Dokument-ID Dokumenttyp Verfasser/Autoren Herausgeber Haupttitel Abstract Auflage Verlagsort Verlag Erscheinungsjahr Seitenzahl Schriftenreihe Titel Schriftenreihe Bandzahl ISBN Quelle der Hochschulschrift Konferenzname Quelle:Titel Quelle:Jahrgang Quelle:Heftnummer Quelle:Erste Seite Quelle:Letzte Seite URN DOI Abteilungen
OPUS4-16287 Dissertation Slawig, Anne Reconstruction methods for the frequency-modulated balanced steady-state free precession MRI-sequence This work considered the frequency-modulated balanced steady-state free precession (fm-bSSFP) sequence as a tool to provide banding free bSSFP MR images. The sequence was implemented and successfully applied to suppress bandings in various in vitro and in vivo examples. In combination with a radial trajectory it is a promising alternative for standard bSSFP applications. First, two specialized applications were shown to establish the benefits of the acquisition strategy in itself. In real time cardiac imaging, it was shown that the continuous shift in frequency causes a movement of the bandings across the FOV. Thus, no anatomical region is constantly impaired, and a suitable timeframe can be found to examine all important structures. Furthermore, a combination of images with different artifact positions, similar to phase-cycled acquisitions is possible. In this way, fast, banding-free imaging of the moving heart was realized. Second, acquisitions with long TR were shown. While standard bSSFP suffers from increasing incidence of bandings with higher TR values, the frequency-modulated approach provided banding free images, regardless of the TR. A huge disadvantage of fm-bSSFP, in combination with the radial trajectory, is the decrease in signal intensity. In this work a specialized reconstruction method, the multifrequency reconstruction for frequency-modulated bSSFP (Muffm), was established, which successfully compensated that phenomena. The application of Muffm to several anatomical sites, such as inner ear, legs and cardiac acquisitions, proofed the advantageous SNR of the reconstruction. Furthermore, fm-bSSFP was applied to the clinically highly relevant task of water-fat separation. Former approaches of a phase-sensitive separation procedure in combination with standard bSSFP showed promising results but failed in cases of high inhomogeneity or high field strengths where banding artifacts become a major issue. The novel approach of using the fm-bSSFP acquisition strategy with the separation approach provided robust, reliable images of high quality. Again, losses in signal intensity could be regained by Muffm, as both approaches are completely compatible. Opposed to conventional banding suppression techniques, like frequency-scouts or phase-cycling, all reconstruction methods established in this work rely on a single radial acquisition, with scan times similar to standard bSSFP scans. No prolonged measurement times occur and patient time in the scanner is kept as short as possible, improving patient comfort, susceptibility to motion or physiological noise and cost of one scan. All in all, the frequency-modulated acquisition in combination with specializes reconstruction methods, leads to a completely new quality of images with short acquisition times. 2018 urn:nbn:de:bvb:20-opus-162871 Graduate School of Life Sciences
OPUS4-6514 Dissertation Basse-Lüsebrink, Thomas Christian Application of 19F MRI for in vivo detection of biological processes This thesis focuses on various aspects and techniques of 19F magnetic resonance (MR). The first chapters provide an overview of the basic physical properties, 19F MR and MR sequences related to this work. Chapter 5 focuses on the application of 19F MR to visualize biological processes in vivo using two different animal models. The dissimilar models underlined the wide applicability of 19F MR in preclinical research. A subsection of Chapter 6 shows the application of compressed sensing (CS) to 19F turbo-spin-echo chemical shift imaging (TSE-CSI), which leads to reduced measurement time. CS, however, can only be successfully applied when a sufficient signal-to-noise ratio (SNR) is available. When the SNR is low, so-called spike artifacts occur with the CS algorithm used in the present work. However, it was shown in an additional subsection that these artifacts can be reduced using a CS-based post processing algorithm. Thus, CS might help overcome limitations with time consuming 19F CSI experiments. Chapter 7 deals with a novel technique to quantify the B+1 profile of an MR coil. It was shown that, using a specific application scheme of off resonant pulses, Bloch-Siegert (BS)-based B+1 mapping can be enabled using a Carr Purcell Meiboom Gill (CPMG)-based TSE sequence. A fast acquisition of the data necessary for B+1 mapping was thus enabled. In the future, the application of BS-CPMG-TSE B+1 mapping to improve quantification using 19F MR could therefore be possible. 2012 urn:nbn:de:bvb:20-opus-77188 Physikalisches Institut
