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Abstract PurposeRecent numerical and empirical results proved that high permittivity materials (HPM) used in pads placed near the subject or directly integrated with coils can increase the SNR and reduce the specific absorption rate (SAR) in MRI. In this paper, we propose an analytical investigation of the effect on the magnetic field distribution of a layer of HPM surrounding an anatomy‐mimicking cylindrical sample. MethodsThe study is based on a reformulation of the Mie scattering for cylindrical geometry, following an approach recently introduced for spherical samples. The total field in each medium is decomposed in terms of inward and outward electromagnetic waves, and the fields are expressed as series of cylindrical harmonics, whose coefficients can be interpreted as classical reflection and transmission coefficients. ResultsOur new formulation allows a quantitative evaluation of the effect of the HPM layer for varying permittivity and thickness, and it provides an intuitive understanding of such effect in terms of propagation and scattering of the RF field. ConclusionWe show how HPM can filter out the modes that only contribute to the noise or RF power deposition, resulting in higher SNR or lower SAR, respectively. Our proposed framework provides physical insight on how to properly design HPM for MRI applications.
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Genetic algorithm search for the worst‐case MRI RF exposure for a multiconfiguration implantable fixation system modeled using artificial neural networks
PurposeThis paper presents a method to search for the worst‐case configuration leading to the highest RF exposure for a multiconfiguration implantable fixation system under MRI. MethodsA two‐step method combining an artificial neural network and a genetic algorithm is developed to achieve this purpose. In the first step, the level of RF exposure in terms of peak 1‐g and/or 10‐g averaged specific absorption rate (SAR1g/10g), related to the multiconfiguration system, is predicted using an artificial neural network. A genetic algorithm is then used to search for the worst‐case configuration of this multidimensional nonlinear problem within both the enumerated discrete sample space and generalized continuous sample space. As an example, a generic plate system with a total of 576 configurations is used for both 1.5T and 3T MRI systems. ResultsThe presented method can effectively identify the worst‐case configuration and accurately predict the SAR1g/10gwith no more than 20% of the samples in the studied discrete sample space, and can even predict the worst case in the generalized continuous sample space. The worst‐case prediction error in the generalized continuous sample space is less than 1.6% for SAR1gand less than 1.3% for SAR10gcompared with the simulation results. ConclusionThe combination of an artificial neural network with genetic algorithm is a robust technique to determine the worst‐case RF exposure level for a multiconfiguration system, and only needs a small amount of training data from the entire system.
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- Award ID(s):
- 1440107
- PAR ID:
- 10454358
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Magnetic Resonance in Medicine
- Volume:
- 84
- Issue:
- 5
- ISSN:
- 0740-3194
- Page Range / eLocation ID:
- p. 2754-2764
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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