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Rosette k-space trajectories are faster, more motion robust, for dynamic Magnetic Resonance Imaging data, in this work we have extended the concept of Rosette trajectories to time-dimensions, repeated sampling of k-space points leads to off-resonance behavior and cause destructive interference or loss of image intensity but with little blurring, off-resonance signal can be removed as background noise, to restore correlations between data, we regarded the sampling data as a tensor and introduced iterative reconstruction method derived from Total Variation and tensor-Singular Value Decomposition regularization terms for dynamic Magnetic Resonance Imaging reconstruction. The experimental results demonstrate that the proposed reconstruction model can effectively achieve better performance and satisfied quality for under-sampled k-space data.
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