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 …

Introduction The advent of image-guided radiation therapy (IGRT) has recently changed the
workflow of radiation treatments by ensuring highly collimated treatments. Artificial …

Objective This study aimed to improve the image quality and CT Hounsfield unit accuracy of
daily cone-beam computed tomography (CBCT) using registration generative adversarial …

Simple Summary Adaptive radiotherapy ensures precise radiation dose deposition to the
target volume while minimizing radiation-induced toxicities. However, due to poor image …

The patient setup technique currently in practice in most radiotherapy departments utilises
on-couch cone-beam computed tomography (CBCT) imaging. Patients are positioned on the …

Unsupervised deep learning network model cycle-consistent generative adversarial network
(CycleGAN) is increasingly applied for artifact correction of cone-beam computed …

Background The goal was to investigate the feasibility of the registration generative
adversarial network (RegGAN) model in image conversion for performing adaptive radiation …

The aim of this systematic review is to determine whether Deep Learning (DL) algorithms
can provide a clinically feasible alternative to classic algorithms for synthetic Computer …

Objectives The respiratory variations will lead to inconsistency between the actual delivery
dose and the planning dose. How the minor interfractional amplitude changes affect the …

Cone-beam computed tomography (CBCT) is routinely collected during image-guided
radiation therapy (IGRT) to provide updated patient anatomy information for cancer …