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Abstract #0568

A Multi-scale Deep ResNet for Radial MR Parameter Mapping

Zhiyang Fu1, Sagar Mandava1, Mahesh Bharath Keerthivasan1, Diego R Martin2, Maria I Altbach2, and Ali Bilgin1,2,3

1Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States, 2Department of Medical Imaging, University of Arizona, Tucson, AZ, United States, 3Biomedical Engineering, University of Arizona, Tucson, AZ, United States

Quantitative mapping of MR parameters has shown great potential for tissue characterization but long acquisition times required by conventional techniques limit their widespread adoption in the clinic. Recently, model-based compressive sensing (CS) reconstructions that produce accurate parameter maps from a limited amount of data have been proposed but these techniques require long reconstruction times making them impractical for routine clinical use. In this work, we propose a multi-scale deep ResNet for MR parameter mapping. Experimental results illustrate that the proposed method achieves reconstruction quality comparable to model-based CS approaches with orders of magnitude faster reconstruction times.

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