COVID-19 Detection via Ultra-Low-Dose X-ray Images Enabled by Deep Learning.

The detection of Coronavirus disease 2019 (COVID-19) is crucial for controlling the spread of the virus. Current research utilizes X-ray imaging and artificial intelligence for COVID-19 diagnosis. However, conventional X-ray scans expose patients to excessive radiation, rendering repeated examinations impractical.

Navigating Uncertainty: Experiences of Older Adults in Wuhan during the 76-Day COVID-19 Lockdown.

The COVID-19 pandemic continues to affect the world. Wuhan, the epicenter of the outbreak, underwent a 76-day lockdown. Research has indicated that the lockdown negatively impacted the quality of life of older individuals, but little is known about their specific experiences during the confinement period.

A statistical deformation model-based data augmentation method for volumetric medical image segmentation.

The accurate delineation of organs-at-risk (OARs) is a crucial step in treatment planning during radiotherapy, as it minimizes the potential adverse effects of radiation on surrounding healthy organs. However, manual contouring of OARs in computed tomography (CT) images is labor-intensive and susceptible to errors, particularly for low-contrast soft tissue. Deep learning-based artificial intelligence algorithms surpass traditional methods but require large datasets.

Ring artifacts correction for computed tomography image using unsupervised contrastive learning.

Computed tomography (CT) is a widely employed imaging technology for disease detection. However, CT images often suffer from ring artifacts, which may result from hardware defects and other factors. These artifacts compromise image quality and impede diagnosis.

An unsupervised dual contrastive learning framework for scatter correction in cone-beam CT image.

Purpose: Cone-beam computed tomography (CBCT) is widely utilized in modern radiotherapy; however, CBCT images exhibit increased scatter artifacts compared to planning CT (pCT), compromising image quality and limiting further applications. Scatter correction is thus crucial for improving CBCT image quality.

Methods: In this study, we proposed an unsupervised contrastive learning method for CBCT scatter correction.

An attention-based deep convolutional neural network for ultra-sparse-view CT reconstruction.

X-ray Computed Tomography (CT) techniques play a vitally important role in clinical diagnosis, but radioactivity exposure can also induce the risk of cancer for patients. Sparse-view CT reduces the impact of radioactivity on the human body through sparsely sampled projections. However, images reconstructed from sparse-view sinograms often suffer from serious streaking artifacts.

Incorporating the synthetic CT image for improving the performance of deformable image registration between planning CT and cone-beam CT.

Objective: To develop a contrast learning-based generative (CLG) model for the generation of high-quality synthetic computed tomography (sCT) from low-quality cone-beam CT (CBCT). The CLG model improves the performance of deformable image registration (DIR).

Methods: This study included 100 post-breast-conserving patients with the pCT images, CBCT images, and the target contours, which the physicians delineated.

Sinogram domain metal artifact correction of CT via deep learning.

Purpose: Metal artifacts can significantly decrease the quality of computed tomography (CT) images. This occurs as X-rays penetrate implanted metals, causing severe attenuation and resulting in metal artifacts in the CT images. This degradation in image quality can hinder subsequent clinical diagnosis and treatment planning.