiotherapy, and (iii) derive attenuation maps for the correction of positron emission tomography. Appropriate database searching was performed on journal articles published between January 2014 and December 2020. The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarizing the achievements. Lastly, the statistics of all the cited works from various aspects were analyzed, revealing the popularity and future trends and the potential of DL-based sCT generation. The current status of DL-based sCT generation was evaluated, assessing the clinical readiness of the presented methods. |
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| Item Description: | Gesehen am 01.06.2022 |
| Physical Description: | Online Resource |
| ISSN: | 2473-4209 |
| DOI: | 10.1002/mp.15150 |