Highly robust reconstruction framework for three-dimensional optical imaging based on physical model constrained neural networks.

. The reconstruction of three-dimensional optical imaging that can quantitatively acquire the target distribution from surface measurements is a serious ill-posed problem. Traditional regularization-based reconstruction can solve such ill-posed problem to a certain extent, but its accuracy is highly dependent oninformation, resulting in a less stable and adaptable method.

High-Resolution Swin Transformer for Automatic Medical Image Segmentation.

The resolution of feature maps is a critical factor for accurate medical image segmentation. Most of the existing Transformer-based networks for medical image segmentation adopt a U-Net-like architecture, which contains an encoder that converts the high-resolution input image into low-resolution feature maps using a sequence of Transformer blocks and a decoder that gradually generates high-resolution representations from low-resolution feature maps. However, the procedure of recovering high-resolution representations from low-resolution representations may harm the spatial precision of the generated segmentation masks.

Phase function estimation from a diffuse optical image via deep learning.

The phase function is a key element of a light propagation model for Monte Carlo (MC) simulation, which is usually fitted with an analytic function with associated parameters. In recent years, machine learning methods were reported to estimate the parameters of the phase function of a particular form such as the Henyey-Greenstein phase function but, to our knowledge, no studies have been performed to determine the form of the phase function.Here we design a convolutional neural network (CNN) to estimate the phase function from a diffuse optical image without any explicit assumption on the form of the phase function.

Special Patterns of Dynamic Brain Networks Discriminate Between Face and Non-face Processing: A Single-Trial EEG Study.

Face processing is a spatiotemporal dynamic process involving widely distributed and closely connected brain regions. Although previous studies have examined the topological differences in brain networks between face and non-face processing, the time-varying patterns at different processing stages have not been fully characterized. In this study, dynamic brain networks were used to explore the mechanism of face processing in human brain.

A Thrombin-Responsive Nanoprobe for Visualization of Thrombus Formation through Three-Dimensional Optical/Computed Tomography Hybrid Imaging.

Early spontaneous detection of thrombin activation benefits precise theranostics for thrombotic vascular disease. Herein, a thrombin-responsive nanoprobe conjugated by a FITC dye, PEGylated FeO nanoparticles, and a thrombin-sensitive peptide (LASG) was constructed to visualize thrombin activation and subsequent thrombosis . The FITC dye was linked to the LASG coated on the FeO nanoparticles for sensing the thrombin activity via the Förster resonance energy transfer effect.

How Native Background Affects Human Performance in Real-World Visual Object Detection: An Event-Related Potential Study.

Visual processing refers to the process of perceiving, analyzing, synthesizing, manipulating, transforming, and thinking of visual objects. It is modulated by both stimulus-driven and goal-directed factors and manifested in neural activities that extend from visual cortex to high-level cognitive areas. Extensive body of studies have investigated the neural mechanisms of visual object processing using synthetic or curated visual stimuli.

Short-range and long-range neuronal oscillatory coupling in multiple frequency bands during face perception.

Neuronal oscillatory activity has been considered to play a key role in face processing through its functional effect on information flow and exchange in human brain. Specifically, most neuronal oscillatory activity is measured in different rhythm based on the electrophysiological signal at single channel level. Although, the neuronal oscillatory coupling between neuronal assembles is associated with the information flow and exchange between brain regions, few studies focus on this type of neuronal oscillatory activity in face processing.

Multi-atlas registration and adaptive hexahedral voxel discretization for fast bioluminescence tomography.

Bioluminescence tomography (BLT) has been a valuable optical molecular imaging technique to non-invasively depict the cellular and molecular processes in living animals with high sensitivity and specificity. Due to the inherent ill-posedness of BLT, a priori information of anatomical structure is usually incorporated into the reconstruction. The structural information is usually provided by computed tomography (CT) or magnetic resonance imaging (MRI).

Hybrid simplified spherical harmonics with diffusion equation for light propagation in tissues.

Aiming at the limitations of the simplified spherical harmonics approximation (SPN) and diffusion equation (DE) in describing the light propagation in tissues, a hybrid simplified spherical harmonics with diffusion equation (HSDE) based diffuse light transport model is proposed. In the HSDE model, the living body is first segmented into several major organs, and then the organs are divided into high scattering tissues and other tissues. DE and SPN are employed to describe the light propagation in these two kinds of tissues respectively, which are finally coupled using the established boundary coupling condition.

Molecular Optical Simulation Environment (MOSE): a platform for the simulation of light propagation in turbid media.

The study of light propagation in turbid media has attracted extensive attention in the field of biomedical optical molecular imaging. In this paper, we present a software platform for the simulation of light propagation in turbid media named the "Molecular Optical Simulation Environment (MOSE)". Based on the gold standard of the Monte Carlo method, MOSE simulates light propagation both in tissues with complicated structures and through free-space.

Influence investigation of a void region on modeling light propagation in a heterogeneous medium.

A void region exists in some biological tissues, and previous studies have shown that inaccurate images would be obtained if it were not processed. A hybrid radiosity-diffusion method (HRDM) that couples the radiosity theory and the diffusion equation has been proposed to deal with the void problem and has been well demonstrated in two-dimensional and three-dimensional (3D) simple models. However, the extent of the impact of the void region on the accuracy of modeling light propagation has not been investigated.