Magnetic Resonance Fingerprinting

6 papers with code • 0 benchmarks • 0 datasets

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Most implemented papers

Spatially Regularized Parametric Map Reconstruction for Fast Magnetic Resonance Fingerprinting

fabianbalsiger/mrf-reconstruction-media2020 9 Nov 2019

Here, we propose a convolutional neural network-based reconstruction, which enables both accurate and fast reconstruction of parametric maps, and is adaptable based on the needs of spatial regularization and the capacity for the reconstruction.

Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations

edongdongchen/PGD-Net 27 Jun 2020

Consistency of the predictions with respect to the physical forward model is pivotal for reliably solving inverse problems.

Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks

fabianbalsiger/mrf-reconstruction-mlmir2020 10 Aug 2020

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).

An off-the-grid approach to multi-compartment magnetic resonance fingerprinting

mgolbabaee/SGB-Lasso-for-partial-volume-quantitative-MRI 23 Nov 2020

We propose a novel numerical approach to separate multiple tissue compartments in image voxels and to estimate quantitatively their nuclear magnetic resonance (NMR) properties and mixture fractions, given magnetic resonance fingerprinting (MRF) measurements.

Cramér-Rao bound-informed training of neural networks for quantitative MRI

quentin-duchemin/mrf-crbloss 22 Sep 2021

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.

A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoisers

ketanfatania/qmri-pnp-recon-poc 10 Feb 2022

This paper proposes an iterative deep learning plug-and-play reconstruction approach to MRF which is adaptive to the forward acquisition process.