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Abstract #2880

Optimized Density Compensation Function for Filtered Backprojection and Compressed Sensing Reconstruction in Radial k-space MRI

KyungPyo Hong1, Amanda L DiCarlo1, Aggelos K Katsaggelos1,2,3, Florian A Schiffers3, Cynthia K Rigsby1,4, Hassan Haji-Valizadeh5, and Daniel Kim1,6
1Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States, 2Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States, 3Computer Science, Northwestern University, Evanston, IL, United States, 4Medical Imaging, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, United States, 5Internal Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States, 6Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, United States

While conventional density compensation function(DCF) performs sufficiently well for filtered backprojection(FBP) and radial k-space MRI when the Nyquist sampling condition is met and/or evenly-spaced view angles are used, it may perform poorly when sub-sampling and/or irrational-view angles are used. We propose an optimized DCF for the aforementioned conditions by calculating the density weights based on geometric properties of radial k-space sampling in a discrete environment, regardless of scan conditions such as data sizes and view angles. Compared with standard DCF, the optimized DCF produces higher signal-to-noise ratio(SNR) in FBP (phantom) and more accurate flow metrics in 48-fold accelerated, phase-contrast MRI.

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