Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction

• URL: http://arxiv.org/abs/2204.10436v1
• Date: Thu, 21 Apr 2022 23:29:52 GMT
• Title: Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction
• Authors: Beliz Gunel, Arda Sahiner, Arjun D. Desai, Akshay S. Chaudhari, Shreyas Vasanawala, Mert Pilanci, John Pauly
• Abstract summary: We propose modeling the proximal operators of unrolled neural networks with scale-equivariant convolutional neural networks. Our approach demonstrates strong improvements over the state-of-the-art unrolled neural networks under the same memory constraints.
• Score: 33.82162420709648
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unrolled neural networks have enabled state-of-the-art reconstruction performance and fast inference times for the accelerated magnetic resonance imaging (MRI) reconstruction task. However, these approaches depend on fully-sampled scans as ground truth data which is either costly or not possible to acquire in many clinical medical imaging applications; hence, reducing dependence on data is desirable. In this work, we propose modeling the proximal operators of unrolled neural networks with scale-equivariant convolutional neural networks in order to improve the data-efficiency and robustness to drifts in scale of the images that might stem from the variability of patient anatomies or change in field-of-view across different MRI scanners. Our approach demonstrates strong improvements over the state-of-the-art unrolled neural networks under the same memory constraints both with and without data augmentations on both in-distribution and out-of-distribution scaled images without significantly increasing the train or inference time.

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