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 Katsaggelos^1,2,3, Florian A Schiffers³, Cynthia K Rigsby^1,4, Hassan Haji-Valizadeh⁵, and Daniel Kim^1,6

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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