A new three-dimensional (3D) tissue imaging technique can help scientists noninvasively study cells and may lead to improved treatments for a variety of diseases, according to research published in eLife.

“Search for an aneurysm is one of the most labor-intensive and critical tasks radiologists undertake,” said co-senior author Kristen Yeom, MD, associate professor of radiology at Stanford University.

A new deep learning approach lowered radiation exposure from CT imaging while producing higher quality scans compared to traditional iterative reconstruction techniques, according to research published in Nature Machine Intelligence.

Deep convolutional neural networks (DCNNs) can better classify chest x-rays when trained on augmented datasets, according to a new study published in Clinical Radiology.

Utilizing the ultrafast scan mode for CT imaging in the emergency department (ED) can significantly reduce motion artifacts, reported a team of Japan-based researchers in a study published by the American Journal of Roentgenology.

The Machine Learning Challenge on Pneumothorax Detection and Localization will kick-off at the SIIM 2019 Annual Meeting starting June 26 in Aurora, Colorado.

“With information obtained from this study, it is possible that neuroimaging in newborns may to some extent predict neurodevelopment even for healthy children, and prenatal intervention targeted at improving white matter integrity at birth will be important for further promoting neurodevelopment in children,” wrote researchers of a new Radiology study.

Aaron Williams was 16 years old when he committed suicide on the campus of his Charleston, South Carolina, high school in 2010. It was only until after the tragedy that neuroimaging revealed multiple lesions in his brain.