RinQ Fingerprinting: Recurrence-Informed Quantile Networks for Magnetic Resonance Fingerprinting

Hoppe E, Thamm F, Körzdörfer G, Syben-Leisner C, Schirrmacher F, Nittka M, Pfeuffer J, Meyer H, Maier A (2019)

Publication Language: English

Publication Type: Conference contribution

Publication year: 2019


Publisher: Springer

Edited Volumes: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

City/Town: Cham

Pages Range: 92-100

Conference Proceedings Title: Proceedings of MICCAI 2019

Event location: Shenzhen CN

ISBN: 9783030322472

DOI: 10.1007/978-3-030-32248-9_11


Recently, Magnetic Resonance Fingerprinting (MRF) was proposed as a quantitative imaging technique for the simultaneous acquisition of tissue parameters such as relaxation times T1 and T2. 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 CNNs for the MRF reconstruction reducing the error in T1 and T2 by more than 80%.

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How to cite


Hoppe, E., Thamm, F., Körzdörfer, G., Syben-Leisner, C., Schirrmacher, F., Nittka, M.,... Maier, A. (2019). RinQ Fingerprinting: Recurrence-Informed Quantile Networks for Magnetic Resonance Fingerprinting. In Proceedings of MICCAI 2019 (pp. 92-100). Shenzhen, CN: Cham: Springer.


Hoppe, Elisabeth, et al. "RinQ Fingerprinting: Recurrence-Informed Quantile Networks for Magnetic Resonance Fingerprinting." Proceedings of the Medical Image Computing and Computer Assisted Intervention, Shenzhen Cham: Springer, 2019. 92-100.

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