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Abstract #1911

Nested tracer-kinetic model-based DCE-MRI reconstruction from under-sampled data

Sajan Goud Lingala1, Yi Guo1, Naren Nallapareddy2, Yannick Bliesener1, R Marc Lebel3, and Krishna S Nayak1

1Electrical Engineering, University of Southern California, Los Angeles, CA, United States, 2Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 3GE Healthcare, Calgary, Canada

We propose a novel nested tracer-kinetic (TK) model based constrained reconstruction method for DCE-MRI reconstruction from under-sampled data. This approach models the concentration time profiles as a sparse linear combination of temporal bases constructed from TK models of varying complexity. Subspaces from the models of plasma volume, Patlak, and the extended-Tofts are constructed. A spatial mask determining the TK model complexity at every pixel location is derived. Reconstruction involves iteration between data consistency and pixel wise projection of the concentration profiles on one of the three subspaces. We demonstrate its utility in retrospective under-sampled reconstruction of brain tumor DCE-MRI datasets.

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