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Learning‐based CBCT correction using alternating random forest based on auto‐context model
PURPOSE: Quantitative Cone Beam CT (CBCT) imaging is increasing in demand for precise image‐guided radiotherapy because it provides a foundation for advanced image‐guided techniques, including accurate treatment setup, online tumor delineation, and patient dose calculation. However, CBCT is currentl...
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| Pubblicato in: | Med Phys |
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| Autori principali: | , , , , , , , , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
John Wiley and Sons Inc.
2018
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7792987/ https://ncbi.nlm.nih.gov/pubmed/30471129 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/mp.13295 |
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