Data augmentation for deep learning based accelerated MRI reconstruction with limited data
Data augmentation for deep learning based accelerated MRI reconstruction with limited data
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发表时间：
2021-06
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通讯作者：
Zalan Fabian;Reinhard Heckel;M. Soltanolkotabi
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作者：
Zalan Fabian;Reinhard Heckel;M. Soltanolkotabi
深度神经网络已经成为图像恢复和重建任务的非常成功的工具。这些网络通常经过端到端的训练，以直接从该图像的噪声或损坏的测量中重建图像。为了实现最先进的性能，在大型和多样化的图像集上进行训练被认为是至关重要的。然而，收集大量的训练图像通常是困难的和/或昂贵的。受数据增强（DA）分类问题的成功启发，在本文中，我们提出了一种用于加速MRI重建的数据增强管道，并研究了其在各种设置中减少所需训练数据的有效性。我们的DA管道MRAugment专门设计用于利用医学成像测量中存在的不变性，因为忽略问题物理学的幼稚DA策略失败了。通过对多个数据集的广泛研究，我们证明了在低数据状态下，DA可以防止过拟合，并且可以匹配甚至超越最先进的技术，同时使用更少的训练数据，而在高数据状态下，它具有递减的收益。此外，我们的研究结果表明，DA可以提高模型的鲁棒性对各种变化的测试分布。
Deep neural networks have emerged as very successful tools for image restoration and reconstruction tasks. These networks are often trained end-to-end to directly reconstruct an image from a noisy or corrupted measurement of that image. To achieve state-of-the-art performance, training on large and diverse sets of images is considered critical. However, it is often difficult and/or expensive to collect large amounts of training images. Inspired by the success of Data Augmentation (DA) for classification problems, in this paper, we propose a pipeline for data augmentation for accelerated MRI reconstruction and study its effectiveness at reducing the required training data in a variety of settings. Our DA pipeline, MRAugment, is specifically designed to utilize the invariances present in medical imaging measurements as naive DA strategies that neglect the physics of the problem fail. Through extensive studies on multiple datasets we demonstrate that in the low-data regime DA prevents overfitting and can match or even surpass the state of the art while using significantly fewer training data, whereas in the high-data regime it has diminishing returns. Furthermore, our findings show that DA can improve the robustness of the model against various shifts in the test distribution.