Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction

Conference Paper (2019)
Author(s)

O. Dzyubachyk (Leiden University Medical Center)

K Koolstra (Leiden University Medical Center)

N Pezzotti (Philips Research, TU Delft - Computer Graphics and Visualisation)

Boudewijn P.F. Lelieveldy (Leiden University Medical Center, TU Delft - Pattern Recognition and Bioinformatics)

A.G. Webb (Leiden University Medical Center)

Peter Börnert (Philips Research, Universiteit Leiden)

Research Group
Computer Graphics and Visualisation
DOI related publication
https://doi.org/10.1007/978-3-030-35817-4_6
More Info
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Publication Year
2019
Language
English
Research Group
Computer Graphics and Visualisation
Pages (from-to)
44-52
ISBN (print)
978-3-030-35816-7
ISBN (electronic)
978-3-030-35817-4

Abstract

Quality assessment of different Magnetic Resonance Fingerprinting (MRF) sequences and their corresponding dictionaries remains an unsolved problem. In this work we present a method in which we approach analysis of MRF dictionaries by performing dimensionality reduction and representing them as low-dimensional point sets (embeddings). Dimensionality reduction was performed using a modification of the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm. First, we demonstrated stability of calculated embeddings that allows neglecting the stochastic nature of t-SNE. Next, we proposed and analyzed two algorithms for comparing the embeddings. Finally, we performed two simulations in which we reduced the MRF sequence/dictionary in length or size and analyzed the influence of this reduction on the resulting embedding. We believe that this research can pave the way to development of a software tool for analysis, including better understanding, optimization and comparison, of different MRF sequences.

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