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Objectives: To evaluate at 1.5 and 3 T MRI the safety and performance of trademarked ENO, TEO, or OTO pacing systems with automated MRI Mode and the image quality of non-enhanced MR examinations.
Methods: A total of 267 implanted patients underwent MRI examination (brain, cardiac, shoulder, cervical spine) at 1.5 (n = 126) or 3 T (n = 141). MRI-related device complications, lead electrical performances stability at 1-month post-MRI, proper functioning of the automated MRI mode and image quality were evaluated.
Results: Freedom from MRI-related complications at 1 month post-MRI was 100% in both 1.5 and 3 T arms (both p < 0.0001). The stability of pacing capture threshold was respectively at 1.5 and 3 T (atrial:: 98.9% (p = 0.001) and 100% (p < 0.0001); ventricular: both 100% (p < 0001)). The stability of sensing was respectively at 1.5 and 3 T (atrial: 100% (p = 0.0001) and 96.9% (p = 0.01); ventricular: 100% (p < 0.0001) and 99.1% (p = 0.0001)). All devices switched automatically to the programmed asynchronous mode in the MRI environment and to initially programmed mode after the MRI exam. While all MR examinations were assessed as interpretable, artifacts deteriorated a subset of examinations including mostly cardiac and shoulder ones.
Conclusion: This study demonstrates the safety and electrical stability of ENO, TEO, or OTO pacing systems at 1 month post-MRI at 1.5 and 3 T. Even if artifacts were noticed in a subset of examinations, overall interpretability was preserved.
Clinical Relevance Statement: ENO, TEO, and OTO pacing systems switch to MR-mode when detecting magnetic field and switch back on conventional mode after MRI. Their safety and electrical stability at 1 month post MRI were shown at 1.5 and 3 T. Overall interpretability was preserved.
Key Points: • Patients implanted with an MRI conditional cardiac pacemaker can be safely scanned under 1.5 or 3 Tesla MRI with preserved interpretability. • Electrical parameters of the MRI conditional pacing system remain stable after a 1.5 or 3 Tesla MRI scan. • The automated MRI mode enabled the automatic switch to asynchronous mode in the MRI environment and to initial settings after the MRI scan in all patients.
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http://dx.doi.org/10.1007/s00330-023-09650-9 | DOI Listing |
J Appl Clin Med Phys
September 2025
Icon Cancer Centre Toowoomba, Toowoomba, Queensland, Australia.
Introduction: The role of imaging in radiotherapy is becoming increasingly important. Verification of imaging parameters prior to treatment planning is essential for safe and effective clinical practice.
Methods: This study described the development and clinical implementation of ImageCompliance, an automated, GUI-based script designed to verify and enforce correct CT and MRI parameters during radiotherapy planning.
AJNR Am J Neuroradiol
September 2025
From the Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, United States of America (J.S.S., B.M., S.H., A.H., J.S.), and Department of Aerospace Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India (H.S.).
Background And Purpose: The choroid of the eye is a rare site for metastatic tumor spread, and as small lesions on the periphery of brain MRI studies, these choroidal metastases are often missed. To improve their detection, we aimed to use artificial intelligence to distinguish between brain MRI scans containing normal orbits and choroidal metastases.
Materials And Methods: We present a novel hierarchical deep learning framework for sequential cropping and classification on brain MRI images to detect choroidal metastases.
Abdom Radiol (NY)
September 2025
Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
Objectives: The escalating global incidence of obesity, cardiometabolic disease and sarcopenia necessitates reliable body composition measurement tools. MRI-based assessment is the gold standard, with utility in both clinical and drug trial settings. This study aims to validate a new automated volumetric MRI method by comparing with manual ground truth, prior volumetric measurements, and against a new method for semi-automated single-slice area measurements.
View Article and Find Full Text PDFFront Neurol
August 2025
Department of Neuroradiology, Hôpital Maison-Blanche, Université Reims-Champagne-Ardenne, Reims, France.
Objective: This study evaluates age- and sex-related differences in brain volume, including normalized gray matter (nGM), normalized white matter (nWM), cerebrospinal fluid (CSF) volume, and total intracranial volume (TIV) in cognitively normal adults using automatic volume segmentation on 3.0 Tesla MRI.
Methods: A prospective cross-sectional study conducted from October 2021 to September 2022 included 110 cognitively normal participants.
J Magn Reson Imaging
September 2025
Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan City, Taiwan.
Background: Automated cardiac MR segmentation enables accurate and reproducible ventricular function assessment in Tetralogy of Fallot (ToF), whereas manual segmentation remains time-consuming and variable.
Purpose: To evaluate the deep learning (DL)-based models for automatic left ventricle (LV), right ventricle (RV), and LV myocardium segmentation in ToF, compared with manual reference standard annotations.
Study Type: Retrospective.