Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks

Balsiger, Fabian; Jungo, Alain; Scheidegger, Olivier; Marty, Benjamin; Reyes, Mauricio (2020). Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks. In: Deeba, Farah; Johnson, Patricia; Würfl, Tobias; Ye, Jong Chul (eds.) Machine Learning for Medical Image Reconstruction. Lecture Notes in Computer Science: Vol. 12450 (pp. 60-69). Cham: Springer 10.1007/978-3-030-61598-7_6

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Magnetic resonance fingerprinting (MRF) enables fast and multiparametric MR imaging. Despite fast acquisition, the state-of-the-art reconstruction of MRF based on dictionary matching is slow and lacks scalability. To overcome these limitations, neural network (NN) approaches estimating MR parameters from fingerprints have been proposed recently. Here, we revisit NN-based MRF reconstruction to jointly learn the forward process from MR parameters to fingerprints and the backward process from fingerprints to MR parameters by leveraging invertible neural networks (INNs). As a proof-of-concept, we perform various experiments showing the benefit of learning the forward process, i.e., the Bloch simulations, for improved MR parameter estimation. The benefit especially accentuates when MR parameter estimation is difficult due to MR physical restrictions. Therefore, INNs might be a feasible alternative to the current solely backward-based NNs for MRF reconstruction.

Item Type:

Book Section (Book Chapter)


04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Balsiger, Fabian; Jungo, Alain; Scheidegger, Olivier and Reyes, Mauricio


600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology
600 Technology > 620 Engineering




Lecture Notes in Computer Science






Fabian Balsiger

Date Deposited:

20 Nov 2020 17:00

Last Modified:

20 Nov 2020 17:00

Publisher DOI:




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