Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction

Zaccharie Ramzi; Jean-Luc Starck; Philippe Ciuciu

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arXiv · 2101.01570

Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction

Zaccharie Ramzi·Jean-Luc Starck·Philippe Ciuciu

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Abstract

Deep neural networks have recently been thoroughly investigated as a powerful tool for MRI reconstruction. There is a lack of research, however, regarding their use for a specific setting of MRI, namely non-Cartesian acquisitions. In this work, we introduce a novel kind of deep neural networks to tackle this problem, namely density compensated unrolled neural networks, which rely on Density Compensation to correct the uneven weighting of the k-space. We assess their efficiency on the publicly available fastMRI dataset, and perform a small ablation study. Our results show that the density-compensated unrolled neural networks outperform the different baselines, and that all parts of the design are needed. We also open source our code, in particular a Non-Uniform Fast Fourier transform for TensorFlow.

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eess.IV cs.CV cs.LG physics.med-ph stat.ML

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Zaccharie Ramzi, Jean-Luc Starck, Philippe Ciuciu. 2021-01-05. Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction. https://arxiv.org/abs/2101.01570

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