Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Bloch Simulations for MR Fingerprinting by Invertible Neural
  Networks   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.

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The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: cmp-lg
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The categories of this paper are: atom-ph
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat