This dissertation describes how to design dielectric pads that can be used to increase image quality in Magnetic Resonance Imaging, and how to accelerate image reconstruction times using a preconditioner.
Image quality is limited by the signal to noise ratio of a scan. This rati
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This dissertation describes how to design dielectric pads that can be used to increase image quality in Magnetic Resonance Imaging, and how to accelerate image reconstruction times using a preconditioner.
Image quality is limited by the signal to noise ratio of a scan. This ratio is increased for higher static magnetic field strengths and therefore there is great interest in high-field MRI. The wavelength of the transmitted magnetic RF field decreases for higher field strengths, and it becomes comparable to the dimensions of the human body. Consequently, RF interference patterns are encountered which can severely degrade image quality because of a low transmit efficiency or because of inhomogeneities in the field distribution. Dielectric pads can be used to improve this distribution as the pads tailor the field by inducing a secondary magnetic field due to its high permittivity. Typically, the pads are placed tangential to the body and in the vicinity of the region of interest. The exact location, dimensions, and constitution of the pad need to be carefully determined, however, and depend on the application and the MR configuration. Normally, parametric design studies are carried out using electromagnetic field solvers to find a suitable pad, but this is a very time consuming process which can last hours to days. In contrast with these design studies, we present methods to efficiently model and design the dielectric pads using reduced order modeling and optimization techniques. Subsequently, we have created a design tool to bridge the gap between the advanced design methods and the practical application by the MR community. Now, pads can be designed for any 7T neuroimaging and 3T body imaging application within minutes.
In the second part of the thesis a preconditioner is designed for parallel imaging (PI) and compressed sensing (CS) reconstructions. MRI acquisition times can be strongly reduced by using PI and CS techniques by acquiring less data than prescribed by the Nyquist criterion to fully reconstruct the anatomic image; this is beneficial for patient's comfort and for minimizing the risk of patient's movement. Although acquisition times are reduced, the reconstruction times are increased significantly. The reconstruction times can be reduced when a preconditioner is used. In this thesis, we construct such a preconditioner for the frequently used iterative Split Bregman framework. We have tested the performance in a conjugate gradient framework, and show that for different coil configurations, undersampling patterns, and anatomies, a five-fold acceleration can be obtained for solving the linear system part of Split Bregman.@en