Ultrasound-Derived Liver Fat Fraction After Bariatric Surgery
Radiology Advances Podcast | RSNA
Release Date: 09/17/2025
Deep Learning for Faster Neuro MRI
Radiology Advances Podcast | RSNA
This episode covers a study from Radiology Advances evaluating deep learning–accelerated MRI across routine neuroradiology exams. Using Siemens’ Deep Resolve, scan times were cut by over 50% without sacrificing diagnostic image quality. Host commentary explores reader preferences, artifacts, and when DL-MRI may be best suited for clinical use.
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CT as a Noninvasive Alternative for Lung Shunt Fraction Estimation
Radiology Advances Podcast | RSNA
This episode discusses a study from Radiology Advances evaluating contrast-enhanced CT as a non-invasive alternative for lung shunt fraction (LSF) estimation in hepatic radioembolization to the current standard, 99mTc-MAA nuclear medicine imaging. The proposed CT-based method showed strong correlation with standard MAA-based LSF, offering a faster, safer, and potentially more accurate planning approach without compromising clinical decision-making.
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Advancing MRI Efficiency in Memory Disorders
Radiology Advances Podcast | RSNA
This episode covers a study in Radiology Advances evaluating deep learning–accelerated T1 MPRAGE MRI in patients with memory loss. The approach cut scan time by more than half while preserving image quality and measurement accuracy—offering faster, more comfortable imaging for dementia care and longitudinal follow-up.
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Automating Myocardial Infarct Segmentation
Radiology Advances Podcast | RSNA
This episode spotlights a study from Radiology Advances introducing a fully automated deep learning pipeline for myocardial infarct segmentation on late gadolinium enhancement cardiac MRI. Developed at the Medical University of Innsbruck, the model showed near-perfect agreement with human experts and even outperformed manual segmentations in blinded qualitative review.
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Ultrasound-Derived Liver Fat Fraction After Bariatric Surgery
Radiology Advances Podcast | RSNA
A prospective study evaluates ultrasound-derived fat fraction (UDFF) as a tool to monitor hepatic steatosis after bariatric surgery. Host commentary unpacks how UDFF may offer a non-invasive, accessible, and quantitative alternative to MRI-PDFF and liver biopsy, and highlights UDFF’s clinical potential for routine liver fat surveillance.
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Automated Brain Hemorrhage Segmentationwith on CT
Radiology Advances Podcast | RSNA
A multi-center study evaluating an AI model for automated CT segmentation of intracerebral hemorrhage, intraventricular hemorrhage, and perihematomal edema. Host commentary highlights how the deep learning tool delivers near-expert accuracy in under 20 seconds—dramatically reducing time and enhancing precision in acute stroke care.
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The Robotic Edge in CT-Guided Procedures
Radiology Advances Podcast | RSNA
A prospective randomized trial compares robotic versus manual needle insertion for CT-guided intervention. Host commentary summarizes the results that show the robotic system matched manual accuracy and clinical success rates while significantly reducing radiation exposure to the interventionalist. The discussion touches on clinical implications for workflow, safety, and the evolving role of robotics in interventional radiology.
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AI-Powered Precision in MRI with MRAnnotator
Radiology Advances Podcast | RSNA
MRAnnotator is a deep learning model that segments 44 anatomical structures across diverse MRI sequences. Developed at Mount Sinai, it shows strong generalizability across scanners and sites, outperforming existing models. Host commentary summarizes the model development and datasets and explores its impact on AI development, annotation workflows, and multi-center research.
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The Future of Breast Cancer Screening with AI
Radiology Advances Podcast | RSNA
This episode explores a groundbreaking study from Radiology Advances evaluating the use of artificial intelligence as a second reader in screening mammography. Host commentary highlights how the AI-assisted workflow improved cancer detection, reduced radiologist workload, and enhanced reading efficiency, while also emphasizing the importance of thoughtful integration into clinical practice.
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Non-Contrast Dual-Energy CT for PE Detection
Radiology Advances Podcast | RSNA
In this ai generated episode of the Radiology Advances Podcast, we explore an innovative approach to detecting pulmonary embolism using dual-energy CT without intravenous contrast. Learn how electron density and Z-effective maps could offer a new option for patients with contraindications to contrast media.
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