Compressed Sensing in MRI : optimization-based design of k-space filling curves for accelerated MRI

Abstract : Magnetic resonance imaging (MRI) is one of the most powerful and safest imaging modalities for examining the human body. High-resolution MRI is expected to aid in the understanding and diagnosis of many neurodegenerative pathologies involving submillimetric lesions or morphological alterations, such as Alzheimer’s disease and multiple sclerosis. Although high-magnetic-field systems can deliver a sufficient signal-to-noise ratio (SNR) to increase spatial resolution, long scan times and motion sensitivity continue hindering the utilization of high resolution MRI. Despite the development of corrections for bulk and physiological motion, lengthy acquisition times remain a major obstacle to high-resolution acquisition, especially in clinical applications.In the last decade, the newly developed theory of compressed sensing (CS) offered a promising solution for reducing the MRI scan time. After having explained the theory of compressed sensing, this PhD project proposes an empirical and quantitative analysis of the maximum undersampling factor achievable with CS for T ₂ *-weighted imaging.Furthermore, the application of CS to MRI commonly relies on simple sampling patterns such as straight lines, spirals or slight variations of these elementary shapes, which do not take full advantage of the degrees of freedom offered by the hardware and cannot be easily adapted to fit an arbitrary sampling distribution. In this PhD thesis, I have introduced a method called SPARKLING, that may overcome these limitations by taking a radically new approach to the design of k-space sampling. The acronym SPARKLING stands for Spreading Projection Algorithm for Rapid K-space sampLING. It is a versatile method inspired from stippling techniques that automatically generates optimized non-Cartesian sampling patterns compatible with MR hardware constraints on maximum gradient amplitude and slew rate. These sampling curves are designed to comply with key criteria for optimal sampling: a controlled distribution of samples and a locally uniform k-space coverage. Before engaging into experiments, we verified that our gradient system was capable of executing the complex gradient waveforms. We implemented a local phase measurement method and we observed a very good adequacy between prescribed and measured k-space trajectories.Finally, combining sampling efficiency with compressed sensing and parallel imaging, the SPARKLING sampling patterns allowed up to 20-fold reductions in MR scan time, compared to fully-sampled Cartesian acquisitions, for T ₂ *-weighted imaging without deterioration of image quality, as demonstrated by our experimental results at 7 Tesla on in vivo human brains. In comparison to existing non-Cartesian sampling strategies (spiral and radial), the proposed technique also yielded superior image quality. Finally, the proposed approach was also extended to 3D imaging and applied at 3 Tesla for which preliminary results on ex vivo phantoms at 0.8 mm isotropic resolution suggest the possibility to reach very high acceleration factors up to 60 for T ₂ *-weighting and susceptibility-weighted imaging.
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Carole Lazarus. Compressed Sensing in MRI : optimization-based design of k-space filling curves for accelerated MRI. Signal and Image processing. Université Paris-Saclay, 2018. English. ⟨NNT : 2018SACLS309⟩. ⟨tel-02170511v2⟩

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