High-Resolution Solid-State NMR of Cultural Inorganic Materials

Capitani, Donatella; Ciccioli, Piero; Tullio, Valeria Di; Proietti, Noemi

doi:10.1007/978-3-319-28275-6_28-2

Cited by 1 publication

(1 citation statement)

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“…Surface magnets are mostly designed by manually (brute force technique) tuning the position and orientation of a small number of cubic or cuboid permanent magnets [28][29][30][31]. Under certain assumptions, such as the desired region of interest (ROI) is far from the magnet surfaces and the magnetization fills a single region with smooth curved boundaries, the inverse of the magnet design problem could be made linear.…”

Section: Magnet Array Designmentioning

confidence: 99%

Deep neural-network based optimization for the design of a multi-element surface magnet for MRI applications

2022

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We present a combination of a CNN-based encoder with an analytical forward map for solving inverse problems. We call it an encoder-analytic (EA) hybrid model. It does not require a dedicated training dataset and can train itself from the connected forward map in a direct learning fashion. A separate regularization term is not required either, since the forward map also acts as a regularizer. As it is not a generalization model it does not suffer from overfitting. We further show that the model can be customized to either finding a specific target solution or one that follows a given heuristic. As an example, we apply this approach to the design of a multi-element surface magnet for low-field magnetic resonance imaging (MRI). We further show that the EA model can outperform the benchmark genetic algorithm model currently used for magnet design in MRI, obtaining almost 10 times better results.

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Section: Magnet Array Designmentioning

confidence: 99%