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 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.