Deep convolutional neural networks (CNNs) can be trained to predict sequence types for brain MR images, according to new research published in the Journal of Digital Imaging.

The researchers analyzed 39 consecutive patients with 44 pathologically confirmed cirrhotic nodules who first received a CEUS exam, followed by a traditional follow-up ultrasound every three or four months for their study.

As part of the collaboration, Yale New Haven Hospital has been using the MRI system for a two-year study involving the American Heart Association. Results of the preliminary research will be announced early next year.

A new startup—Hyperfine—and its visionary leader Jonathan Rothberg are creating a new, portable MRI machine they believe can revolutionize the way doctors think about the technology.

Exposure to radioiodine can lead to DNA damage in thyroid cells and other tissue, but new research suggests current radiation protection principles may not be sufficiently monitoring radiation exposure to nuclear medicine professionals.

Discussions about AI and radiology often focus on the researchers who help develop the algorithms and radiologists themselves. But a new analysis published in Academic Radiology shines a light on another key role in the implementation of AI: the imaging informaticist.

Marketing-related initiatives, rather than proven clinical efficacy, are putting pressure on women to choose 3D mammography, according to an investigation covered in USA Today.

Deep learning neural networks can improve the detection of tuberculosis (TB) and provide health systems with considerable cost savings, according to new findings published in Scientific Reports.