This paper presents a review of deep learning (DL)-based medical image registration
methods. We summarized the latest developments and applications of DL-based registration …

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 …

PET imaging involves radiotracer injections, raising concerns about the risk of radiation
exposure. To minimize the potential risk, one way is to reduce the injected tracer. However …

Purpose Current clinical application of cone‐beam CT (CBCT) is limited to patient setup.
Imaging artifacts and Hounsfield unit (HU) inaccuracy make the process of CBCT‐based …

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

Deriving accurate structural maps for attenuation correction (AC) of whole-body positron
emission tomography (PET) remains challenging. Common problems include truncation …

Rationale and Objectives Generative adversarial networks (GANs) are deep learning
models aimed at generating fake realistic looking images. These novel models made a great …

Attenuation correction (AC) of PET/MRI faces challenges including inter-scan motion, image
artifacts such as truncation and distortion, and erroneous transformation of structural voxel …