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 …

Nasopharyngeal carcinoma is a common head and neck malignancy with distinct clinical
management compared to other types of cancer. Precision risk stratification and tailored …

Over the last years, deep learning (DL) models have led to an enormous breakthrough in a
wide range of computer vision tasks, including the classification and quantification of internal …

Simple Summary Cone-beam computed tomography (CBCT) not only plays an important
role in image-guided radiation therapy (IGRT) but also has the potential for dose calculation …

We investigated the feasibility of the generation of synthetic CT (sCT) from CBCT images
with deep learning and the dose evaluation for CBCT-guided breast cancer adaptive …

Artificial intelligence (AI) has demonstrated great potential in a wide variety of applications in
interventional radiology (IR). Support for decision-making and outcome prediction, new …

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

Purpose To investigate the effect of different normalization preprocesses in deep learning on
the accuracy of different tissues in synthetic computed tomography (sCT) and to combine …

Purpose One-table treatments with treatment imaging, preparation and delivery occurring at
one treatment couch, could increase patients' comfort and throughput for palliative …

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