Abstract #1177

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

Michele Scipioni¹, Niccolo Fuin^2,3, Julie Price^3,4, Onofrio A. Catalano^3,4, and Ciprian Catana^3,4

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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