Abstract #1424

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

Melissa Jones¹, Richard Frayne^1,2,3, and Robert Marc Lebel^1,2,3,4

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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