Physical activity and recreational screen time among Chinese children and adolescents: a national cross-sectional study.

Background: The increasing prevalence of physical inactivity and prolonged Recreational Screen Time (RST) among children and adolescents is emerging as a significant public health concern. This study investigates the current status of Physical Activity (PA) and RST among Chinese children and adolescents from 2017 to 2019. It also examines variations in PA and RST across different school levels, genders, urban-rural areas, regions, and seasons.

Clinical applications of neurolinguistics in neurosurgery.

The protection of language function is one of the major challenges of brain surgery. Over the past century, neurosurgeons have attempted to seek the optimal strategy for the preoperative and intraoperative identification of language-related brain regions. Neurosurgeons have investigated the neural mechanism of language, developed neurolinguistics theory, and provided unique evidence to further understand the neural basis of language functions by using intraoperative cortical and subcortical electrical stimulation.

Ordered subset expectation maximization algorithm for positron emission tomographic image reconstruction using belief kernels.

The aim of this study is to investigate the benefits of incorporating prior information in list mode, time-of-flight (TOF) positron emission tomography (PET) image reconstruction using the ordered subset expectation maximization (OSEM) algorithm. This investigation consists of an IEC phantom study and a patient study. For the image under reconstruction, the activity profile along a line of response is treated as and is combined with the TOF measurement to define a belief kernel used for forward and backward projections during the OSEM image reconstruction.

Fast PET Preview Image Reconstruction, Streaming, and Visualization During Data Acquisition: A Preliminary Study.

PET acquisition and reconstruction are time-consuming. A PET preview image is commonly reconstructed at the end of data acquisition of each bed-position frame in the step-and-shoot mode. We propose a scheme to reconstruct, stream, and visualize the PET preview image during acquisition to provide quasi-real-time visual feedback.

Full-Dose PET Image Estimation from Low-Dose PET Image Using Deep Learning: a Pilot Study.

Positron emission tomography (PET) imaging is an effective tool used in determining disease stage and lesion malignancy; however, radiation exposure to patients and technicians during PET scans continues to draw concern. One way to minimize radiation exposure is to reduce the dose of radioactive tracer administered in order to obtain the scan. Yet, low-dose images are inherently noisy and have poor image quality making them difficult to read.

Volume sweeping and bodyline matching for automated prealignment in volumetric medical image registration.

For an automated image registration to converge to a good registration, it is crucial that the initial registration is within the capture range of the true registration, as local optimization methods are frequently employed. The ways to set an initial registration in current practice are not ideal and it is highly desirable to automate this initial registration (prealignment). Two automatic prealignment methods are reported here.

Automatic patient table removal in CT images.

In many medical imaging applications, it is desirable and important to localize and remove the patient table from CT images. However, existing methods often require user interactions to define the table and sometimes make inaccurate assumptions about the table shape. Due to different patient table designs, shapes, and characteristics, these methods are not robust in identifying and removing the patient table.

Pixel-feature hybrid fusion for PET/CT images.

Color blending is a popular display method for functional and anatomic image fusion. The underlay image is typically displayed in grayscale, and the overlay image is displayed in pseudo colors. This pixel-level fusion provides too much information for reviewers to analyze quickly and effectively and clutters the display.

Likelihood maximization approach to image registration.

A likelihood maximization approach to image registration is developed in this paper. It is assumed that the voxel values in two images in registration are probabilistically related. The principle of maximum likelihood is then exploited to find the optimal registration: the likelihood that given image f, one has image g and given image g, one has image f is optimized with respect to registration parameters.

Mutual information-based registration of temporal and stereo retinal images using constrained optimization.

In the registration of temporal and stereo retinal images, the rotation angle is normally less than 5 degrees and the scaling factor is between 0.95 and 1.05.

A Java program for stereo retinal image visualization.

Stereo imaging of the optic-disc is a gold standard examination of glaucoma, and progression of glaucoma can be detected from temporal stereo images. A Java-based software system is reported here which automatically aligns the left and right stereo retinal images and presents the aligned images side by side, along with the anaglyph computed from the aligned images. Moreover, the disparity between two aligned images is computed and used as the depth cue to render the optic-disc images, which can be interactively edited, panned, zoomed, rotated, and animated, allowing one to examine the surface of the optic-nerve head from different view angles.

An object-oriented framework for medical image registration, fusion, and visualization.

An object-oriented framework for image registration, fusion, and visualization was developed based on the classic model-view-controller paradigm. The framework employs many design patterns to facilitate legacy code reuse, manage software complexity, and enhance the maintainability and portability of the framework. Three sample applications built a-top of this framework are illustrated to show the effectiveness of this framework: the first one is for volume image grouping and re-sampling, the second one is for 2D registration and fusion, and the last one is for visualization of single images as well as registered volume images.

Volume image registration by cross-entropy optimization.

Cross-entropy (CE), an information-theoretic measure, quantifies the difference between two probability density functions. This measure is applied to volume image registration. When a good prior estimation of the joint distribution of the voxel values of two images in registration is available, the CE can be minimized to find an optimal registration.

Influence of implementation parameters on registration of MR and SPECT brain images by maximization of mutual information.

Unlabelled: Mutual-information maximization is one of the most popular algorithms for automatic image registration. However, many implementation issues have not been evaluated in a single, coherent context.

Methods: Twenty-one registrations between MR and SPECT brain images (8 patients) were achieved by mutual-information maximization with different implementation strategies.