Deep learning methods for enhancing cone-beam CT image quality toward adaptive radiation therapy: A systematic review.
Med Phys
; 49(9): 6019-6054, 2022 Sep.
Article
en En
| MEDLINE
| ID: mdl-35789489
ABSTRACT
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 anatomy to modify treatment parameters before irradiation. Poor CBCT image quality has been an impediment to realizing ART due to the increased scatter conditions inherent to cone-beam acquisitions. Given the recent interest in DL applications in radiation oncology, and specifically DL for CBCT correction, we provide a systematic theoretical and literature review for future stakeholders. The review encompasses DL approaches for synthetic CT generation, as well as projection domain methods employed in the CBCT correction literature. We review trends pertaining to publications from January 2018 to April 2022 and condense their major findings-with emphasis on study design and DL techniques. Clinically relevant endpoints relating to image quality and dosimetric accuracy are summarized, highlighting gaps in the literature. Finally, we make recommendations for both clinicians and DL practitioners based on literature trends and the current DL state-of-the-art methods utilized in radiation oncology.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Planificación de la Radioterapia Asistida por Computador
/
Aprendizaje Profundo
Tipo de estudio:
Guideline
/
Systematic_reviews
Límite:
Humans
Idioma:
En
Revista:
Med Phys
Año:
2022
Tipo del documento:
Article
País de afiliación:
Australia