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.
Keywords
- Magnetic resonance fingerprinting
- Parameter mapping
- Image reconstruction
- Convolutional neural network
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Acknowledgements
This research was supported by the Swiss Foundation for Research on Muscle Diseases (ssem), grant attributed to author OS. The authors thank the NVIDIA Corporation for their GPU donation.
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Balsiger, F. et al. (2018). Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks. In: Knoll, F., Maier, A., Rueckert, D. (eds) Machine Learning for Medical Image Reconstruction. MLMIR 2018. Lecture Notes in Computer Science(), vol 11074. Springer, Cham. https://doi.org/10.1007/978-3-030-00129-2_5
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DOI: https://doi.org/10.1007/978-3-030-00129-2_5
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