Deep-learning-based tomographic imaging is an important application of artificial
intelligence and a new frontier of machine learning. Deep learning has been widely used in …

Filtered back projection (FBP) has been the standard CT image reconstruction method for 4
decades. A simple, fast, and reliable technique, FBP has delivered high-quality images in …

Recently, diffusion models have been used to solve various inverse problems in an
unsupervised manner with appropriate modifications to the sampling process. However, the …

The commonly used method to reduce the dose is to reduce the tube current. The number of
photons received by the detector decreases, making the CT image obtained by analytical …

New technologies are transforming medicine, and this revolution starts with data. Health
data, clinical images, genome sequences, data on prescribed therapies and results …

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 …

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 …

Diffusion models have emerged as the new state-of-the-art generative model with high
quality samples, with intriguing properties such as mode coverage and high flexibility. They …

Deep learning has attracted rapidly increasing attention in the field of tomographic image
reconstruction, especially for CT, MRI, PET/SPECT, ultrasound and optical imaging. Among …

We introduce a model-based image reconstruction framework with a convolution neural
network (CNN)-based regularization prior. The proposed formulation provides a systematic …