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Abstract #4443

Geometrically-derived Density Compensation Function for Non-Cartesian k-space Sampling Trajectories in MRI

Oluyemi Bright Aboyewa1,2, Daniel Kim1,2, and KyungPyo Hong2
1Biomedical Engineering Department, Northwestern University, Evanston, IL, United States, 2Radiology Department, Northwestern University, Chicago, IL, United States

Synopsis

Keywords: Image Reconstruction, Image Reconstruction, Density Compensation Function, Radial, Spiral, Rosette, Gridding, Iterative Reconstruction

Motivation: A geometrically-derived density compensation function (gDCF) showed improved performance for accelerated iterative reconstruction of radial k-space data. Its simple concepts may be translatable to other non-Cartesian sampling trajectories.

Goal(s): To extend gDCF to arbitrary 2D non-Cartesian sampling patterns and evaluate its performance (image quality, calculation times) in gridding and iterative reconstruction through numerical simulations.

Approach: We redefined the degree of overlap calculation based on the Beurling-Landau sampling theory and evaluated the new formalism in radial, spiral and rosette trajectories and compared to other DCFs.

Results: The new gDCF works for arbitrary non-Cartesian k-space trajectories and performs fast calculation times

Impact: MRI acquisition using non-Cartesian sampling trajectories has potential benefits for rapid imaging and mitigating motion artifacts. The new gDCF enhances the image reconstruction of any 2D non-Cartesian MRI acquisitions, thus increasing their utility in patient scan.

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