Deep learning based approaches have been used to improve image quality in cone-beam
computed tomography (CBCT), a medical imaging technique often used in applications such …

This study investigates, through a narrative review, the transformative impact of deep
learning (DL) in the field of radiotherapy, particularly in light of the accelerated …

Background and purpose Efficient workflows for adaptive proton therapy are of high
importance. This study evaluated the possibility to replace repeat-CTs (reCTs) with synthetic …

Background and purpose To improve cone-beam computed tomography (CBCT), deep-
learning (DL)-models are being explored to generate synthetic CTs (sCT). The sCT …

Background and purpose: In radiotherapy, dose calculations based on 4D cone beam CTs
(4DCBCTs) require image intensity corrections. This retrospective study compared the dose …

Background Image‐guided neurosurgery requires high localization and registration
accuracy to enable effective treatment and avoid complications. However, accurate …

Background The application of cone‐beam computed tomography (CBCT) in image‐guided
radiotherapy and adaptive radiotherapy remains limited due to its poor image quality …

Objective. Clinical implementation of synthetic CT (sCT) from cone-beam CT (CBCT) for
adaptive radiotherapy necessitates a high degree of anatomical integrity, Hounsfield unit …

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

Radiation therapy has dramatically changed with the advent of computed tomography and
intensity modulation. This added complexity to the workflow but allowed for more precise …