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You searched for subject:(Magnetic fieldmap). Showing records 1 – 2 of 2 total matches.

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Brunel University

1. Giboudot, Yoel. Study of beam dynamics in NS-FFAG EMMA with dynamical map.

Degree: PhD, 2011, Brunel University

Dynamical maps for magnetic components are fundamental to studies of beam dynamics in accelerators. However, it is usually not possible to write down maps in closed form for anything other than simplified models of standard accelerator magnets. In the work presented here, the magnetic field is expressed in analytical form obtained from fitting Fourier series to a 3D numerical solution of Maxwell’s equations. Dynamical maps are computed for a particle moving through this field by applying a second order (with the paraxial approximation) explicit symplectic integrator. These techniques are used to study the beam dynamics in the first non-scaling FFAG ever built, EMMA, especially challenging regarding the validity of the paraxial approximation for the large excursion of particle trajectories. The EMMA lattice has four degrees of freedom (strength and transverse position of each of the two quadrupoles in each periodic cell). Dynamical maps, computed for a set of lattice configurations, may be efficiently used to predict the dynamics in any lattice configuration. We interpolate the coefficients of the generating function for the given configuration, ensuring the symplecticity of the solution. An optimisation routine uses this tool to look for a lattice defined by four constraints on the time of flight at different beam energies. This provides a way to determine the tuning of the lattice required to produce a desired variation of time of flight with energy, which is one of the key characteristics for beam acceleration in EMMA. These tools are then benchmarked against data from the recent EMMA commissioning.

Subjects/Keywords: 531.16; Magnetic fieldmap; Symplectic integrator; Non linear dynamics; Paraxial approximation; Generating function

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APA (6th Edition):

Giboudot, Y. (2011). Study of beam dynamics in NS-FFAG EMMA with dynamical map. (Doctoral Dissertation). Brunel University. Retrieved from http://bura.brunel.ac.uk/handle/2438/5947 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540753

Chicago Manual of Style (16th Edition):

Giboudot, Yoel. “Study of beam dynamics in NS-FFAG EMMA with dynamical map.” 2011. Doctoral Dissertation, Brunel University. Accessed December 04, 2020. http://bura.brunel.ac.uk/handle/2438/5947 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540753.

MLA Handbook (7th Edition):

Giboudot, Yoel. “Study of beam dynamics in NS-FFAG EMMA with dynamical map.” 2011. Web. 04 Dec 2020.

Vancouver:

Giboudot Y. Study of beam dynamics in NS-FFAG EMMA with dynamical map. [Internet] [Doctoral dissertation]. Brunel University; 2011. [cited 2020 Dec 04]. Available from: http://bura.brunel.ac.uk/handle/2438/5947 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540753.

Council of Science Editors:

Giboudot Y. Study of beam dynamics in NS-FFAG EMMA with dynamical map. [Doctoral Dissertation]. Brunel University; 2011. Available from: http://bura.brunel.ac.uk/handle/2438/5947 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540753

2. Matakos, Antonios. Dynamic Image and Fieldmap Joint Estimation Methods for MRI Using Single-Shot Trajectories.

Degree: PhD, Electrical Engineering-Systems, 2013, University of Michigan

In susceptibility-weighted MRI, ignoring the magnetic field inhomogeneity can lead to severe reconstruction artifacts. Correcting for the effects of magnetic field inhomogeneity requires accurate fieldmaps. Especially in functional MRI, dynamic updates are desirable, since the fieldmap may change in time. Also, susceptibility effects that induce field inhomogeneity often have non-zero through-plane gradients, which, if uncorrected, can cause signal loss in the reconstructed images. Most image reconstruction methods that compensate for field inhomogeneity, even using dynamic fieldmap updates, ignore through-plane fieldmap gradients. Furthermore, standard optimization methods, like CG-based algorithms, may be slow to converge and recently proposed algorithms based on the Augmented Lagrangian (AL) framework have shown the potential to lead to more efficient optimization algorithms, especially in MRI reconstruction problems with non-quadratic regularization. In this work, we propose a computationally efficient, model-based iterative method for joint reconstruction of dynamic images and fieldmaps in single coil and parallel MRI, using single-shot trajectories. We first exploit the fieldmap smoothness to perform joint estimation using less than two full data sets and then we exploit the sensitivity encoding from parallel imaging to reduce the acquisition length and perform joint reconstruction using just one full k-space dataset. Subsequently, we extend the proposed method to account for the through-plane gradients of the field inhomogeneity. To improve the efficiency of the reconstruction algorithm we use a linearization technique for fieldmap estimation, which allows the use of the conjugate gradient algorithm. The resulting method allows for efficient reconstruction by applying fast approximations that allow the use of the conjugate gradient algorithm along with FFTs. Our proposed method can be computationally efficient for quadratic regularizers, but the CG-based algorithm is not directly applicable to non-quadratic regularization. To improve the efficiency of our method for non-quadratic regularization we propose an algorithm based on the augmented Lagrangian (AL) framework with variable splitting. This new algorithm can also be used for the non-linear optimization problem of fieldmap estimation without the need for the linearization approximation. Advisors/Committee Members: Fessler, Jeffrey A. (committee member), Noll, Douglas C. (committee member), Nielsen, Jon-Fredrik (committee member), Nadakuditi, Rajesh Rao (committee member).

Subjects/Keywords: Magnetic Resonance Imaging (MRI); Echo-Planar Imaging (EPI); EPI Ghost Correction; Joint Estimation; Through-plane Fieldmap Gradients; Augmented Lagrangian (AL); Electrical Engineering; Engineering

…image reconstruction. (b) Image reconstruction corrected with initial fieldmap… …fieldmap of Figure 4.4c. (d) Joint image reconstruction. (e) Joint fieldmap… …True image, fieldmap, and gradient map for 4 out of 20 slices. Slices 3, 8, 13 and 18 are… …fieldmap, and gradient map for 4 out of 20 slices. Slices 3, 8, 13 and 18 are shown from left to… …right. . . . . . . . . . . . . . . . . . . . . . . . . Image and fieldmap reconstructions for… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Matakos, A. (2013). Dynamic Image and Fieldmap Joint Estimation Methods for MRI Using Single-Shot Trajectories. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/102449

Chicago Manual of Style (16th Edition):

Matakos, Antonios. “Dynamic Image and Fieldmap Joint Estimation Methods for MRI Using Single-Shot Trajectories.” 2013. Doctoral Dissertation, University of Michigan. Accessed December 04, 2020. http://hdl.handle.net/2027.42/102449.

MLA Handbook (7th Edition):

Matakos, Antonios. “Dynamic Image and Fieldmap Joint Estimation Methods for MRI Using Single-Shot Trajectories.” 2013. Web. 04 Dec 2020.

Vancouver:

Matakos A. Dynamic Image and Fieldmap Joint Estimation Methods for MRI Using Single-Shot Trajectories. [Internet] [Doctoral dissertation]. University of Michigan; 2013. [cited 2020 Dec 04]. Available from: http://hdl.handle.net/2027.42/102449.

Council of Science Editors:

Matakos A. Dynamic Image and Fieldmap Joint Estimation Methods for MRI Using Single-Shot Trajectories. [Doctoral Dissertation]. University of Michigan; 2013. Available from: http://hdl.handle.net/2027.42/102449

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