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

Magnetic resonance imaging is a powerful imaging modality that can provide versatile
information. However, it has a fundamental challenge that is time consuming to acquire …

Fast and accurate MRI image reconstruction from undersampled data is crucial in clinical
practice. Deep learning based reconstruction methods have shown promising advances in …

In order to reach precision medicine and improve patients' quality of life, machine learning is
increasingly used in medicine. Brain disorders are often complex and heterogeneous, and …

Sparse recovery in the context of the inverse problem has become an enormously popular
technique in reconstructing various degraded images in various applications. One of the …

Since Magnetic Resonance Imaging (MRI) requires a long acquisition time, various methods
were proposed to reduce the time, but they ignored the frequency information and non-local …

Several studies have shown the excellent performance of deep learning in image
segmentation. Usually, this benefits from a large amount of annotated data. Medical image …

Supervised MRI reconstruction methods perform well when provided with matched
undersampled and fully sampled data pairs. However, acquiring paired data can be …

Iterative thresholding-type algorithm, as one of the typical methods of compressed sensing
(CS) theory, is widelyused in sparse recovery field, because of its simple computational …

Considering the problems of poor effect, long reconstruction time, large mean square error
(MSE), low signal‐to‐noise ratio (SNR), and structural similarity index (SSIM) of traditional …