Title | ||
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RinQ Fingerprinting: Recurrence-Informed Quantile Networks for Magnetic Resonance Fingerprinting. |
Abstract | ||
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Recently, Magnetic Resonance Fingerprinting (MRF) was proposed as a quantitative imaging technique for the simultaneous acquisition of tissue parameters such as relaxation times T-1 and T-2. Although the acquisition is highly accelerated, the state-of-the-art reconstruction suffers from long computation times: Template matching methods are used to find the most similar signal to the measured one by comparing it to pre-simulated signals of possible parameter combinations in a discretized dictionary. Deep learning approaches can overcome this limitation, by providing the direct mapping from the measured signal to the underlying parameters by one forward pass through a network. In this work, we propose a Recurrent Neural Network (RNN) architecture in combination with a novel quantile layer. RNNs are well suited for the processing of time-dependent signals and the quantile layer helps to overcome the noisy outliers by considering the spatial neighbors of the signal. We evaluate our approach using in-vivo data from multiple brain slices and several volunteers, running various experiments. We show that the RNN approach with small patches of complex-valued input signals in combination with a quantile layer outperforms other architectures, e.g. previously proposed Convolutional Neural Networks for the MRF reconstruction reducing the error in T-1 and T-2 by more than 80%. |
Year | DOI | Venue |
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2019 | 10.1007/978-3-030-32248-9_11 | Lecture Notes in Computer Science |
Keywords | DocType | Volume |
Deep learning,Recurrent Neural Networks,Magnetic resonance fingerprinting reconstruction | Conference | 11766 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 9 |
Name | Order | Citations | PageRank |
---|---|---|---|
Elisabeth Hoppe | 1 | 1 | 2.05 |
Florian Thamm | 2 | 0 | 0.34 |
Gregor Körzdörfer | 3 | 0 | 0.34 |
Christopher Syben | 4 | 21 | 6.40 |
Franziska Schirrmacher | 5 | 5 | 5.86 |
Mathias Nittka | 6 | 0 | 1.01 |
Josef Pfeuffer | 7 | 39 | 9.50 |
Heiko Meyer | 8 | 0 | 0.34 |
Andreas K. Maier | 9 | 560 | 178.76 |