Magnetic Resonance Fingerprinting Using Recurrent Neural Networks
Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitative magnetic resonance imaging that allows simultaneous measurement of multiple tissue properties in a single, time efficient acquisition. Standard MRF reconstructs parametric maps using dictionary matching and requires high computational time. We propose to perform MRF map reconstruction using a recurrent neural network, which exploits the time dependent information of the MRF signal evolution. We evaluate our method on multiparametric synthetic signals and compare it to existing MRF map reconstruction approaches, including those based on neural networks. Our method achieves state of the art estimates of T1 and T2 values. In addition, the reconstruction time is reduced compared to dictionary matching based approach.
2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)