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 …

Purpose Artificial intelligence (AI) models are playing an increasing role in biomedical
research and healthcare services. This review focuses on challenges points to be clarified …

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 …

COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing
life around the world to a frightening halt and claiming thousands of lives. Due to COVID …

Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The
potential applications are vast and include the entirety of the medical imaging life cycle from …

Deep learning has become the most widely used approach for cardiac image segmentation
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …

Deep convolutional neural networks have performed remarkably well on many Computer
Vision tasks. However, these networks are heavily reliant on big data to avoid overfitting …

Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …

Low-dose computed tomography (LDCT) has drawn major attention in the medical imaging
field due to the potential health risks of CT-associated X-ray radiation to patients. Reducing …

Compressive sensing (CS) is an effective technique for reconstructing image from a small
amount of sampled data. It has been widely applied in medical imaging, remote sensing …