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Abstract #3878

GenSeT: Reconstruction of k-space under-sampled DCE-MRI data using high-order generalized series and temporal constraint

Hien Minh Nguyen1, Yuan Le2, and Wei Huang3

1Electrical Engineering & Information Technology, Vietnamese-German University, Binh Duong New City, Vietnam, 2Department of Radiology, Mayo Clinic Arizona, Scottsdale, AZ, United States, 3Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, United States

A novel reconstruction method exploiting high-order generalized series and temporal sparsity constraint has been presented for sparsely-sampled DCE-MRI. The method uses a static reference to model high-resolution anatomical structures while extrapolating the missing k-space and imposing the sparsity of the time frame difference. Our initial experience with human breast DCE-MRI data shows that the proposed GenSeT method yields more accurate spatiotemporal dynamics and PK analysis than the conventional zero-filling and TWIST reconstruction methods. Further validation of the method as a useful reconstruction approach for sparsely-sampled DCE-MRI is warranted in a larger cohort and with data from different organs.

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