ArticleMagnetic resonance in medicine2024
Unsupervised deep learning with convolutional neural networks for static parallel transmit design: A retrospective study.
Article in Magnetic resonance in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- SelExNet: A Self-Supervised Physics-Informed Framework for Multi-Channel Joint RF and Gradient Waveform Optimization in 2D Spatially Selective Excitation.Magnetic resonance in medicine · 2026Article
- Cross-Site Generalization of CNN-BasedNMR in biomedicine · 2026Article
- Exact analytical solutions of the Bloch equation for the hyperbolic-secant and chirp pulses.Magnetic resonance in medicine · 2025Article
- Fast-RF-Shimming: Accelerate RF shimming in 7T MRI using deep learning.Meta-radiology · 2025Article
- Deep learning-based whole-brain BMagnetic resonance in medicine · 2025Article
- Physics-guided self-supervised learning: Demonstration for generalized RF pulse design.Magnetic resonance in medicine · 2025Article
- TOWARDS FAST HARD-CONSTRAINED PARALLEL TRANSMIT DESIGN IN ULTRAHIGH FIELD MRI WITH PHYSICS-DRIVEN NEURAL NETWORKS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
purposeTo mitigate
methodsDeep learning parallel transmit (pTx) pulse design has received attention, but such methods have relied on supervised training and did not use CNNs for multi-channel
resultsThe proposed method outperformed the unregularized MLS solution for RMS error and coefficient-of-variation and had comparable energy consumption. Additionally, the proposed method did not show local phase singularities leading to distinct holes in the resulting magnetization unlike the unregularized MLS solution.
conclusionProposed unsupervised deep learning with CNNs performs better than unregularized MLS in static pTx for speed and robustness.
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Registered trials
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