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.
翻译:松动的神经网络使得能够进行最先进的重建性能和加速磁共振成像(MRI)重建任务快速推断时间,然而,这些方法依赖于作为地面真象数据的全面抽样扫描,这种地面真象数据在许多临床成像应用中要么费用昂贵,要么不可能获得;因此,减少对数据的依赖是可取的。在这项工作中,我们提议建模无动性神经网络的准操作器,这些神经网络具有规模等同神经网络,以便提高数据效率和稳健性,使由于病人解剖或视场变化而在不同MRI扫描仪中产生的图像在规模上漂移。我们的方法表明,在同样的记忆限制下,在分配和分配范围外的图像的数据增强方面,在不大量增加火车或推论时间的情况下,对最新无动神经网络有了很大的改进。