Notably lower T1 relaxation times, or how brain signals can weaken over time, may point to the presence of gadolinium in the brains of patients who’ve had more than one contrast-enhanced MRI exam, according to research published online Jan. 1 in Radiology.

Using less than 1,000 imaging cases, researchers from Massachusetts General Hospital (MGH) in Boston were able to train an artificial intelligence (AI) algorithm to detect intracranial hemorrhage (ICH) and classify its five subtypes on unenhanced head CT scans, according to research published in the journal Nature Biomedical Engineering.

A one-of-a-kind trial is underway testing the GE180 radioactive tracer in humans to potentially better understand the root causes of Alzheimer’s and Parkinson’s disease, the Las Vegas Review-Journal reported.

New research published in The American Journal of Psychiatry suggests the brain function of individuals with schizophrenia is more nuanced than previously thought.

A new feature for a personal diabetes management application leverages AI to predict the likelihood of a user experiencing a hypoglycemic event in the next one to four hours.

Researchers have successfully used two different machine learning algorithms to predict three common symptoms—sleep disturbance, anxiety and depression—experienced by cancer patients undergoing chemotherapy. The team's findings were published in PLOS One.

The team, led by Terisa P. Gabrielsen, PhD, assistant professor at Brigham Young University in Provo, Utah, successfully conducted structural and functional MRI scans of 37 children and adolescents between the ages of 7 and 17 years with autism—including 17 with less-developed language skills, according to research published online Jan. 2 in the journal Molecular Autism.

Using a technique that combines two MRI scans, researchers have developed an objective way to monitor concussions, according to a study published in NeuroImage: Clinical.