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Abstract #0749

Compressed Sensing MRI Exploiting Complementary Dual Decomposition

Suhyung Park 1 , Chul-Ho Sohn 2 , and Jaeseok Park 1

1 Department of Brain and Cognitive Engineering, Korea University, Seoul, Seoul, Korea, 2 Department of Radiology, Seoul National University Hospital, Seoul, Korea

Compressed sensing (CS) [1,2] exploits the sparsity of an image in a transform domain. However, it has been shown that CS suffers particularly from loss of low contrast image features with increasing reduction factor. To retain image details, in this work we introduce a novel CS algorithm exploiting feature-based complementary dual decomposition with joint estimation of local scale mixture (LSM) model and images. Images are decomposed into dual block sparse components: total variation (TV) for piecewise smooth parts and wavelets for residuals. The LSM model parameters of residuals in the wavelet domain are estimated and then employed as a regional constraint in spatially adaptive reconstruction of high frequency subbands to restore image details missing in piecewise smooth parts. Experiments demonstrate the superior performance of the proposed method in preserving low-contrast image features even at high reduction factors.

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