@phdthesis{Neumann2014,
  author    = {Neumann, Daniel},
  title     = {Advances in Fast MRI Experiments},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-108165},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2014},
  abstract  = {Magnetic Resonance Imaging (MRI) is a non-invasive medical imaging technique, that is rou- tinely used in clinical practice for detection and diagnosis of a wide range of different diseases. In MRI, no ionizing radiation is used, making even repeated application unproblematic. This is an important advantage over other common imaging methods such as X-rays and Computer To- mography. One major drawback of MRI, however, are long acquisition times and associated high costs of experiments. Since the introduction of MRI, several important technical developments have been made to successfully reduce acquisition times. In this work, novel approaches were developed to increase the efficiency of MRI acquisitions. In Chapter 4, an improved radial turbo spin-echo (TSE) combined acquisition and reconstruction strategy was introduced. Cartesian turbo spin-echo sequences [3] are widely used especially for the detection and diagnosis of neurological pathologies, as they provide high SNR images with both clinically important proton density and T2 contrasts. TSE acquisitions combined with radial sampling are very efficient, since it is possible to obtain a number of ETL images with different contrasts from a single radial TSE measurement [56-58]. Conventionally, images with a particular contrast are obtained from both radial and Cartesian TSE acquisitions by combining data from different echo times into a single image. In the radial case, this can be achieved by employing k-space weighted image contrast (KWIC) reconstruction. In KWIC, the center region of k-space is filled exclusively with data belonging to the desired contrast while outer regions also are assembled with data acquired at other echo times. However, this data sharing leads to mixed contrast contributions to both Cartesian and radial TSE images. This is true especially for proton density weighted images and therefore may reduce their diagnostic value. In the proposed method, an adapted golden angle reordering scheme is introduced for radial TSE acquisitions, that allows a free choice of the echo train length and provides high flexibility in image reconstruction. Unwanted contrast contaminations are greatly reduced by employing a narrow-band KWIC filter, that restricts data sharing to a small temporal window around the de- sired echo time. This corresponds to using fewer data than required for fully sampled images and consequently leads to images exhibiting aliasing artifacts. In a second step, aliasing-free images are obtained using parallel imaging. In the neurological examples presented, the CG-SENSE algorithm [42] was chosen due to its stable convergence properties and its ability to reconstruct arbitrarily sampled data. In simulations as well as in different in vivo neurological applications, no unwanted contrast contributions could be observed in radial TSE images reconstructed with the proposed method. Since this novel approach is easy to implement on today's scanners and requires low computational power, it might be valuable for the clinical breakthrough of radial TSE acquisitions. In Chapter 5, an auto-calibrating method was introduced to correct for stimulated echo contribu- tions to T2 estimates from a mono-exponential fit of multi spin-echo (MSE) data. Quantification of T2 is a useful tool in clinical routine for the detection and diagnosis of diseases as well as for tis- sue characterization. Due to technical imperfections, refocusing flip angles in a MSE acquisition deviate from the ideal value of 180○. This gives rise to significant stimulated echo contributions to the overall signal evolution. Therefore, T2 estimates obtained from MSE acquisitions typically are notably higher than the reference. To obtain accurate T2 estimates from MSE acquisitions, MSE signal amplitudes can be predicted using the extended phase graph (EPG, [23, 24]) algo- rithm. Subsequently, a correction factor can be obtained from the simulated EPG T2 value and applied to the MSE T2 estimates. However, EPG calculations require knowledge about refocus- ing pulse amplitudes, T2 and T1 values and the temporal spacing of subsequent echoes. While the echo spacing is known and, as shown in simulations, an approximate T1 value can be assumed for high ratios of T1/T2 without compromising accuracy of the results, the remaining two parameters are estimated from the data themselves. An estimate for the refocusing flip angle can be obtained from the signal intensity ratio of the second to the first echo using EPG. A conventional mono- exponential fit of the MSE data yields a first estimate for T2. The T2 correction is then obtained iteratively by updating the T2 value used for EPG calculations in each step. For all examples pre- sented, two iterations proved to be sufficient for convergence. In the proposed method, a mean flip angle is extracted across the slice. As shown in simulations, this assumption leads to greatly reduced deviations even for more inhomogeneous slice profiles. The accuracy of corrected T2 values was shown in experiments using a phantom consisting of bottles filled with liquids with a wide range of different T2 values. While T2 MSE estimates were shown to deviate significantly from the spin-echo reference values, this is not the case for corrected T2 values. Furthermore, applicability was demonstrated for in vivo neurological experiments. In Chapter 6, a new auto-calibrating parallel imaging method called iterative GROG was pre- sented for the reconstruction of non-Cartesian data. A wide range of different non-Cartesian schemes have been proposed for data acquisition in MRI, that present various advantages over conventional Cartesian sampling such as faster acquisitions, improved dynamic imaging and in- trinsic motion correction. However, one drawback of non-Cartesian data is the more complicated reconstruction, which is ever more problematic for non-Cartesian parallel imaging techniques. Iterative GROG uses Calibrationless Parallel Imaging by Structured Low-Rank Matrix Completion (CPI) for data reconstruction. Since CPI requires points on a Cartesian grid, it cannot be used to directly reconstruct non-Cartesian data. Instead, Grappa Operator Gridding (GROG) is employed in a first step to move the non-Cartesian points to the nearest Cartesian grid locations. However, GROG requires a fully sampled center region of k-space for calibration. Combining both methods in an iterative scheme, accurate GROG weights can be obtained even from highly undersampled non-Cartesian data. Subsequently, CPI can be used to reconstruct either full k- space or a calibration area of arbitrary size, which can then be employed for data reconstruction with conventional parallel imaging methods. In Chapter 7, a new 2D sampling scheme was introduced consisting of multiple oscillating effi- cient trajectories (MOET), that is optimized for Compressed Sensing (CS) reconstructions. For successful CS reconstruction of a particular data set, some requirements have to be met. First, ev- ery data sample has to carry information about the whole object, which is automatically fulfilled for the Fourier sampling employed in MRI. Additionally, the image to be reconstructed has to be sparse in an arbitrary domain, which is true for a number of different applications. Last, data sam- pling has to be performed in an incoherent fashion. For 2D imaging, this important requirement of CS is difficult to achieve with conventional Cartesian and non-Cartesian sampling schemes. Ra- dial sampling is often used for CS reconstructions of dynamic data despite the streaking present in undersampled images. To obtain incoherent aliasing artifacts in undersampled images while at the same time preserving the advantages of radial sampling for dynamic imaging, MOET com- bines radial spokes with oscillating gradients of varying amplitude and alternating orientation orthogonal to the readout direction. The advantage of MOET over radial sampling in CS re- constructions was demonstrated in simulations and in in vivo cardiac imaging. MOET provides superior results especially when used in CS reconstructions with a sparsity constraint directly in image space. Here, accurate results could be obtained even from few MOET projections, while the coherent streaking artifacts present in the case of radial sampling prevent image recovery even for smaller acceleration factors. For CS reconstructions of dynamic data with sparsity constraint in xf-space, the advantage of MOET is smaller since the temporal reordering is responsible for an important part of incoherency. However, as was shown in simulations of a moving phantom and in the reconstruction of ungated cardiac data, the additional spatial incoherency provided by MOET still leads to improved results with higher accuracy and may allow reconstructions with higher acceleration factors.},
  subject      = {Kernspintomografie},
  language  = {en}
}
@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}
}