This episode reviews a proof-of-concept study from Mayo Clinic Minnesota on the use of ferumoxytol-enhanced MRI for detecting gastrointestinal bleeding after a comprehensive conventional workup has been negative. We examine how this blood pool agent's prolonged intravascular half-life addresses the diagnostic challenge of slow and intermittent GI bleeding, and discuss the clinical implications for patient management.

Feasibility of ferumoxytol-enhanced MRI for detection of gastrointestinal bleeding when conventional evaluation is negative. Wells et al. Radiology Advances, 2026, 3, umaf043.

This episode reviews a study from the ROADMAP Group evaluating deep reinforcement learning for automatic aortic landmark localization in Stanford Type B aortic dissection — examining whether AI can match expert human performance for a task critical to treatment planning and long-term surveillance.

Deep reinforcement learning for automatic anatomic CT landmark localization in Stanford Type B aortic dissection. Baeumler et al. Radiology Advances, 2026, 3, umag006.

This episode explores a technological advance from Johns Hopkins in the United States that improves diagnostic ultrasound for breast masses. By combining short-lag spatial coherence imaging with an objective metric called generalized contrast-to-noise ratio, the researchers achieved a dramatic boost in diagnostic accuracy—especially in dense breast tissue—while reducing variability among radiologists and avoiding misclassification of cancers.

Generalized contrast-to-noise ratio applied to short-lag
spatial coherence ultrasound differentiates breast cysts
from solid masses. Sharma et al. Radiology Advances, 2025, 2(6), umaf037.