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 |
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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 | |
Publication status | Published - 2020 |
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) |
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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 |
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Country/Territory | Peru |
City | Lima |
Period | 20/10/4 → 20/10/8 |
Bibliographical note
Publisher Copyright:© 2020, Springer Nature Switzerland AG.
Keywords
- 3D MR fingerprinting
- GRAPPA
- Graph convolution
- K-space interpolation
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science