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.
@article{arxiv.1807.06356,
title = {Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks},
author = {Fabian Balsiger and Amaresha Shridhar Konar and Shivaprasad Chikop and Vimal Chandran and Olivier Scheidegger and Sairam Geethanath and Mauricio Reyes},
journal= {arXiv preprint arXiv:1807.06356},
year = {2019}
}
Comments
Accepted for Machine Learning for Medical Image Reconstruction (MLMIR) workshop at MICCAI 2018. The revision corrects Amaresha's last name and Section 2.1 (scanner type and flip angles)