1997
DOI: 10.1006/jmre.1997.1200
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Compact MRI Magnet Design by Stochastic Optimization

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Cited by 51 publications
(19 citation statements)
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“…Next generation magnets came with the concept of active shielding where the stray field is less than 4 G (in regions at least 4 m away from the iso-center) either by using ferromagnetic materials [11] or using coaxial coils carrying current in the opposite direction [12]. Different methods are used to design magnets of highly homogeneous field, based on stochastic optimization [20], matrix subset selection [21], inverse approach [22], hybrid numerical methods [23] and methods for permanent magnet design [24].…”
Section: Magnet Design and Modelingmentioning
confidence: 99%
“…Next generation magnets came with the concept of active shielding where the stray field is less than 4 G (in regions at least 4 m away from the iso-center) either by using ferromagnetic materials [11] or using coaxial coils carrying current in the opposite direction [12]. Different methods are used to design magnets of highly homogeneous field, based on stochastic optimization [20], matrix subset selection [21], inverse approach [22], hybrid numerical methods [23] and methods for permanent magnet design [24].…”
Section: Magnet Design and Modelingmentioning
confidence: 99%
“…In terms of these common factors of design and other more specific application-related limitations, optimization methods used previously can be grouped into two categories: methods that search a large parameter space from which an optimal superconducting coil configuration is obtained (4-7), or ones wherein predefined constraints are used as part of the optimization strategy to speed up the calculation of a solution, while allowing for more stable convergence toward the optimal solution (8)(9)(10). In the first case, generally large computational resources are required to establish coil arrangements, whereas in the second case, a priori knowledge incorporated into the optimization strategy provides methods that converge fast, given that the seed data has been accurately formulated.…”
Section: Introductionmentioning
confidence: 99%
“…Optimization methods previously employed may be considered as one of two general approaches: either where a broad parameter space is searched for an optimal coil layout [2][3][4][5], or where initial constraints are placed on the optimization strategy to enable the calculation of a solution either more efficiently, or in a more convergent and stable manner [6][7][8]. The former tends to be associated with optimization strategies that require large computational resources, and the latter tends to achieve magnet coil layouts faster given favourable initial coil layout approximation or seed data.…”
Section: Introductionmentioning
confidence: 99%