Claim OwnershipRadiology Advances Podcast | RSNA
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Description
A podcast showcasing articles from the Radiology Advances journal.
Podcast Team
Lead Podcast Editor- Diego Lopez-Gonzalez, MD, MPH,
Trainee Editors- Nelson Gil, MD, PhD and Luca Salhöfer, MD
Podcast Team
Lead Podcast Editor- Diego Lopez-Gonzalez, MD, MPH,
Trainee Editors- Nelson Gil, MD, PhD and Luca Salhöfer, MD
16 Episodes
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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.
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. Deep learning MRI halves scan time and preserves image quality across routine neuroradiologic examinations. Lyo et al. Radiology Advances, 2025, 2(5), umaf029.
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. Contrast-enhanced CT as a non-invasive alternative for lung shunt fraction estimation in hepatic transarterial radioembolization. Mehadji et al. Radiology Advances, 2025, 2(4), umaf025.
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. Deep-learning-accelerated T1-MPRAGE MRI for quantification and visual grading of cerebral volume in memory loss patients. Gil et al. Radiology Advances, 2025, 2(4), umaf022
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. Deep learning pipeline for fully automated myocardial infarct segmentation from clinical cardiac MR scans. Schwab et al. Radiology Advances, 2025, 2(4), umaf023.
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. Quantifying changes in steatotic liver disease after bariatric surgery using ultrasound-derived fat fraction. Nanda Thimmappa, Gaballah, Labyed, et al. Radiology Advances, 2025, 2(3), umaf018
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. Cross-institutional automated multilabel segmentation for acute intracerebral hemorrhage, intraventricular hemorrhage, and perihematomal edema on CT. Nawabi, Baumgartner, Penzkofer, et al. Radiology Advances, 2025, 2(2), umaf012
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. Comparison of robotic versus manual needle insertion for CT-guided intervention: prospective randomized trial. Radiology Advances, 2025, 2(2), umaf010
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. MRAnnotator: multi-anatomy and many-sequence MRI segmentation of 44 structures. Radiology Advances, 2025, 2(1), umae035.
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.
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. Pulmonary embolism detection without intravenous contrast using electron density and Z-effective maps from dual-energy CT. Radiology Advances, Volume 1, Issue 3, September 2024, umae025.
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