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.

This episode highlights a study from Korea using deep learning to generate synthetic contrast-enhanced brain MRI images—without injecting contrast agents. The model accurately segmented the choroid plexus and matched real contrast-enhanced scans in volume analysis, offering a potentially safer, scalable tool for neuroimaging.

Automated synthetic contrast-enhanced MRI improves
choroid plexus segmentation in Parkinsonian syndromes. Ambaye et al. Radiology Advances, 2025, 2(6), umaf042

This episode explores a study from Radiology Advances tackling one of AI's toughest challenges in medical imaging: consistent pancreas segmentation across CT scans. The authors benchmarked multiple models against multi-reader human consensus and introduced a new metric, Fractional Threshold (FT), to measure robustness. Their human-in-the-loop workflow flagged just 5% of cases for expert review, matching human reliability while cutting annotation time 23-fold.
Benchmarking Robustness of Automated CT Pancreas Segmentation: Achieving Human-Level Reliability Through Human-in-the-Loop Optimization. Oviedo et al. Radiology Advances, Volume 2, Issue 6, November 2025, umaf040,

This episode explores a study from Radiology Advances challenging FDA's acoustic output limits for liver ultrasound elastography for obese patients. The authors tested the exam at a mechanical index of 2.5, well above the 1.9 regulatory ceiling, and found no liver injury using stringent biochemical criteria. The payoff: a 29.2% reduction in measurement variability and 40% fewer failed attempts in obese participants, potentially transforming metabolic dysfunction associated steatotic liver disease screening in the population that needs it most.

Liver shear wave elastography using a mechanical index
exceeding regulatory limits is safe and effective. Pierce et al. Radiology Advances, 2025, 2(6), umaf034.

This episode features a cutting-edge study from Radiology Advances exploring Deep Silicon Photon-Counting CT (DS-IPCCT) for liver fat quantification. Using in silico models, the investigational system demonstrated high spectral accuracy, robust material decomposition, and low error rates—potentially overcoming key limitations of conventional CT and MRI.

Liver fat quantification using deep silicon photon-counting CT: an in silico imaging study. Panta et al. Radiology Advances, 2025, 2(5), umaf031.

This episode explores Radiology Advances research on RadGPT—a hybrid AI system combining image analysis with a language model to interpret knee radiographs. Built on 77,000 images, the system incorporates mandatory human review, dramatically improving diagnostic accuracy and report quality. Host commentary highlights its potential as a diagnostic assistant for trainees and an efficiency tool for experts.

Visual-language artificial intelligence system for knee radiograph diagnosis and interpretation: a collaborative system with humans. He et al. Radiology Advances, 2025, 2(5), umaf027.