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arXiv · 1807.06356

Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks

Fabian Balsiger·Amaresha Shridhar Konar·Shivaprasad Chikop·Vimal Chandran·Olivier Scheidegger·Sairam Geethanath·Mauricio Reyes
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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.

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Fabian Balsiger, Amaresha Shridhar Konar, Shivaprasad Chikop, Vimal Chandran, Olivier Scheidegger, Sairam Geethanath, Mauricio Reyes. 2018-07-17. Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks. https://doi.org/10.1007/978-3-030-00129-2_5

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