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.
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12:08
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12:08
CT as a Noninvasive Alternative for Lung Shunt Fraction Estimation
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.
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11:39
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11:39
Advancing MRI Efficiency in Memory Disorders
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
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11:25
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11:25
Automating Myocardial Infarct Segmentation
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.
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10:46
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10:46
Ultrasound-Derived Liver Fat Fraction After Bariatric Surgery
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 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
South Africa