Stochastic neighbor embedding as a tool for visualizing the encoding capability of magnetic resonance fingerprinting dictionaries

Journal Article (2021)
Author(s)

Kirsten Koolstra (Leiden University Medical Center)

P Börnert (Leiden University Medical Center, Philips Innovation Services)

BPF Lelieveldy (Leiden University Medical Center, TU Delft - Pattern Recognition and Bioinformatics)

Andrew G. Webb (Leiden University Medical Center)

Oleh Dzyubachyk (Leiden University Medical Center)

Research Group
Pattern Recognition and Bioinformatics
Copyright
© 2021 Kirsten Koolstra, Peter Börnert, B.P.F. Lelieveldt, A. Webb, Oleh Dzyubachyk
DOI related publication
https://doi.org/10.1007/s10334-021-00963-8
More Info
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Publication Year
2021
Language
English
Copyright
© 2021 Kirsten Koolstra, Peter Börnert, B.P.F. Lelieveldt, A. Webb, Oleh Dzyubachyk
Research Group
Pattern Recognition and Bioinformatics
Issue number
2
Volume number
35 (2022)
Pages (from-to)
223-234
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Abstract

Objective: To visualize the encoding capability of magnetic resonance fingerprinting (MRF) dictionaries. Materials and methods: High-dimensional MRF dictionaries were simulated and embedded into a lower-dimensional space using t-distributed stochastic neighbor embedding (t-SNE). The embeddings were visualized via colors as a surrogate for location in low-dimensional space. First, we illustrate this technique on three different MRF sequences. We then compare the resulting embeddings and the color-coded dictionary maps to these obtained with a singular value decomposition (SVD) dimensionality reduction technique. We validate the t-SNE approach with measures based on existing quantitative measures of encoding capability using the Euclidean distance. Finally, we use t-SNE to visualize MRF sequences resulting from an MRF sequence optimization algorithm. Results: t-SNE was able to show clear differences between the color-coded dictionary maps of three MRF sequences. SVD showed smaller differences between different sequences. These findings were confirmed by quantitative measures of encoding. t-SNE was also able to visualize differences in encoding capability between subsequent iterations of an MRF sequence optimization algorithm. Discussion: This visualization approach enables comparison of the encoding capability of different MRF sequences. This technique can be used as a confirmation tool in MRF sequence optimization.