Free non-invasive prostate cancer screenings using multi-parametric MRI—which combines up to three different types of scans for a clearer image of the prostate—may soon be introduced in the U.K., according to a recent report by BBC News.

The Washington University School of Medicine in St. Louis (WUSTL) was awarded a five-year, $6.3 million grant to establish a new research center to develop and test novel PET tracers.

An artificial intelligence (AI) technique developed by engineers at MIT in Cambridge, Massachusetts may be used to illuminate transparent features in medical images taken with little to no light. The research was published online Dec. 12 in the journal Physical Review Letters.

At the RSNA’s 2018 Annual Meeting in Chicago, Aidoc announced a new partnership with the American College of Radiology Science Institute to create standard solutions for the integration of AI into medical imaging and radiologists’ daily workflow.

Radiology has continuously been on the forefront of adopting new technologies. But at one institution, it took a bit of training and exposure to existing interactive multimedia reporting features before radiologists were willing to adopt it into clinical practice.

Artificial intelligence (AI) can generate high-quality amyloid PET images from simultaneously acquired MR images and ultra-low-dose PET data, according to a Dec. 11 study published in Radiology.

Researchers from the Mayo Clinic in Rochester, Minnesota found that 98 percent of digital breast tomosynthesis-detected masses sent to ultrasound directly were adequately evaluated without the use of diagnostic mammography, according to research published Nov. 29 in the British Journal of Radiology.

A recent piece in the New York Times analyzed early results of the Adolescent Brain Cognitive Development (ABCD) Study covered during CBS’s “60 Minutes,” which associated increased screen usage with lower scores on aptitude tests and further brain processes.

“If radiologists are expected to utilize machine learning models safely and effectively for imaging interpretation, education for all levels of background and experience will be required, and a formalized machine learning curriculum targeted toward early career radiologists and trainees is urgently needed," Monica J. Wood, MD and colleagues wrote.

A deep learning algorithm developed by researchers at the Mayo Clinic in Rochester, Minnesota, segmented abdominal CT images to determine body composition similarly to, and at times, better than trained radiologists.