The digital transformation in healthcare, propelled by the integration of deep learning
models and the Internet of Things (IoT), is creating unprecedented opportunities for …

Generative AI (GenAI) has witnessed remarkable progress in recent years and
demonstrated impressive performance in various generation tasks in different domains such …

Medical image arbitrary-scale super-resolution (MIASSR) has recently gained widespread
attention, aiming to supersample medical volumes at arbitrary scales via a single model …

Background Deep learning (DL) reconstructions can enhance image quality while
decreasing MRI acquisition time. However, DL reconstruction methods combined with …

Magnetic particle imaging (MPI) offers exceptional contrast for magnetic nanoparticles
(MNP) at high spatio-temporal resolution. A common procedure in MPI starts with a …

There is a growing demand for high-resolution (HR) medical images for both clinical and
research applications. Image quality is inevitably traded off with acquisition time, which in …

Objectives To evaluate the effect of super-resolution deep-learning-based reconstruction
(SR-DLR) on the image quality of coronary CT angiography (CCTA). Methods Forty-one …

Super-resolution (SR) reconstruction is a hot topic in medical image processing. SR implies
reconstructing corresponding high-resolution (HR) images from observed low-resolution …

This article reviews the deep learning methods for computed tomography image denoising
and deblurring separately and simultaneously. Then, we discuss promising directions in this …

Cardiac magnetic resonance (CMR) is an essential clinical tool for the assessment of
cardiovascular disease. Deep learning (DL) has recently revolutionized the field through …