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Abstract #2606

Self-Updating NonLocal Total Variation for Highly Undersampled Variable Density Spiral Reconstruction

Sheng Fang1, Wenchuan Wu2, Kui Ying3, Hua Guo2

1Institute of nulcear and new energy technology, Tsinghua University, Beijing, China; 2Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China; 3Department of engineering physics, Tsinghua University, Beijing, China

A Nonlocal Total variation (NLTV) that automatically refines the image-dependent weights was proposed for reconstructing highly undersampled variable density spiral (VDS) imaging data. Unlike existing NLTV-related method, the proposed method doesnt rely on a reference image for weight map estimation. Instead, it automatically updates the weight based on a filtered intermediate image. The wavelet soft shrinkage method is used for the filtering step. Since the aliasing artifact of VDS is incoherent, it can be expected that the shrinkage can perturbation of aliasing artifact and increase the accuracy of weight computation. The in vivo VDS experiment demonstrates that the proposed method can effectively suppress noises amplification and perverse better image details than TV.