Hierarchical stochastic neighbor embedding as a tool for visualizing the encoding capability of magnetic resonance fingerprinting dictionaries
This provides a tool for researchers in medical imaging to assess and optimize MRF sequences, though it is incremental as it applies an existing dimensionality reduction method to a specific domain.
The authors tackled the problem of evaluating the encoding capability of Magnetic Resonance Fingerprinting (MRF) sequences, showing that Hierarchical Stochastic Neighbor Embedding (HSNE) can visualize and compare dictionaries, with results matching those from MRF matching simulations.
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.