Magnetic resonance (MR) imaging visualises soft tissue contrast in exquisite detail without
harmful ionising radiation. In this work, we provide a state‐of‐the‐art review on the use of …

Deep learning (DL) has emerged as a leading approach in accelerating MRI. It employs
deep neural networks to extract knowledge from available datasets and then applies the …

Recently, it has become progressively more evident that classic diagnostic labels are unable
to accurately and reliably describe the complexity and variability of several clinical …

Multi-contrast magnetic resonance imaging can bring convenience to medical diagnosis and
decision-making. However, due to the long acquisition time, the images are easily disturbed …

Multi-contrast magnetic resonance (MR) imaging is an advanced technology used in
medical diagnosis, but the long acquisition process can lead to patient discomfort and limit …

Purpose Although recent convolutional neural network (CNN) methodologies have shown
promising results in fast MR imaging, there is still a desire to explore how they can be used …

Undersampling-space data in magnetic resonance imaging (MRI) reduces scan time but
pose challenges in image reconstruction. Considerable progress has been made in …

Multi-modal magnetic resonance imaging (MRI) plays a crucial role in comprehensive
disease diagnosis in clinical medicine. However, acquiring certain modalities, such as T2 …

Magnetic resonance (MR) imaging is widely used in the clinical field due to its non-
invasiveness, but the long scanning time is still a bottleneck for its popularization. Using the …

Since multiple MRI contrasts of the same anatomy contain redundant information, one
contrast can be used as a prior for guiding the reconstruction of an undersampled …