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 …

Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …

What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …

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

The goals of this review paper on deep learning (DL) in medical imaging and radiation
therapy are to (a) summarize what has been achieved to date;(b) identify common and …

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 …

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

Positron emission tomography (PET) is a widely used imaging modality, providing insight
into both the biochemical and physiological processes of human body. Usually, a full dose …

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

Purpose Image quality of positron emission tomography (PET) is limited by various physical
degradation factors. Our study aims to perform PET image denoising by utilizing prior …