Abstract #2421

A Neural Network for Rapid Generation of Cardiac MR Fingerprinting Dictionaries with Arbitrary Heart Rhythms

Jesse Ian Hamilton¹, Danielle Currey², Mark Griswold^1,3, and Nicole Seiberlich^1,3

¹Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, ²Computer Science, Johns Hopkins University, Baltimore, MD, United States, ³Radiology, University Hospitals, Cleveland, OH, United States

Cardiac MR Fingerprinting with ECG gating typically requires that a new Bloch equation simulation be performed after each scan so that the subject’s cardiac rhythm is incorporated in the dictionary. However, this may be challenging for clinical translation and online reconstruction. This study proposes to use a neural network to rapidly generate the dictionary when given input ranges for T₁ and T₂, as well as the cardiac rhythm (RR intervals). The network produces dictionaries for arbitrary cardiac rhythms and is more than 100 times faster than performing a Bloch equation simulation.

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