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Abstract #1424

Image quality impact of randomized sampling trajectories: implications for compressed sensing

Melissa Jones1, Richard Frayne1,2,3, and Robert Marc Lebel1,2,3,4

1Biomedical Engineering, University of Calgary, Calgary, AB, Canada, 2Seaman Family MR Centre, Foothills Medical Centre, Calgary, AB, Canada, 3Radiology, University of Calgary, Calgary, AB, Canada, 4GE Healthcare, Calgary, AB, Canada

Compressed sensing (CS) has the potential to drastically reduce MR acquisition times, however image quality of prospectively implemented CS is not as good as predicted by retrospectively under-sampled data. This may be due to the sensitivity of appropriate (randomized) CS k-space sampling to eddy currents. We show the existence of these detrimental interactions in full but randomly-sampled k-space, and quantify these interactions in under-sampled CS image reconstruction. We demonstrate how sorting the acquisition order to minimize the total k-space trajectory length mitigates this issue and improves image quality.

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