An improved low-rank plus sparse unrolling network method for dynamic magnetic resonance imaging.

Background: Recent advances in deep learning have sparked new research interests in dynamic magnetic resonance imaging (MRI) reconstruction. However, existing deep learning-based approaches suffer from insufficient reconstruction efficiency and accuracy due to the lack of time correlation modeling during the reconstruction procedure.

Purpose: Inappropriate tensor processing steps and deep learning models may lead to not only a lack of modeling in the time dimension but also an increase in the overall size of the network.

Spatially-Aware Context Neural Networks.

A variety of computer vision tasks benefit significantly from increasingly powerful deep convolutional neural networks. However, the inherently local property of convolution operations prevents most existing models from capturing long-range feature interactions for improved performances. In this paper, we propose a novel module, called Spatially-Aware Context (SAC) block, to learn spatially-aware contexts by capturing multi-mode global contextual semantics for sophisticated long-range dependencies modeling.

An ultrasound image-based deep multi-scale texture network for liver fibrosis grading in patients with chronic HBV infection.

Background & Aims: The evaluation of the stage of liver fibrosis is essential in patients with chronic liver disease. However, due to the low quality of ultrasound images, the non-invasive diagnosis of liver fibrosis based on ultrasound images is still an outstanding question. This study aimed to investigate the diagnostic accuracy of a deep learning-based method in ultrasound images for liver fibrosis staging in multicentre patients.

Validity of scalar diffraction theory and effective medium theory for analysis of a blazed grating microstructure at oblique incidence.

The accuracy of scalar diffraction theory (SDT) and effective medium theory (EMT) for analyzing a blazed grating is quantitatively demonstrated by making a comparison of diffraction efficiencies calculated by the two simplified methods to exact results from the Fourier modal method (FMM). It is found that when the normalized period is more than fivefold wavelength of incident light at normal incidence and is more than about tenfold wavelength at larger incident angle, SDT can be used to easily analyze effectively the transmittance characteristics of a blazed grating with divergence less than 1%. Particularly, for zeroth-order diffraction when the groove depth is less than threefold wavelength, the transmittance calculated by SDT with refractive index of 1.