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• W1015380743 abstract "Magnetic resonance imaging (MRI) experiments can be sensitised to the erratic thermal displacement of (water) molecules by the application of magnetic field gradients. The associated signal decay allows for the quantification of the Brownian motion in terms of diffusion coefficients. In linearly structured biological tissues such as brain white matter where cell boundaries of bundled nerve fibres restrict and hinder free propagation, a marked dependence of the results on the direction of the gradients can be observed, the highest diffusivity being measured parallel to the fibre orientation. On the comparatively course scale of the MRI resolution contrasted with the cellular level, some aspects of the molecular dynamics in simple anisotropic geometries have been successfully characterised by an average diffusion tensor whose main eigenvector then coincides with the material's principal direction. For the reliable determination of the model parameters, a considerable number of data sets has to be acquired. Since the computation relies on local signal comparison, this time-consuming experiment is prone to errors arising from subject movement in in-vivo studies.To date, the fastest MRI diffusion measurements are based on imaging sequences which are encumbered by distortion artefacts partly due to their reliance on gradient echos for signal generation. In contrast, the approach followed in this thesis builds on the sizeably slower single-shot stimulated echo acquisition mode (SSSTEAM) which however maps the anatomy truthfully. In order to better comply with the stringent time constraints, a data space reduction scheme, parallel imaging, has been adopted here. This method makes use of the data redundancy afforded by the simultaneous recording of the NMR (Nuclear Magnetic Resonance) signal with multiple receivers. For the application to SSSTEAM particular care has to be taken to redistribute the available magnetisation contributing to the signal over the data space via a defined modulation of the excitation strength in order to control the point spread function (PSF) of the acquisition and thus to honour the nominal resolution. The current work presents an exact approach to this problem which supports the shaping of arbitrary PSFs as well as a broader class of data space trajectories than a previously published approximate ansatz which it extends.In comparison with a reference sequence, diffusion-weighted measurements using SSSTEAM with the minimal parallel imaging reduction factor are here shown to yield data with uncompromised quality as measured by the signal-to-noise ratio (SNR), in spite of being faster by 45% than the non-accelerated experiment. Theoretical considerations even suggest the possibility of future SNR improvements at increased speed-ups.For the investigation of the image quality a computer program has been devised, in particular to test the measured data for their potential to help elucidate the gross fibre architecture of the human brain by means of main diffusion direction tractography. In addition to the published descriptions of anatomical white matter structures already traced with diffusion tensor MRI, the anatomical fidelity of SSSTEAM allows the reconstruction of fibres in regions poorly accessible to gradient echo sequences. For example the brain stem and even the optic nerve and its surrounding muscles can be depicted. Moreover, the presented tractography-based segmentation results suggest a revisal of the current view on the nature of the axon bundles populating the anterior internal capsule." @default.
• W1015380743 created "2016-06-24" @default.
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• W1015380743 date "2022-02-20" @default.
• W1015380743 modified "2023-09-24" @default.
• W1015380743 title "Parallele Datenakquisition zur Beschleunigung Diffusionsgewichteter Kernspintomographie mit Stimulierten Echos" @default.
• W1015380743 doi "https://doi.org/10.53846/goediss-2699" @default.
• W1015380743 hasPublicationYear "2022" @default.
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