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Abstract #1177

A kinetic-guided compressed sensing approach for DCE-MRI reconstruction

Michele Scipioni1, Niccolo Fuin2,3, Julie Price3,4, Onofrio A. Catalano3,4, and Ciprian Catana3,4

1Department of Information Engineering, University of Pisa, Pisa, Italy, 2School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 3Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, United States, 4Department of Radiology, Massachusetts General Hospital, Boston, MA, United States

In this work, we propose a reconstruction method for free-breathing DCE-MRI, that combines golden-angle radial sampling, parallel imaging and tracer kinetic (TK) modeling in an iterative reconstruction approach. We introduce a new model in which information coming from TK modeling are treated as a priori knowledge, assisting image reconstruction. The proposed approach is compared with Total Variation Compressed Sensing reconstruction achieving comparable denoising effect in the spatial domain, but improved temporal fidelity and TK modeling.

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