OPUS4-6121 Dissertation Fischer, André On the Application of Compressed Sensing to Magnetic Resonance Imaging This thesis investigated the potential of Compressed Sensing (CS) applied to Magnetic Resonance Imaging (MRI). CS is a novel image reconstruction method that emerged from the field of information theory. The framework of CS was first published in technical reports in 2004 by Candès and Donoho. Two years later, the theory of CS was published in a conference abstract and two papers. Candès and Donoho proved that it is possible, with overwhelming probability, to reconstruct a noise-free sparse signal from incomplete frequency samples (e.g., Fourier coefficients). Hereby, it is assumed a priori that the desired signal for reconstruction is sparse. A signal is considered "sparse" when the number of non-zero elements is significantly smaller than the number of all elements. Sparsity is the most important foundation of CS. When an ideal noise-free signal with few non-zero elements is given, it should be understandably possible to obtain the relevant information from fewer Fourier coefficients than dictated by the Nyquist-Shannon criterion. The theory of CS is based on noise-free sparse signals. As soon as noise is introduced, no exact sparsity can be specified since all elements have signal intensities that are non-zero. However, with the addition of little or moderate noise, an approximate sparsity that can be exploited using the CS framework will still be given. The ability to reconstruct noisy undersampled sparse MRI data using CS has been extensively demonstrated. Although most MR datasets are not sparse in image space, they can be efficiently sparsified by a sparsifying transform. In this thesis, the data are either sparse in the image domain, after Discrete Gradient transformation, or after subtraction of a temporally averaged dataset from the data to be reconstructed (dynamic imaging). The aim of this thesis was to identify possible applications of CS to MRI. Two different algorithms were considered for reconstructing the undersampled sparse data with the CS concept. The Nonlinear Conjugate Gradient based technique with a relaxed data consistency constraint as suggested by Lustig et al. is termed Relaxed DC method. An alternative represents the Gradient or Steepest Descent algorithm with strict data consistency and is, therefore, termed the Strict DC method. Chapter 3 presents simulations illustrating which of these two reconstruction algorithms is best suited to recover undersampled sparse MR datasets. The results lead to the decision for the Strict DC method as reconstruction technique in this thesis. After these simulations, different applications and extensions of CS are demonstrated. Chapter 4 shows how CS benefits spectroscopic 19F imaging at 7 T, allowing a significant reduction of measurement times during in vivo experiments. Furthermore, it allows highly resolved spectroscopic 3D imaging in acceptable measurement times for in vivo applications. Chapter 5 introduces an extension of the Strict DC method called CS-CC (CS on Combined Coils), which allows efficient processing of sparse undersampled multi-coil data. It takes advantage of a concept named "Joint Sparsity", which exploits the fact that all channels of a coil array detect the same sparse object weighted with the coil sensitivity profiles. The practical use of this new algorithm is demonstrated in dynamic radial cardiac imaging. Accurate reconstructions of cardiac motion in free breathing without ECG triggering were obtained for high undersampling factors. An Iterative GRAPPA algorithm is introduced in Chapter 6 that can recover undersampled data from arbitrary (Non-Cartesian) trajectories and works solely in the Cartesian plane. This characteristic makes the proposed Iterative GRAPPA computationally more efficient than SPIRiT. Iterative GRAPPA was developed in a preceding step to combine parallel imaging with CS. Optimal parameters for Iterative GRAPPA (e.g. number of iterations, GRAPPA kernel size) were determined in phantom experiments and verified by retrospectively undersampling and reconstructing a radial cardiac cine dataset. The synergistic combination of the coil-by-coil Strict DC CS method and Iterative GRAPPA called CS-GRAPPA is presented in Chapter 7. CS-GRAPPA allows accurate reconstruction of undersampled data from even higher acceleration factors than each individual method. It is a formulation equivalent to L1-SPIRiT but computationally more efficient. Additionally, a comparison with CS-CC is given. Interestingly, exploiting joint sparsity in CS-CC is slightly more efficient than the proposed CS-GRAPPA, a hybrid of parallel imaging and CS. The last chapter of this thesis concludes the findings presented in this dissertation. Future applications expected to benefit from CS are discussed and possible synergistic combinations with other existing MR methodologies for accelerated imaging are also contemplated. 2011 urn:nbn:de:bvb:20-opus-72496 Physikalisches Institut