Artificial intelligence (AI) has recently become a very popular buzzword, as a consequence
of disruptive technical advances and impressive experimental results, notably in the field of …

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 …

Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …

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 and purpose Studies have demonstrated the potential of online adaptive
radiotherapy (oART). However, routine use has been limited due to resource demanding …

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

Purpose To improve image quality and computed tomography (CT) number accuracy of
daily cone beam CT (CBCT) through a deep learning methodology with generative …

As a popular probabilistic generative model, generative adversarial network (GAN) has
been successfully used not only in natural image processing, but also in medical image …

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 …

The need for time and attention given by the doctor to the patient, due to the increased
volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes …