  In Magnetic Resonance Fingerprinting (MRF) the quality of the estimated
parameter maps depends on the encoding capability of the variable flip angle
train. In this work we show how the dimensionality reduction technique
Hierarchical Stochastic Neighbor Embedding (HSNE) can be used to obtain insight
into the encoding capability of different MRF sequences. Embedding
high-dimensional MRF dictionaries into a lower-dimensional space and
visualizing them with colors, being a surrogate for location in low-dimensional
space, provides a comprehensive overview of particular dictionaries and, in
addition, enables comparison of different sequences. Dictionaries for various
sequences and sequence lengths were compared to each other, and the effect of
transmit field variations on the encoding capability was assessed. Clear
differences in encoding capability were observed between different sequences,
and HSNE results accurately reflect those obtained from an MRF matching
simulation.
