ArticleMagnetic resonance in medicine2020
A deep learning method for image-based subject-specific local SAR assessment.
Article in Magnetic resonance in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed.
- Using U-Nets to Predict the Effects of Head Motion on Simulated Specific Absorption Rate for Ultra-High Field Magnetic Resonance Imaging With Parallel Transmission.Magnetic resonance in medicine · 2026Article
- Roadmap towards personalized approaches and safety considerations in non-ionizing radiation: from dosimetry to therapeutic and diagnostic applications.Physics in medicine and biology · 2026Review
- Feasibility study of subject-specific, brain specific-absorption-rate maps retrieved from MRI data.Magnetic resonance in medicine · 2025Article
- Deep learning-based whole-brain BMagnetic resonance in medicine · 2025Article
- Local SAR management strategies to use two-channel RF shimming for fetal MRI at 3 T.Magnetic resonance in medicine · 2024Article
- Deep learning-based local SAR prediction using BMagnetic resonance in medicine · 2023Article
- Rapid prediction of MRI-induced RF heating of active implantable medical devices using machine learning.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2023Article
- Application of Machine learning to predict RF heating of cardiac leads during magnetic resonance imaging at 1.5 T and 3 T: A simulation study.Journal of magnetic resonance (San Diego, Calif. : 1997) · 2023Article
- Progress in Understanding Radiofrequency Heating and Burn Injuries for Safer MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023Review
- Bench to bore ramifications of inter-subject head differences on RF shimming and specific absorption rates at 7T.Magnetic resonance imaging · 2022Article
- Article
- Ultra-high-field MR in Prostate cancer: Feasibility and Potential.Magma (New York, N.Y.) · 2022Review
- Personalized local SAR prediction for parallel transmit neuroimaging at 7T from a single T1-weighted dataset.Magnetic resonance in medicine · 2022Article
- Rigid motion-resolvedMagnetic resonance in medicine · 2022Article
- Safety and imaging performance of two-channel RF shimming for fetal MRI at 3T.Magnetic resonance in medicine · 2021Article
- Machine learning-based prediction of MRI-induced power absorption in the tissue in patients with simplified deep brain stimulation lead models.IEEE transactions on electromagnetic compatibility · 2021Article
- Real-time assessment of potential peak local specific absorption rate value without phase monitoring: Trigonometric maximization method for worst-case local specific absorption rate determination.Magnetic resonance in medicine · 2021Article
- Electrical Properties Tomography: A Methodological Review.Diagnostics (Basel, Switzerland) · 2021Review
- MRSaiFE: An AI-based Approach Towards the Real-Time Prediction of Specific Absorption Rate.IEEE access : practical innovations, open solutions · 2021Article
- Individualized SAR calculations using computer vision-based MR segmentation and a fast electromagnetic solver.Magnetic resonance in medicine · 2021Article
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Authors and funding
8 authors.
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Abstract
purposeLocal specific absorption rate (SAR) cannot be measured and is usually evaluated by offline numerical simulations using generic body models that of course will differ from the patient's anatomy. An additional safety margin is needed to include this intersubject variability. In this work, we present a deep learning-based method for image-based subject-specific local SAR assessment. We propose to train a convolutional neural network to learn a "surrogate SAR model" to map the relation between subject-specific
methodOur database of 23 subject-specific models with an 8-transmit channel body array for prostate imaging at 7 T was used to build 5750 training samples. These synthetic complex
resultsIn silico cross-validation shows a good qualitative and quantitative match between predicted and ground-truth local SAR distributions. The peak local SAR estimation error distribution shows a mean overestimation error of 15% with 13% probability of underestimation. The higher accuracy of the proposed method allows the use of less conservative safety factors compared with standard procedures. In vivo validation shows that the method is applicable with realistic measurement data with impressively good qualitative and quantitative agreement to simulations.
conclusionThe proposed deep learning method allows online image-based subject-specific local SAR assessment. It greatly reduces the uncertainty in current state-of-the-art SAR assessment methods, reducing the time in the examination protocol by almost 25%.
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