@phdthesis{Seiberlich2008,
  author    = {Seiberlich, Nicole},
  title     = {Advances in Non-Cartesian Parallel Magnetic Resonance Imaging using the GRAPPA Operator},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-28321},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2008},
  abstract  = {Magnetic Resonance Imaging (MRI) is an imaging modality which provides anatomical or functional images of the human body with variable contrasts in an arbitrarily positioned slice without the need for ionizing radiation. In MRI, data are not acquired directly, but in the reciprocal image space (otherwise known as k-space) through the application of spatially variable magnetic field gradients. The k-space is made up of a grid of data points which are generally acquired in a line-by-line fashion (Cartesian imaging). After the acquisition, the k-space data are transformed into the image domain using the Fast Fourier Transformation (FFT). However, the acquisition of data is not limited to the rectilinear Cartesian sampling scheme described above. Non-Cartesian acquisitions, where the data are collected along exotic trajectories, such as radial and spiral, have been shown to be beneficial in a number of applications. However, despite their additional properties and potential advantages, working with non-Cartesian data can be complicated. The primary difficulty is that non-Cartesian trajectories are made up of points which do not fall on a Cartesian grid, and a simple and fast FFT algorithm cannot be employed to reconstruct images from non-Cartesian data. In order to create an image, the non-Cartesian data are generally resampled on a Cartesian grid, an operation known as gridding, before the FFT is performed. Another challenge for non-Cartesian imaging is the combination of unusual trajectories with parallel imaging. This thesis has presented several new non-Cartesian parallel imaging methods which simplify both gridding and the reconstruction of images from undersampled data. In Chapter 4, a novel approach which uses the concepts of parallel imaging to grid data sampled along a non-Cartesian trajectory called GRAPPA Operator Gridding (GROG) is described. GROG shifts any acquired k-space data point to its nearest Cartesian location, thereby converting non-Cartesian to Cartesian data. The only requirements for GROG are a multi-channel acquisition and a calibration dataset for the determination of the GROG weights. Chapter 5 discusses an extension of GRAPPA Operator Gridding, namely Self-Calibrating GRAPPA Operator Gridding (SC-GROG). SC-GROG is a method by which non-Cartesian data can be gridded using spatial information from a multi-channel coil array without the need for an additional calibration dataset, as required in standard GROG. Although GROG can be used to grid undersampled datasets, it is important to note that this method uses parallel imaging only for gridding, and not to reconstruct artifact-free images from undersampled data. Chapter 6 introduces a simple, novel method for performing modified Cartesian GRAPPA reconstructions on undersampled non-Cartesian k-space data gridded using GROG to arrive at a non-aliased image. Because the undersampled non-Cartesian data cannot be reconstructed using a single GRAPPA kernel, several Cartesian patterns are selected for the reconstruction. Finally, Chapter 7 discusses a novel method of using GROG to mimic the bunched phase encoding acquisition (BPE) scheme. In MRI, it is generally assumed that an artifact-free image can be reconstructed only from sampled points which fulfill the Nyquist criterion. However, the BPE reconstruction is based on the Generalized Sampling Theorem of Papoulis, which states that a continuous signal can be reconstructed from sampled points as long as the points are on average sampled at the Nyquist frequency. A novel method of generating the "bunched" data using GRAPPA Operator Gridding (GROG), which shifts datapoints by small distances in k-space using the GRAPPA Operator instead of employing zig-zag shaped gradients, is presented in this chapter. With the conjugate gradient reconstruction method, these additional "bunched" points can then be used to reconstruct an artifact-free image from undersampled data. This method is referred to as GROG-facilitated Bunched Phase Encoding, or GROG-BPE.},
  subject      = {NMR-Tomographie},
  language  = {en}
}
@phdthesis{Ehses2011,
  author    = {Ehses, Philipp},
  title     = {Development of new Acquisition Strategies for fast Parameter Quantification in Magnetic Resonance Imaging},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-72531},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2011},
  abstract  = {Magnetic resonance imaging (MRI) is a medical imaging method that involves no ionizing radiation and can be used non-invasively. Another important - if not the most important - reason for the widespread and increasing use of MRI in clinical practice is its interesting and highly flexible image contrast, especially of biological tissue. The main disadvantages of MRI, compared to other widespread imaging modalities like computed tomography (CT), are long measurement times and the directly resulting high costs. In the first part of this work, a new technique for accelerated MRI parameter mapping using a radial IR TrueFISP sequence is presented. IR TrueFISP is a very fast method for the simultaneous quantification of proton density, the longitudinal relaxation time T1, and the transverse relaxation time T2. Chapter 2 presents speed improvements to the original IR TrueFISP method. Using a radial view-sharing technique, it was possible to obtain a full set of relaxometry data in under 6 s per slice. Furthermore, chapter 3 presents the investigation and correction of two major sources of error of the IR TrueFISP method, namely magnetization transfer and imperfect slice profiles. In the second part of this work, a new MRI thermometry method is presented that can be used in MRI-safety investigations of medical implants, e.g. cardiac pacemakers and implantable cardioverter-defibrillators (ICDs). One of the major safety risks associated with MRI examinations of pacemaker and ICD patients is RF induced heating of the pacing electrodes. The design of MRI-safe (or MRI-conditional) pacing electrodes requires elaborate testing. In a first step, many different electrode shapes, electrode positions and sequence parameters are tested in a gel phantom with its geometry and conductivity matched to a human body. The resulting temperature increase is typically observed using temperature probes that are placed at various positions in the gel phantom. An alternative to this local thermometry approach is to use MRI for the temperature measurement. Chapter 5 describes a new approach for MRI thermometry that allows MRI thermometry during RF heating caused by the MRI sequence itself. Specifically, a proton resonance frequency (PRF) shift MRI thermometry method was combined with an MR heating sequence. The method was validated in a gel phantom, with a copper wire serving as a simple model for a medical implant.},
  subject      = {Kernspintomografie},
  language  = {en}
}