Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction

Oleh Dzyubachyk; Kirsten Koolstra; Nicola Pezzotti; Boudewijn Lelieveldt; Andrew Webb; Peter Börnert

doi:10.1007/978-3-030-35817-4_6

Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction

Oleh Dzyubachyk^*, Kirsten Koolstra, Nicola Pezzotti, Boudewijn Lelieveldt, Andrew Webb, Peter Börnert

^*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Graph Learning in Medical Imaging
Subtitle of host publication	1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings
Editors	Daoqiang Zhang, Luping Zhou, Biao Jie, Mingxia Liu
Place of Publication	Cham
Publisher	Springer
Pages	44-52
Number of pages	9
ISBN (Electronic)	978-3-030-35817-4
ISBN (Print)	978-3-030-35816-7
DOIs	https://doi.org/10.1007/978-3-030-35817-4_6
Publication status	Published - 2019
Event	1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: 17 Oct 2019 → 17 Oct 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11849
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Country/Territory	China
City	Shenzhen
Period	17/10/19 → 17/10/19

Keywords

Dimensionality reduction
Magnetic Resonance Fingerprinting (MRF)
Point cloud registration
t-SNE

Access to Document

10.1007/978-3-030-35817-4_6

Cite this

Dzyubachyk, O., Koolstra, K., Pezzotti, N., Lelieveldt, B., Webb, A., & Börnert, P. (2019). Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction. In D. Zhang, L. Zhou, B. Jie, & M. Liu (Eds.), Graph Learning in Medical Imaging : 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings (pp. 44-52). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11849 ). Springer. https://doi.org/10.1007/978-3-030-35817-4_6

Dzyubachyk, Oleh ; Koolstra, Kirsten ; Pezzotti, Nicola et al. / Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction. Graph Learning in Medical Imaging : 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings. editor / Daoqiang Zhang ; Luping Zhou ; Biao Jie ; Mingxia Liu. Cham : Springer, 2019. pp. 44-52 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{f2e55f445c3f4a90a4450048998d2fc7,

title = "Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction",

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.",

keywords = "Dimensionality reduction, Magnetic Resonance Fingerprinting (MRF), Point cloud registration, t-SNE",

author = "Oleh Dzyubachyk and Kirsten Koolstra and Nicola Pezzotti and Boudewijn Lelieveldt and Andrew Webb and Peter B{\"o}rnert",

year = "2019",

doi = "10.1007/978-3-030-35817-4_6",

language = "English",

isbn = "978-3-030-35816-7",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "44--52",

editor = "Daoqiang Zhang and Luping Zhou and Biao Jie and Mingxia Liu",

booktitle = "Graph Learning in Medical Imaging",

note = "1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 ; Conference date: 17-10-2019 Through 17-10-2019",

}

Dzyubachyk, O, Koolstra, K, Pezzotti, N, Lelieveldt, B , Webb, A & Börnert, P 2019, Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction. in D Zhang, L Zhou, B Jie & M Liu (eds), Graph Learning in Medical Imaging : 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11849 , Springer, Cham, pp. 44-52, 1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, China, 17/10/19. https://doi.org/10.1007/978-3-030-35817-4_6

Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction. / Dzyubachyk, Oleh; Koolstra, Kirsten; Pezzotti, Nicola et al.
Graph Learning in Medical Imaging : 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings. ed. / Daoqiang Zhang; Luping Zhou; Biao Jie; Mingxia Liu. Cham: Springer, 2019. p. 44-52 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11849 ).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction

AU - Dzyubachyk, Oleh

AU - Koolstra, Kirsten

AU - Pezzotti, Nicola

AU - Lelieveldt, Boudewijn

AU - Webb, Andrew

AU - Börnert, Peter

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N2 - 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.

AB - 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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KW - Magnetic Resonance Fingerprinting (MRF)

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SN - 978-3-030-35816-7

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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A2 - Zhou, Luping

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ER -

Dzyubachyk O, Koolstra K, Pezzotti N, Lelieveldt B , Webb A, Börnert P. Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction. In Zhang D, Zhou L, Jie B, Liu M, editors, Graph Learning in Medical Imaging : 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings. Cham: Springer. 2019. p. 44-52. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-35817-4_6

Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction

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