Susceptibility-weighted magnetic resonance imaging is a powerful tool for high resolution imaging of the vasculature, aiding in the diagnosis of many pathologic conditions. The technique is especially beneficial at hi...
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Susceptibility-weighted magnetic resonance imaging is a powerful tool for high resolution imaging of the vasculature, aiding in the diagnosis of many pathologic conditions. The technique is especially beneficial at higher field strengths where traditional sequences that measure cerebral blood volume suffer from severe distortions, rendering them inapplicable at 7 T. However, conventional susceptibility-weighted imaging (SWI) sequences involve long scan times, on the order of 10 minutes for a 2 cm slab of coverage. This work implemented two partially parallel imaging reconstruction methods, 1) an autocalibrating parallel technique based on GRAPPA algorithm, and 2) sensitivity encoding (SENSE) for accelerating SWI of the brain at 7 Tesla. By employing twofold under-sampling in the phase-encoding direction for both techniques, a two-fold reduction in scan time was simulated. Analysis of contrast ratios in large and small vessels compared to the surrounding brain parenchyma showed close agreement between the full and GRAPPA reconstructed datasets for both vessel sizes, while a decrease in the small vessel contrast was observed with SENSE
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