A Divergence-Free Bem Method to Model Quasi-Static Currents: Application to Mri Coil Design

Sánchez, Clemente Cobos; Barrio‐García, Salvador del; Angulo, Luis D.; Coevorden, C. Moreno de Jong van; Bretones, and Amelia Rubio

doi:10.2528/pierb10011504

Cited by 23 publications

(15 citation statements)

References 17 publications

(27 reference statements)

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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

View full text Add to dashboard Cite

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“…The current-density-based method is usually efficient and effective for both regular and irregular geometries but can suffer from local minima in the optimization process. For regularly shaped structures, the current density can be described with Fourier expansions such as the target field (TF) method [6]- [8] and related approaches [9], [10]; for irregularly shaped structures, the coil space is divided into meshes and then the finite element method (FEM) or the boundary element method (BEM) [11]- [14] are used to approximate current density distributions over the coil space. The current density distribution in the coil space can be obtained from the control points in the TF by solving the inverse problem.…”

Section: Introductionmentioning

confidence: 99%

A Finite Difference Method for the Design of Gradient Coils in MRI—An Initial Framework

Zhu

Xia

et al. 2012

IEEE Trans. Biomed. Eng.

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This paper proposes a finite-difference (FD)-based method for the design of gradient coils in MRI. The design method first uses the FD approximation to describe the continuous current density of the coil space and then employs the stream function method to extract the coil patterns. During the numerical implementation, a linear equation is constructed and solved using a regularization scheme. The algorithm details have been exemplified through biplanar and cylindrical gradient coil design examples. The design method can be applied to unusual coil designs such as ultrashort or dedicated gradient coils. The proposed gradient coil design scheme can be integrated into a FD-based electromagnetic framework, which can then provide a unified computational framework for gradient and RF design and patient-field interactions.

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“…Based on the Biot‐Savart law, the relationship between the current‐carrying discrete points and desired magnetic field within the ROI can be constructed, which can be used to obtain the best possible distribution of current density using an inverse method. Commonly used distributed winding methods include target field method (TF), the boundary element method (BEM), the finite difference method (FDM), and finite element method (FEM) . Among these numerical methods, the BEM is flexible to design coil with arbitrary geometry, which only needs to divide the domain boundary surface, and use the discrete form of control functions to satisfy the boundary conditions.…”

Section: Introductionmentioning

confidence: 99%

Optimization magnetic resonance imaging shim coil using second derivative discretized stream function

Wang

Zhu

et al. 2017

Concepts Magn Reson

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In Magnetic Resonance Imaging (MRI) equipment, a set of shim coils are designed to generate specific magnetic fields, thus eliminating harmonic components of magnetic field to obtain a high level homogeneous magnetic field within the region of interesting (ROI). In the electromagnetic design process, in order to produce the desired magnetic field, the deviation between the calculated magnetic field of shim coil and the theoretical magnetic field is treated as a kind of traditional objective functions to optimize the distribution of current density on the surface of shim coil skeleton. However, such function is ill-posed because of the overdetermined or underdetermined system of equations. The regularization method is commonly used to solve such problem by constructing the regularization term. This article proposes a new iterative optimization method for the design of shim coils in MRI. Based on the boundary element method (BEM), the discretized stream functions can be obtained by discretizing the surface of coil skeleton using a set of triangular elements. As the regularization term, the second derivative stream function is included in the minimization of the deviation between calculated magnetic fields and target magnetic fields. The distribution of coil which meets the design requirements can be obtained by using the BroydenFletcher-Goldfarb-Shanno (BFGS) algorithm. At last, the cubic spline interpolation is used to make lines as smooth as possible to be processed. In this article, the proposed method was employed to design two kinds of room temperature shim coils for cylindrical and/or biplanar MRI shim coil system. The simulation results demonstrate that the proposed method is effective and practical.

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A Divergence-Free Bem Method to Model Quasi-Static Currents: Application to Mri Coil Design

Cited by 23 publications

References 17 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

A Finite Difference Method for the Design of Gradient Coils in MRI—An Initial Framework

Optimization magnetic resonance imaging shim coil using second derivative discretized stream function

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