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Abstract #3416

MR Image Reconstruction with Optimized Gaussian Mixture Model for Structured Sparsity

Zechen Zhou 1 , Niranjan Balu 2 , Rui Li 1 , Jinnan Wang 2,3 , and Chun Yuan 1,2

1 Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 2 Vascular Imaging Lab, Department of Radiology, University of Washington, Seattle, WA, United States, 3 Philips Research North America, Briarcliff Manor, NY, United States

Parallel Imaging (PI) and Compressed Sensing (CS) enable accelerated MR imaging. However, the actual PI-CS reconstruction performance is usually limited by noise amplification and image boundary/structure blurring particularly at high reduction factor. In this work, a Gaussian Mixture Model (GMM) was optimized to promote structured sparsity and it was further merged into the SPIRiT framework as a regularization constraint. The proposed algorithm has demonstrated its improved performance for image boundary and detail structure preservation in accelerated 3D high resolution brain imaging.

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