medical

Magnetic Resonance Fingerprinting

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Spatially Regularized Parametric Map Reconstruction for Fast Magnetic Resonance Fingerprinting

M. Reyes, F. Balsiger, Alain Jungo, O. Scheidegger, P. Carlier, B. Marty•Fri Nov 08 2019

A convolutional neural network-based MRF reconstruction is proposed, 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.

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Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations

Dongdong Chen, M. Davies, Mohammad Golbabaee•Fri Jun 26 2020

The proposed PGD-Net is a learned proximal gradient descent framework that directly incorporates the forward acquisition and Bloch dynamic models within a recurrent learning mechanism, that adopts a compact neural proximal model for de-aliasing and quantitative inference.

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Learning Bloch Simulations for MR Fingerprinting by Invertible Neural Networks

M. Reyes, F. Balsiger, Alain Jungo, O. Scheidegger, B. Marty•Sun Aug 09 2020

N-based MRF reconstruction is revisited 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).

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

C. Poon, Mohammad Golbabaee•Sun Nov 22 2020

An off-the-grid approach equipped with an extended notion of the sparse group Lasso regularisation for sparse approximation using continuous (non-discretised) Bloch response models is proposed, enabling efficient back-propagation of the gradients through the proposed algorithm.

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Cramér–Rao bound‐informed training of neural networks for quantitative MRI

Kangning Liu, C. Fernandez‐Granda, Xiaoxia Zhang, Quentin Duchemin, Sebastian Flassbeck, Cem Gultekin, Jakob Assländer•Tue Sep 21 2021

To improve the performance of neural networks for parameter estimation in quantitative MRI, in particular when the noise propagation varies throughout the space of biophysical parameters.

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

Mohammad Golbabaee, Ketan Fatania, Carolin M. Pirkl, M. Menzel, Peter Hall•Wed Feb 09 2022

An iterative deep learning plug-and-play reconstruction approach to MRF which is adaptive to the forward acquisition process and shows consistent dealiasing performance against both acquisition schemes and accurate mapping of tissues’ quantitative bio-properties.

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