Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction

• Published in: Graph Learning in Medical Imaging Vol. 11849; pp. 44 - 52
• Main Authors: Dzyubachyk, Oleh, Koolstra, Kirsten, Pezzotti, Nicola, Lelieveldt, Boudewijn P. F., Webb, Andrew, Börnert, Peter
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Dimensionality reduction; Magnetic Resonance Fingerprinting (MRF); Point cloud registration; t-SNE
• ISBN: 303035816X; 9783030358167
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-35817-4_6

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.