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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