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

Magnetic resonance imaging (MRI) Super-Resolution (SR) aims to obtain high resolution
(HR) images with more detailed information for precise diagnosis and quantitative image …

Magnetic resonance imaging (MRI) is a medical imaging technique used to show
anatomical structures and physiological processes of the human body. Due to limitations like …

To circumvent the problems caused by prolonged acquisition periods, compressed sensing
MRI enjoys a high usage profile to accelerate the recovery of high-quality images from under …

Abstract Multi-modal Magnetic Resonance Imaging (MRI) super-resolution (SR) and
reconstruction aims to obtain a high-quality target image from corresponding sparsely …

To cope with the challenges stemming from prolonged acquisition periods, compressed
sensing MRI has emerged as a popular technique to accelerate the reconstruction of high …

Consistency and interpretability have long been the critical issues in MRI reconstruction.
While interpretability has been dramatically improved with the employment of deep …

In the realm of medical diagnosis, recent strides in deep neural network-guided magnetic
resonance imaging (MRI) restoration have shown promise. Nevertheless, persistent …

Objective. Multi-contrast magnetic resonance (MR) imaging super-resolution (SR)
reconstruction is an effective solution for acquiring high-resolution MR images. It utilizes …

Magnetic Resonance Imaging (MRI) is a widely used imaging technique, however it has the
limitation of long scanning time. Though previous model-based and learning-based MRI …