Benchmarking Deep Nets MRI Reconstruction Models on the FastMRI Publicly Available Dataset

Zaccharie Ramzi (1, 2, 3) , Philippe Ciuciu (2, 1) , Jean-Luc Starck (3, 4)

1 NEUROSPIN - Service NEUROSPIN
2 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
3 LCS - COSMOSTAT - Laboratoire de Cosmologie et Statistiques
4 AIM (UMR_7158 / UMR_E_9005 / UM_112) - Astrophysique Interprétation Modélisation

The MRI reconstruction field lacked a proper data set that allowed for reproducible results on real raw data (i.e. complex-valued), especially when it comes to deep learning (DL) methods as this kind of approaches require much more data than classical Compressed Sensing (CS) reconstruction. This lack is now filled by the fastMRI data set, and it is needed to evaluate recent DL models on this benchmark. Besides, these networks are written in different frameworks and repositories (if publicly available), it is therefore needed to have a common tool, publicly available, allowing a reproducible benchmark of the different methods and ease of building new models. We provide such a tool that allows the benchmark of different reconstruction deep learning models.

Mots clés: MRI reconstruction; Deep learning; fastMRI

Domaines: Traitement du signal et de l'image [eess.SP]; Intelligence artificielle [cs.AI]; Imagerie

Zaccharie Ramzi, Philippe Ciuciu, Jean-Luc Starck. Benchmarking Deep Nets MRI Reconstruction Models on the FastMRI Publicly Available Dataset. ISBI 2020 - International Symposium on Biomedical Imaging, Apr 2020, Iowa City, United States. ⟨hal-02436223⟩