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• W2211925052 abstract "We introduce a novel algorithm to recover real time dynamic MR images from highly under-sampled k- t space measurements. The proposed scheme models the images in the dynamic dataset as points on a smooth, low dimensional manifold in high dimensional space. We propose to exploit the non-linear and non-local redundancies in the dataset by posing its recovery as a manifold smoothness regularized optimization problem. A navigator acquisition scheme is used to determine the structure of the manifold, or equivalently the associated graph Laplacian matrix. The estimated Laplacian matrix is used to recover the dataset from undersampled measurements. The utility of the proposed scheme is demonstrated by comparisons with state of the art methods in multi-slice real-time cardiac and speech imaging applications." @default.
• W2211925052 created "2016-06-24" @default.
• W2211925052 creator A5072299998 @default.
• W2211925052 creator A5073618680 @default.
• W2211925052 date "2016-04-01" @default.
• W2211925052 modified "2023-10-13" @default.
• W2211925052 title "Dynamic MRI Using SmooThness Regularization on Manifolds (SToRM)" @default.
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• W2211925052 doi "https://doi.org/10.1109/tmi.2015.2509245" @default.
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