Medical image synthesis represents a critical area of research in clinical decision-making,
aiming to overcome the challenges associated with acquiring multiple image modalities for …

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 …

This review sets out to discuss the foremost applications of artificial intelligence (AI),
particularly deep learning (DL) algorithms, in single-photon emission computed tomography …

Medical imaging denoising faces great challenges, yet is in great demand. With its
distinctive characteristics, medical imaging denoising in the image domain requires …

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 Tendency is to moderate the injected activity and/or reduce acquisition time in PET
examinations to minimize potential radiation hazards and increase patient comfort. This …

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

Purpose Due to various physical degradation factors and limited counts received, PET
image quality needs further improvements. The denoising diffusion probabilistic model …