Acceleration of High-Resolution 3D MR Fingerprinting via a Graph Convolutional Network

Feng Cheng; Yong Chen; Xiaopeng Zong; Weili Lin; Dinggang Shen; Pew Thian Yap

doi:10.1007/978-3-030-59713-9_16

Acceleration of High-Resolution 3D MR Fingerprinting via a Graph Convolutional Network

Feng Cheng, Yong Chen, Xiaopeng Zong, Weili Lin, Dinggang Shen, Pew Thian Yap

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Magnetic resonance fingerprinting (MRF) is a novel imaging framework for fast and simultaneous quantification of multiple tissue properties. Recently, 3D MRF methods have been developed, but the acquisition speed needs to be improved before they can be adopted for clinical use. The purpose of this study is to develop a novel deep learning approach to accelerate 3D MRF acquisition along the slice-encoding direction in k-space. We introduce a graph-based convolutional neural network that caters to non-Cartesian spiral trajectories commonly used for MRF acquisition. We improve tissue quantification accuracy compared with the state of the art. Our method enables fast 3D MRF with high spatial resolution, allowing whole-brain coverage within 5 min, making MRF more feasible in clinical settings.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
Editors	Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	158-166
Number of pages	9
ISBN (Print)	9783030597122
DOIs	https://doi.org/10.1007/978-3-030-59713-9_16
Publication status	Published - 2020
Externally published	Yes
Event	23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 - Lima, Peru Duration: 2020 Oct 4 → 2020 Oct 8

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12262 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Country/Territory	Peru
City	Lima
Period	20/10/4 → 20/10/8

Keywords

3D MR fingerprinting
GRAPPA
Graph convolution
K-space interpolation

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-030-59713-9_16

Cite this

Cheng, F., Chen, Y., Zong, X., Lin, W., Shen, D., & Yap, P. T. (2020). Acceleration of High-Resolution 3D MR Fingerprinting via a Graph Convolutional Network. In A. L. Martel, P. Abolmaesumi, D. Stoyanov, D. Mateus, M. A. Zuluaga, S. K. Zhou, D. Racoceanu, & L. Joskowicz (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings (pp. 158-166). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12262 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59713-9_16

Acceleration of High-Resolution 3D MR Fingerprinting via a Graph Convolutional Network. / Cheng, Feng; Chen, Yong; Zong, Xiaopeng; Lin, Weili; Shen, Dinggang; Yap, Pew Thian.

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. ed. / Anne L. Martel; Purang Abolmaesumi; Danail Stoyanov; Diana Mateus; Maria A. Zuluaga; S. Kevin Zhou; Daniel Racoceanu; Leo Joskowicz. Springer Science and Business Media Deutschland GmbH, 2020. p. 158-166 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12262 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Cheng, F, Chen, Y, Zong, X, Lin, W, Shen, D & Yap, PT 2020, Acceleration of High-Resolution 3D MR Fingerprinting via a Graph Convolutional Network. in AL Martel, P Abolmaesumi, D Stoyanov, D Mateus, MA Zuluaga, SK Zhou, D Racoceanu & L Joskowicz (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12262 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 158-166, 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, Lima, Peru, 20/10/4. https://doi.org/10.1007/978-3-030-59713-9_16

Cheng F, Chen Y, Zong X, Lin W, Shen D, Yap PT. Acceleration of High-Resolution 3D MR Fingerprinting via a Graph Convolutional Network. In Martel AL, Abolmaesumi P, Stoyanov D, Mateus D, Zuluaga MA, Zhou SK, Racoceanu D, Joskowicz L, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 158-166. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-59713-9_16

Cheng, Feng ; Chen, Yong ; Zong, Xiaopeng ; Lin, Weili ; Shen, Dinggang ; Yap, Pew Thian. / Acceleration of High-Resolution 3D MR Fingerprinting via a Graph Convolutional Network. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings. editor / Anne L. Martel ; Purang Abolmaesumi ; Danail Stoyanov ; Diana Mateus ; Maria A. Zuluaga ; S. Kevin Zhou ; Daniel Racoceanu ; Leo Joskowicz. Springer Science and Business Media Deutschland GmbH, 2020. pp. 158-166 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Acceleration of High-Resolution 3D MR Fingerprinting via a Graph Convolutional Network",

abstract = "Magnetic resonance fingerprinting (MRF) is a novel imaging framework for fast and simultaneous quantification of multiple tissue properties. Recently, 3D MRF methods have been developed, but the acquisition speed needs to be improved before they can be adopted for clinical use. The purpose of this study is to develop a novel deep learning approach to accelerate 3D MRF acquisition along the slice-encoding direction in k-space. We introduce a graph-based convolutional neural network that caters to non-Cartesian spiral trajectories commonly used for MRF acquisition. We improve tissue quantification accuracy compared with the state of the art. Our method enables fast 3D MRF with high spatial resolution, allowing whole-brain coverage within 5 min, making MRF more feasible in clinical settings.",

keywords = "3D MR fingerprinting, GRAPPA, Graph convolution, K-space interpolation",

author = "Feng Cheng and Yong Chen and Xiaopeng Zong and Weili Lin and Dinggang Shen and Yap, {Pew Thian}",

note = "Funding Information: This work was supported in part by NIH grant EB022880. Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 ; Conference date: 04-10-2020 Through 08-10-2020",

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doi = "10.1007/978-3-030-59713-9_16",

language = "English",

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AB - Magnetic resonance fingerprinting (MRF) is a novel imaging framework for fast and simultaneous quantification of multiple tissue properties. Recently, 3D MRF methods have been developed, but the acquisition speed needs to be improved before they can be adopted for clinical use. The purpose of this study is to develop a novel deep learning approach to accelerate 3D MRF acquisition along the slice-encoding direction in k-space. We introduce a graph-based convolutional neural network that caters to non-Cartesian spiral trajectories commonly used for MRF acquisition. We improve tissue quantification accuracy compared with the state of the art. Our method enables fast 3D MRF with high spatial resolution, allowing whole-brain coverage within 5 min, making MRF more feasible in clinical settings.

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Acceleration of High-Resolution 3D MR Fingerprinting via a Graph Convolutional Network

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