Deep learning techniques, in particular generative models, have taken on great importance
in medical image analysis. This paper surveys fundamental deep learning concepts related …

Compressed sensing (CS) has been playing a key role in accelerating the magnetic
resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence …

The performance of artificial intelligence (AI) for brain MRI can improve if enough data are
made available. Generative adversarial networks (GANs) showed a lot of potential to …

Artificial intelligence (AI)–based image enhancement has the potential to reduce scan times
while improving signal-to-noise ratio (SNR) and maintaining spatial resolution. This study …

Purpose Complementary information obtained from multiple contrasts of tissue facilitates
physicians assessing, diagnosing and planning treatment of a variety of diseases. However …

Generative adversarial networks (GAN) can produce images of improved quality but their
ability to augment image-based classification is not fully explored. We evaluated if a …

Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides
excellent soft-tissue contrast and high-resolution images of the human body, allowing us to …

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 …

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 …

Learning-based translation between MRI contrasts involves supervised deep models trained
using high-quality source-and target-contrast images derived from fully-sampled …