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.
CITATION STYLE
Balsiger, F., Shridhar Konar, A., Chikop, S., Chandran, V., Scheidegger, O., Geethanath, S., & Reyes, M. (2018). Magnetic resonance fingerprinting reconstruction via spatiotemporal convolutional neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11074 LNCS, pp. 39–46). Springer Verlag. https://doi.org/10.1007/978-3-030-00129-2_5
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