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2018 | OriginalPaper | Chapter

Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks

Authors : Fabian Balsiger, Amaresha Shridhar Konar, Shivaprasad Chikop, Vimal Chandran, Olivier Scheidegger, Sairam Geethanath, Mauricio Reyes

Published in: Machine Learning for Medical Image Reconstruction

Publisher: Springer International Publishing

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Abstract

Magnetic resonance fingerprinting (MRF) quantifies multiple nuclear magnetic resonance parameters in a single and fast acquisition. Standard MRF reconstructs parametric maps using dictionary matching, which lacks scalability due to computational inefficiency. We propose to perform MRF map reconstruction using a spatiotemporal convolutional neural network, which exploits the relationship between neighboring MRF signal evolutions to replace the dictionary matching. We evaluate our method on multiparametric brain scans and compare it to three recent MRF reconstruction approaches. Our method achieves state-of-the-art reconstruction accuracy and yields qualitatively more appealing maps compared to other reconstruction methods. In addition, the reconstruction time is significantly reduced compared to a dictionary-based approach.
Literature
5.
go back to reference Hoppe, E., et al.: Deep learning for magnetic resonance fingerprinting: a new approach for predicting quantitative parameter values from time series. In: Röhrig, R., Timmer, A., Binder, H., Sax, U. (eds.) German Medical Data Sciences: Visions and Bridges, Oldenburg, vol. 243, pp. 202–206 (2017). https://​doi.​org/​10.​3233/​978-1-61499-808-2-202 Hoppe, E., et al.: Deep learning for magnetic resonance fingerprinting: a new approach for predicting quantitative parameter values from time series. In: Röhrig, R., Timmer, A., Binder, H., Sax, U. (eds.) German Medical Data Sciences: Visions and Bridges, Oldenburg, vol. 243, pp. 202–206 (2017). https://​doi.​org/​10.​3233/​978-1-61499-808-2-202
7.
go back to reference Shaik, I., et al.: Tailored magnetic resonance fingerprinting: optimizing acquisition schedule and intelligent reconstruction using a block approach. In: ISMRM 2018 (2018) Shaik, I., et al.: Tailored magnetic resonance fingerprinting: optimizing acquisition schedule and intelligent reconstruction using a block approach. In: ISMRM 2018 (2018)
Metadata
Title
Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks
Authors
Fabian Balsiger
Amaresha Shridhar Konar
Shivaprasad Chikop
Vimal Chandran
Olivier Scheidegger
Sairam Geethanath
Mauricio Reyes
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-030-00129-2_5

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