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The goal of the work presented in this thesis was to explore the possibilities and limitations of MRI / MRS using an ultra high field of 17.6 tesla. A broad range of specific applications and MR methods, from MRI to MRSI and MRS were investigated. The main foci were on sodium magnetic resonance spectroscopic imaging of rodents, magnetic resonance spectroscopy of the mouse brain, and the detection of small amounts of iron labeled stem cells in the rat brain using MRI Sodium spectroscopic imaging was explored since it benefits tremendously from the high magnetic field. Due to the intrinsically low signal in vivo, originating from the low concentrations and short transverse relaxation times, only limited results have been achieved by other researchers until now. Results in the literature include studies conducted on large animals such as dogs to animals as small as rats. No studies performed on mice have been reported, despite the fact that the mouse is the most important laboratory animal due to the ready availability of transgenic strains. Hence, this study concentrated on sodium MRSI of small rodents, mostly mice (brain, heart, and kidney), and in the case of the brain on young rats. The second part of this work concentrated on proton magnetic resonance spectroscopy of the rodent brain. Due to the high magnetic field strength not only the increasing signal but also the extended spectral resolution was advantageous for such kind of studies. The difficulties/limitations of ultra high field MRS were also investigated. In the last part of the presented work detection limits of iron labeled stem cells in vivo using magnetic resonance imaging were explored. The studies provided very useful benchmarks for future researchers in terms of the number of labeled stem cells that are required for high-field MRI studies. Overall this work has shown many of the benefits and the areas that need special attention of ultra high fields in MR. Three topics in MRI, MRS and MRSI were presented in detail. Although there are significant additional difficulties that have to be overcome compared to lower frequencies, none of the work presented here would have been possible at lower field strengths.
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.
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Besides image contrast, imaging speed is probably the most important consideration in clinical magnetic resonance imaging (MRI). MR scanners currently operate at the limits of potential imaging speed, due to technical and physiological problems associated with rapidly switched gradient systems. Parallel imaging (parallel MRI or pMRI) is a method which allows one to significantly shorten the acquisition time of MR images without changing the contrast behavior of the underlying MR sequence. The accelerated image acquisition in pMRI is accomplished without relying on more powerful technical equipment or exceeding physiological boundaries. Because of these properties, pMRI is currently employed in many clinical routines, and the number of applications where pMRI can be used to accelerate imaging is increasing. However, there is also growing criticism of parallel imaging in certain applications. The primary reason for this is the intrinsic loss in the SNR due to the accelerated acquisition. In addition, other effects can also lead to a reduced image quality. Due to unavoidable inaccuracies in the pMRI reconstruction process, local and global errors may appear in the final reconstructed image. The local errors are visible as noise enhancement, while the global errors result in the so-called fold-over artifacts. The appearance and strength of these negative effects, and thus the image quality, depend upon different factors, such as the parallel imaging method chosen, specific parameters in the method, the sequence chosen, as well as specific sequence parameters. In general, it is not possible to optimize all of these parameters simultaneously for all applications. The application of parallel imaging in can lead to very pronounced image artifacts, i.e. parallel imaging can amplify errors. On the other hand, there are applications such as abdominal MR or MR angiography, in which parallel imaging does not reconstruct images robustly. Thus, the application of parallel imaging leads to errors. In general, the original euphoria surrounding parallel imaging in the clinic has been dampened by these problems. The reliability of the pMRI methods currently implemented is the main criticism. Furthermore, it has not been possible to significantly increase the maximum achievable acceleration with parallel imaging despite major technical advances. An acceleration factor of two is still standard in clinical routine, although the number of independent receiver channels available on most MR systems (which are a basic requirement for the application of pMRI) has increased by a factor of 3-6 in recent years. In this work, a novel and elegant method to address this problem has been demonstrated. The idea behind the work is to combine two methods in a synergistic way, namely non-Cartesian acquisition schemes and parallel imaging. The so-called non-Cartesian acquisition schemes have several advantages over standard Cartesian acquisitions, in that they are often faster and less sensitive to physiological noise. In addition, such acquisition schemes are very robust against fold-over artifacts even in the case of vast undersampling of k-space. Despite the advantages described above, non-Cartesian acquisition schemes are not commonly employed in clinical routines. A reason for that is the complicated reconstruction techniques which are required to convert the non-Cartesian data to a Cartesian grid before the fast Fourier transformation can be employed to arrive at the final MR image. Another reason is that Cartesian acquisitions are routinely accelerated with parallel imaging, which is not applicable for non-Cartesian MR acquisitions due to the long reconstruction times. This negates the speed advantage of non-Cartesian acquisition methods. Through the development of the methods presented in this thesis, reconstruction times for accelerated non-Cartesian acquisitions using parallel imaging now approach those of Cartesian images. In this work, the reliability of such methods has been demonstrated. In addition, it has been shown that higher acceleration factors can be achieved with such techniques than possible with Cartesian imaging. These properties of the techniques presented here lead the way for an implementation of such methods on MR scanners, and thus also offer the possibility for their use in clinical routine. This will lead to shorter examination times for patients as well as more reliable diagnoses.