The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …

What has happened in machine learning lately, and what does it mean for the future of
medical image analysis? Machine learning has witnessed a tremendous amount of attention …

For medical image analysis, there is always an immense need for rich details in an image.
Typically, the diagnosis will be served best if the fine details in the image are retained and …

High-resolution (HR) magnetic resonance images (MRI) provide detailed anatomical
information important for clinical application and quantitative image analysis. However, HR …

Magnetic resonance (MR) imaging plays an important role in clinical and brain exploration.
However, limited by factors such as imaging hardware, scanning time, and cost, it is …

Purpose To develop a super‐resolution technique using convolutional neural networks for
generating thin‐slice knee MR images from thicker input slices, and compare this method …

Magnetic resonance imaging (MRI) can present multi-contrast images of the same
anatomical structures, enabling multi-contrast super-resolution (SR) techniques. Compared …

The purpose of super-resolution approaches is to overcome the hardware limitations and
the clinical requirements of imaging procedures by reconstructing high-resolution images …

Magnetic resonance image (MRI) in high spatial resolution provides detailed anatomical
information and is often necessary for accurate quantitative analysis. However, high spatial …

High resolution magnetic resonance (MR) images are desired in many clinical and research
applications. Acquiring such images with high signal-to-noise (SNR), however, can require a …