Abstract #4528

t-Distributed Stochastic Neighbor Embedding (t-SNE) as a Tool for Visualizing the Encoding Capability of Magnetic Resonance Fingerprinting (MRF) Dictionaries

Kirsten Koolstra¹, Peter Börnert^1,2, Boudewijn Lelieveldt^3,4, Andrew Webb¹, and Oleh Dzyubachyk³

¹C.J. Gorter Center for High Field MRI, Radiology, Leiden University Medical Center, Leiden, Netherlands, ²Philips Research Hamburg, Hamburg, Germany, ³Division of Image Processing, Radiology, Leiden University Medical Center, Leiden, Netherlands, ⁴Intelligent Systems Department, Delft University of Technology, Delft, Netherlands

In Magnetic Resonance Fingerprinting (MRF), the quality of the parameter maps depends on the encoding capability of the variable flip angle train. In this work we show how the dimensionality reduction technique t-Distributed Stochastic Neighbor Embedding (t-SNE) can be used to obtain insight into the encoding capability of different MRF sequences by embedding high-dimensional MRF dictionaries into a lower-dimensional space and visualizing them as colormaps. Experiments on example dictionaries perform comparison between different sequences and assess the effect of B1+ variations on the encoding capability.

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