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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