Cargando…
Deep learning methods for enhancing cone‐beam CT image quality toward adaptive radiation therapy: A systematic review
The use of deep learning (DL) to improve cone‐beam CT (CBCT) image quality has gained popularity as computational resources and algorithmic sophistication have advanced in tandem. CBCT imaging has the potential to facilitate online adaptive radiation therapy (ART) by utilizing up‐to‐date patient ana...
Autores principales: | Rusanov, Branimir, Hassan, Ghulam Mubashar, Reynolds, Mark, Sabet, Mahsheed, Kendrick, Jake, Rowshanfarzad, Pejman, Ebert, Martin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9543319/ https://www.ncbi.nlm.nih.gov/pubmed/35789489 http://dx.doi.org/10.1002/mp.15840 |
Ejemplares similares
-
Generating patient‐matched 3D‐printed pedicle screw and laminectomy drill guides from Cone Beam CT images: Studies in ovine and porcine cadavers
por: Kanawati, Andrew, et al.
Publicado: (2022) -
Motion artifacts assessment and correction using optical tracking in synchrotron radiation breast CT
por: Brombal, Luca, et al.
Publicado: (2021) -
Radiomics for Identification and Prediction in Metastatic Prostate Cancer: A Review of Studies
por: Kendrick, Jake, et al.
Publicado: (2021) -
A framework for defining FLASH dose rate for pencil beam scanning
por: Folkerts, Michael M., et al.
Publicado: (2020) -
A pencil beam algorithm for magnetic resonance image‐guided proton therapy
por: Padilla‐Cabal, Fatima, et al.
Publicado: (2018)