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1000
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Abstract/Summary
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<jats:p>Low-field (B<jats:sub>0</jats:sub> &lt; 0.2 T) magnetic resonance imaging (MRI) is emerging as a low cost, point-of-care alternative to provide access to diagnostic imaging technology even in resource scarce environments. MRI magnets can be constructed based on permanent neodymium-iron-boron (NdFeB) magnets in discretized arrangements, leading to substantially lower mass and costs. A challenge with these designs is, however, a good <jats:italic>B</jats:italic><jats:sub><jats:italic>0</jats:italic></jats:sub> field homogeneity, which is needed to produce high quality images free of distortions. In this work, we describe an iterative approach to build a low-field MR magnet based on a <jats:italic>B</jats:italic><jats:sub><jats:italic>0</jats:italic></jats:sub>-shimming methodology using genetic algorithms. The methodology is tested by constructing a small bore (inner bore diameter = 130 mm) desktop MR magnet (&lt;15 kg) at a field strength of <jats:italic>B</jats:italic><jats:sub><jats:italic>0</jats:italic></jats:sub> = 0.1 T and a target volume of 4 cm in diameter. The configuration consists of a base magnet and shim inserts, which can be placed iteratively without modifying the base magnet assembly and without changing the inner dimensions of the bore or the outer dimensions of the MR magnet. Applying the shims, <jats:italic>B</jats:italic><jats:sub><jats:italic>0</jats:italic></jats:sub> field inhomogeneity could be reduced by a factor 8 from 5,448 to 682 ppm in the target central slice of the magnet. Further improvements of these results can be achieved in a second or third iteration, using more sensitive magnetic field probes (e.g., nuclear magnetic resonance based magnetic field measurements). The presented methodology is scalable to bigger magnet designs. The MR magnet can be reproduced with off-the-shelf components and a 3D printer and no special tools are needed for construction. All design files and code to reproduce the results will be made available as open source hardware.</jats:p>
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1000
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Hinweis
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DeepGreen-ID: 3890dd50b8494a83824a2066fee25e43 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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