This paper reviewed the deep learning‐based studies for medical imaging synthesis and its
clinical application. Specifically, we summarized the recent developments of deep learning …

Abstract Recently, deep learning (DL)‐based methods for the generation of synthetic
computed tomography (sCT) have received significant research attention as an alternative to …

Background Daily or weekly cone‐beam computed tomography (CBCT) scans are
commonly used for accurate patient positioning during the image‐guided radiotherapy …

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 …

Objective To develop high-quality synthetic CT (sCT) generation method from low-dose
cone-beam CT (CBCT) images by using attention-guided generative adversarial networks …

Cone-beam computed tomography (CBCT) integrated with a linear accelerator is widely
used to increase the accuracy of radiotherapy and plays an important role in image-guided …

Purpose Image-guided radiation therapy could benefit from implementing adaptive radiation
therapy (ART) techniques. A cycle-generative adversarial network (cycle-GAN)-based cone …

Purpose Anatomical variations occur during head and neck (H&N) radiotherapy treatment.
kV cone‐beam computed tomography (CBCT) images can be used for daily dose monitoring …

This review provides an overview of the application of artificial intelligence (AI) in radiation
therapy (RT) from a radiation oncologist's perspective. Over the years, advances in …

Abstract Adaptive-radiation-therapy (ART) is applied to account for anatomical variations
observed over the treatment course. Daily or weekly cone-beam computed tomography …