Magnetic Field in Mri Yokeless Devices: Analytical Approach

Ravaud, Romain; Lemarquand, Guy

doi:10.2528/pier09061205

Cited by 15 publications

(15 citation statements)

References 25 publications

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“…It provides greater contrast between the different soft tissues of the body than computed tomography (CT) does, making it especially useful in neurological (brain), musculoskeletal, cardiovascular, and oncological (cancer) imaging [2,3]. The diagnostic values of MRI are greatly magnified by the automated and accurate classification of the MR images [4,5].…”

Section: Introductionmentioning

confidence: 99%

Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm

Zhang¹,

Wu²,

Wang³

2011

PIER

168

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Abstract-Automated and accurate classification of magnetic resonance (MR) brain images is a hot topic in the field of neuroimaging. Recently many different and innovative methods have been proposed to improve upon this technology. In this study, we presented a hybrid method based on forward neural network (FNN) to classify an MR brain image as normal or abnormal. The method first employed a discrete wavelet transform to extract features from images, and then applied the technique of principle component analysis (PCA) to reduce the size of the features. The reduced features were sent to an FNN, of which the parameters were optimized via an improved artificial bee colony (ABC) algorithm based on both fitness scaling and chaotic theory. We referred to the improved algorithm as scaled chaotic artificial bee colony (SCABC). Moreover, the K-fold stratified cross validation was employed to avoid overfitting. In the experiment, we applied the proposed method on the data set of T2-weighted MRI images consisting of 66 brain images (18 normal and 48 abnormal). The proposed SCABC was compared with traditional training methods such as BP, momentum BP, genetic algorithm, elite genetic algorithm with migration, simulated annealing, and ABC. Each algorithm was run 20 times to reduce randomness. The results show that our SCABC can obtain the least mean MSE and 100% classification accuracy.

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Section: Introductionmentioning

confidence: 99%

Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm

Zhang¹,

Wu²,

Wang³

2011

PIER

168

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“…Information such as magnetic field distribution, interaction between the field and materials can be easily obtained [16,17]. For interparticle magnetic forces, the magnetic dipole-dipole attractive force has been well understood and measured under different conditions both analytically and experimentally by means of ferromagnetic materials [18,19].…”

Section: Introductionmentioning

confidence: 99%

“…5). The viscosity of the melt after imposing a magnetic field can be obtained according to the Stokes' law and Equations (11) to (16). Therefore, in the following calculations, we adopted Equation (17) to evaluate the magnetic field effect on the melt viscosity (effective viscosity).…”

Section: Effect Of the Magnetic Field On The Melt Viscositymentioning

confidence: 99%

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Magnetic Interaction Between Two Non-Magnetic Particles Migrating in a Conductive Fluid Induced by a Strong Magnetic Field-an Analytical Approach

Sun¹,

Guo²,

Verhaeghe³

et al. 2010

PIER

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Abstract-An analytical approach is developed in the present paper to investigate the interaction between two non-magnetic particles migrating in a conductive fluid due to an imposed strong magnetic field (e.g., 10 Tesla). The interaction between the conductive fluid and a single particle migrating along the magnetic lines is influenced by the magnetic field and can be represented by an additional fluid viscosity. Thus the effective fluid viscosity is discussed and the magnetic field effect on the particle migrating velocity is examined. For two particles, two kinds of magnetic forces are induced: namely, the attractive force due to the magnetisation and the repulsive force caused by the conductive fluid flow around the non-magnetic particles. The forces are then evaluated with the consideration of the magnetic field effect on the particle migration and become significant with the increase of the magnetic flux density. The counteracting behavior with a critical particle size of the interparticle magnetic forces is discussed and compared with different magnetic field densities and gradient values.

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“…QCHMF can provide high accuracy quasi-continuous field at a relatively low cost for many experiments traditionally carried out in continuous magnetic fields [1][2][3]. However, due to the long pulse time and the high current, both the maximum power and consumed energy of the QCHMF magnet are usually very high which are easy to exceed the power and energy limits of the power supply.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Multi-Objective Design Optimization of Quasi-Continuous High Magnetic Field Systems

Li¹,

Ding²

2013

PIER

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Abstract-This paper proposes a coupling model of the QuasiContinuous High Magnetic Field (QCHMF) systems that incorporates the electrical, thermal and mechanical dynamics of the magnet system and the power supply system. The design of QCHMF systems is formulated as a five-objective optimization problem and a scoring system based on preference of the designer is adopted to classify the Pareto points of the optimization problem. An optimized mono-coil 50 T/100 ms QCHMF system is designed with a 67.5 MW rectifier of the Wuhan National High Magnetic Field Center (WHMFC), which is taken as an example to verify the proposed model and optimization method. Detailed simulation models of the optimized QCHMF system are built in Matlab and Comsol and the results agree well with the designed technical specifications. The proposed model and optimization method are generic which can be applied to other QCHMF systems with minor modifications.

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Magnetic Field in Mri Yokeless Devices: Analytical Approach

Cited by 15 publications

References 25 publications

Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm

Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm

Magnetic Interaction Between Two Non-Magnetic Particles Migrating in a Conductive Fluid Induced by a Strong Magnetic Field-an Analytical Approach

Modeling and Multi-Objective Design Optimization of Quasi-Continuous High Magnetic Field Systems

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