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Abstract #4849

The Influence of Various Adaptive Radial Undersampling Schemes on Compressed-Sensing L1-Regularized Reconstruction

Rachel Wai-chung Chan1, Elizabeth Anne Ramsay2, Donald Bruce Plewes2

1Medical Biophysics, University of Toronto, Toronto, ON, Canada; 2Imaging Research, University of Toronto, Toronto, ON, Canada

Adaptive radial imaging allows multiple images to be retrospectively reconstructed from the same dataset, each with a different spatial-temporal balance. It has been shown that compressed sensing reconstruction can be used reduce streak artifacts in high-temporal-resolution images created by radial undersampling. Here, we compare the effect of 3 adaptive sampling schemes (golden angle, bit-reversed, and random sampling scheme) on the ability of CS reconstruction to reduce streak artifacts, at various spatiotemporal resolutions. Results show that CS reconstruction lowers the degree of error and mostly preserves the differences among sampling schemes compared to Fourier reconstruction.