Advanced methods in post cartesian imaging

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Description
Magnetic resonance (MR) imaging with data acquisition on a non-rectangular grid permits a variety of approaches to cover k-space. This flexibility can be exploited to achieve clinically relevant characteristics -- fast yet full coverage for short scan times, center out

Magnetic resonance (MR) imaging with data acquisition on a non-rectangular grid permits a variety of approaches to cover k-space. This flexibility can be exploited to achieve clinically relevant characteristics -- fast yet full coverage for short scan times, center out schemes for short Te, over-sampled k-space for robustness to motion, long acquisition time for improved signal-to-noise (SNR) performance and benign under-sampling (aliasing) artifact. This dissertation presents advances in Periodically Rotated Overlapping ParallEL Lines with Enhanced Reconstruction (PROPELLER) trajectory design and improved reconstruction for spiral imaging. Scan time in PROPELLER imaging can be reduced by tailoring the trajectory to the required Field-Of-View (FOV). A technique to design the PROPELLER trajectory for an elliptical FOV is described. The proposed solution is a set of empirically derived closed form equations that preserve the standard PROPELLER geometry and specify the minimum number of blades necessary. Reconstructing spiral scans requires accurate trajectory information. A simple method to measure the deviation from the designed trajectory due to gradient coupling is presented. A line phantom is used to force a uniform structure in a predetermined orientation in k-space. This uniformity permits measurements of zeroth order trajectory deviations due to gradient coupling. Spiral reconstruction is also sensitive to B0 inhomogeneities (variations in the external magnetic field). This sensitivity manifests itself as a spatially varying blur. An algorithm to correct for concomitant field and first order B0 inhomogeneity effects is developed based on de-blurring via convolution by separable kernels. To reduce computation time, an empirical equation for sufficient kernel length is derived. It is also necessary to know the noise characteristics of the proposed algorithm; this is investigated via Monte-Carlo simulations. The algorithm is further extended to correct for concomitant field artifacts by modeling these artifacts as blurring due to a temporally static field map. This approach has the potential for further reduction in computational cost by combining the B0 map with the concomitant field map to simultaneously correct for artifacts resulting from both field inhomogeneities and concomitant field map.