ArticleMagnetic resonance in medicine2023
Deep learning-based local SAR prediction using B
Article in Magnetic resonance in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 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
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
purposeTo predict subject-specific local specific absorption rate (SAR) distributions of the human head for parallel transmission (pTx) systems at 7 T. THEORY AND
methodsElectromagnetic energy deposition in tissues is nonuniform at 7 T, and interference patterns due to individual channels of pTx systems may result in increased local SAR values, which can only be estimated with very high safety margins. We proposed, designed, and demonstrated a multichannel 3D convolutional neural network (CNN) architecture to predict local SAR maps as well as peak-spatial SAR (ps-SAR) levels. We hypothesized that utilizing a three-channel 3D CNN, in which each channel is fed by a
resultsThe proposed three-channel 3D CNN predicted ps-SAR
conclusionMultichannel 3D CNN networks can be promising in predicting local SAR values and perform predictions within a second, making them clinically useful as an alternative to virtual observation points-based methods.
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Registered trials
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