Next Article in Journal
Nonlinear Seismic Response of Multistory Steel Frames with Self-Centering Tension-Only Braces
Next Article in Special Issue
Development of Computer-Aided Semi-Automatic Diagnosis System for Chronic Post-Stroke Aphasia Classification with Temporal and Parietal Lesions: A Pilot Study
Previous Article in Journal
Numerical Evaluation of Dynamic Responses of Steel Frame Structures with Different Types of Haunch Connection Under Blast Load
Previous Article in Special Issue
A Novel Simplified Convolutional Neural Network Classification Algorithm of Motor Imagery EEG Signals Based on Deep Learning
Benchmark

Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets

1
CEA/NeuroSpin, Bât 145, F-91191 Gif-sur Yvette, France
2
Inria Saclay Ile-de-France, Parietal team, Univ. Paris-Saclay, 91120 Palaiseau, France
3
AIM, CEA, CNRS, Université Paris-Saclay, Université Paris Diderot, Sorbonne Paris Cité, F-91191 Gif-sur-Yvette, France
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(5), 1816; https://doi.org/10.3390/app10051816
Received: 8 February 2020 / Revised: 23 February 2020 / Accepted: 26 February 2020 / Published: 6 March 2020
(This article belongs to the Special Issue Signal Processing and Machine Learning for Biomedical Data)
Deep learning is starting to offer promising results for reconstruction in Magnetic Resonance Imaging (MRI). A lot of networks are being developed, but the comparisons remain hard because the frameworks used are not the same among studies, the networks are not properly re-trained, and the datasets used are not the same among comparisons. The recent release of a public dataset, fastMRI, consisting of raw k-space data, encouraged us to write a consistent benchmark of several deep neural networks for MR image reconstruction. This paper shows the results obtained for this benchmark, allowing to compare the networks, and links the open source implementation of all these networks in Keras. The main finding of this benchmark is that it is beneficial to perform more iterations between the image and the measurement spaces compared to having a deeper per-space network. View Full-Text
Keywords: image reconstruction; neural networks; deep learning; fastMRI; OASIS; MRI image reconstruction; neural networks; deep learning; fastMRI; OASIS; MRI
Show Figures

Figure 1

MDPI and ACS Style

Ramzi, Z.; Ciuciu, P.; Starck, J.-L. Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets. Appl. Sci. 2020, 10, 1816. https://doi.org/10.3390/app10051816

AMA Style

Ramzi Z, Ciuciu P, Starck J-L. Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets. Applied Sciences. 2020; 10(5):1816. https://doi.org/10.3390/app10051816

Chicago/Turabian Style

Ramzi, Zaccharie, Philippe Ciuciu, and Jean-Luc Starck. 2020. "Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets" Applied Sciences 10, no. 5: 1816. https://doi.org/10.3390/app10051816

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop