Teenagers who came into the world as “preemies” have altered brain connectivity on MRI, as well as differences in cognitive function, compared with their peers who spent the full nine months in utero.

Researchers at Facebook—you read those three words right—have developed a way to feed image data to AI visual-recognition models at a much faster pace than ever before, and with no compromise on precision. Radiology AI developers are sure to watch and learn. 

Harvard and MIT researchers have found that the more reading schoolkids do over the summer, the more their brains develop—and those who struggle with reading and come from low-income households stand to make the most impressive gains.

Artificial intelligence is driving change into pathology as well as radiology. In Canada, for example, researchers have developed a lens-less microscope that uses algorithms based on mathematical models of light to produce large-scale slide images in 3D.

Clinicians referring patients for imaging with nuclear and molecular modalities will soon be able to do so with ready access to utilization assistance that’s based on appropriate-use criteria developed by the Society of Nuclear Medicine and Molecular Imaging (SNMMI).

Both MRI and CT are highly accurate when it comes to detecting Crohn’s disease in the small intestine, and MRI can be the go-to modality when radiation exposure is an issue—as can be the case due to the repetitive imaging that’s often indicated for managing this chronic inflammatory bowel disease.

Mayo Clinic researchers have found a correlation between neuroimaging biomarkers of Alzheimer’s disease and results from a literal smell test: If at-risk older folks are losing their sense of smell, they’re more likely to be developing the disease than their well-smelling peers. 

Japanese scientists have developed a gamma-ray detector small enough to hold with one hand yet powerful enough to render high-resolution 3D molecular imaging of anatomic structures in mice.