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Abstract #2650

Multi-Scale Subband Weighted Partially Parallel Imaging

Suhyung Park1, Jaeseok Park1

1Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea

Combination of partially parallel imaging (PPI) and compressed sensing (CS) [1-4] employs complementary properties of the two competitive methods. Among them, direct combination approaches [1-2], which jointly consider both CS and PPI constraints, potentially suffer from image artifacts at high acceleration, because sparsifying transform are less coherent with sensitivity encoding than Fourier encoding. Then, combination of CS and PPI in a sequential fashion [3-4] was recently introduced, demonstrating its feasibility in overcoming the aforementioned problems. In this work, we develop a novel, multi-scale subband weighted PPI algorithm, wherein 1) CS is utilized to yield multi-scale sparse solutions, 2) Subbands in each scale are employed to produce multiple de-noised filtered k-spaces, 3) Join estimation of PPI convolution kernels and k-spaces are performed, considering both inter-subband correlation and spatial correlation over multiple coils.