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.

There have been several manual, semi‐automatic and fully‐automatic methods proposed for verification of the position of mechanical isocenter as part of comprehensive quality assurance programs required for linear accelerator‐based stereotactic radiosurgery/radiotherapy (SRS/SRT) treatments. In this paper, a systematic review has been carried out to discuss the present methods for isocenter verification and compare their characteristics, to help physicists in making a decision on selection of their quality assurance routine.PACS numbers: 87.53.Ly, 87.56.Fc, 87.56.‐v