1,015 research outputs found
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 lacks scalability
due to computational inefficiency. 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
significantly reduced compared to dictionary-matching based approaches.Comment: Accepted for ISBI 201
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.Comment: Accepted at MICCAI MLMIR 202
Deep Unrolling for Magnetic Resonance Fingerprinting
Magnetic Resonance Fingerprinting (MRF) has emerged as a promising
quantitative MR imaging approach. Deep learning methods have been proposed for
MRF and demonstrated improved performance over classical compressed sensing
algorithms. However many of these end-to-end models are physics-free, while
consistency of the predictions with respect to the physical forward model is
crucial for reliably solving inverse problems. To address this, recently [1]
proposed a proximal gradient descent framework that directly incorporates the
forward acquisition and Bloch dynamic models within an unrolled learning
mechanism. However, [1] only evaluated the unrolled model on synthetic data
using Cartesian sampling trajectories. In this paper, as a complementary to
[1], we investigate other choices of encoders to build the proximal neural
network, and evaluate the deep unrolling algorithm on real accelerated MRF
scans with non-Cartesian k-space sampling trajectories.Comment: Tech report. arXiv admin note: substantial text overlap with
arXiv:2006.1527
Only-Train-Once MR Fingerprinting for Magnetization Transfer Contrast Quantification
Magnetization transfer contrast magnetic resonance fingerprinting (MTC-MRF)
is a novel quantitative imaging technique that simultaneously measures several
tissue parameters of semisolid macromolecule and free bulk water. In this
study, we propose an Only-Train-Once MR fingerprinting (OTOM) framework that
estimates the free bulk water and MTC tissue parameters from MR fingerprints
regardless of MRF schedule, thereby avoiding time-consuming process such as
generation of training dataset and network training according to each MRF
schedule. A recurrent neural network is designed to cope with two types of
variants of MRF schedules: 1) various lengths and 2) various patterns.
Experiments on digital phantoms and in vivo data demonstrate that our approach
can achieve accurate quantification for the water and MTC parameters with
multiple MRF schedules. Moreover, the proposed method is in excellent agreement
with the conventional deep learning and fitting methods. The flexible OTOM
framework could be an efficient tissue quantification tool for various MRF
protocols.Comment: Accepted at 25th International Conference on Medical Image Computing
and Computer Assisted Intervention (MICCAI'22
Cram\'er-Rao bound-informed training of neural networks for quantitative MRI
Neural networks are increasingly used to estimate parameters in quantitative
MRI, in particular in magnetic resonance fingerprinting. Their advantages over
the gold standard non-linear least square fitting are their superior speed and
their immunity to the non-convexity of many fitting problems. We find, however,
that in heterogeneous parameter spaces, i.e. in spaces in which the variance of
the estimated parameters varies considerably, good performance is hard to
achieve and requires arduous tweaking of the loss function, hyper parameters,
and the distribution of the training data in parameter space. Here, we address
these issues with a theoretically well-founded loss function: the Cram\'er-Rao
bound (CRB) provides a theoretical lower bound for the variance of an unbiased
estimator and we propose to normalize the squared error with respective CRB.
With this normalization, we balance the contributions of hard-to-estimate and
not-so-hard-to-estimate parameters and areas in parameter space, and avoid a
dominance of the former in the overall training loss. Further, the CRB-based
loss function equals one for a maximally-efficient unbiased estimator, which we
consider the ideal estimator. Hence, the proposed CRB-based loss function
provides an absolute evaluation metric. We compare a network trained with the
CRB-based loss with a network trained with the commonly used means squared
error loss and demonstrate the advantages of the former in numerical, phantom,
and in vivo experiments.Comment: Xiaoxia Zhang, Quentin Duchemin, and Kangning Liu contributed equally
to this wor
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