Researchers have developed a machine learning model that upgrades 3T MRI images to mimic the higher-resolution 7T MRI, providing enhanced detail for detecting brain abnormalities. The synthetic 7T images reveal finer features, such as white matter lesions and subcortical microbleeds, which are often difficult to see with standard MRI systems. This AI-driven approach could improve diagnostic accuracy for conditions like traumatic brain injury (TBI) and multiple sclerosis (MS), though clinical validation is needed before wider use.

A recent study used PET imaging to examine synaptic connections in autistic and non-autistic adults, marking the first time this technology was applied to autism research. Findings show autistic adults have fewer synapses, or connections between brain cells, which closely relates to social and communicative differences in autism.

In a breakthrough study, researchers have imaged a network of pathways in the human brain believed to clear waste proteins that can lead to Alzheimer’s and dementia. Using advanced MRI techniques, they revealed perivascular channels that guide cerebrospinal fluid through the brain, providing strong evidence of the glymphatic system's role in waste removal.

Penn AInSights, an AI-based imaging system, enhances radiology by creating precise 3D views of internal organs, enabling early detection of health issues like fatty liver disease and diabetes. By analyzing 2,000 scans per month, it helps clinicians screen for conditions beyond their primary focus. The system's seamless integration into existing workflows and its cost-effectiveness make it a game changer in healthcare, potentially extending lives through early intervention.