论文标题
从噪声中学习扩张的广泛激活网络，用于降级动脉自旋标记（ASL）灌注图像
A Learning-from-noise Dilated Wide Activation Network for denoising Arterial Spin Labeling (ASL) Perfusion Images
论文作者
论文摘要
动脉自旋标记（ASL）灌注MRI提供了一种量化脑血流（CBF）的非侵入性方法，但仍然患有低信噪比（SNR）。使用深度机器学习（DL），几个小组表现出令人鼓舞的降解结果。有趣的是，由于缺乏黄金标准的高质量ASL CBF图像，使用噪声污染的替代参考训练深神经网络时获得了改进。更引人注目的是，这些DL ASL网络（ASLDN）的输出显示出比替代参考更高的SNR。这种现象表明，深度网络对ASL CBF图像denoisising的学习能力，可以通过网络优化进一步增强。在这项研究中，我们提出了一个新的ASLDN，以测试在高度嘈杂的训练参考的情况下，是否可以实现相似甚至更好的ASL CBF图像质量。进行了不同的实验以验证从噪声假设进行学习。结果表明，与以相对较高的SNR参考训练的ASLDN相比，学习过程中的学习策略产生了更好的输出质量。
Arterial spin labeling (ASL) perfusion MRI provides a non-invasive way to quantify cerebral blood flow (CBF) but it still suffers from a low signal-to-noise-ratio (SNR). Using deep machine learning (DL), several groups have shown encouraging denoising results. Interestingly, the improvement was obtained when the deep neural network was trained using noise-contaminated surrogate reference because of the lack of golden standard high quality ASL CBF images. More strikingly, the output of these DL ASL networks (ASLDN) showed even higher SNR than the surrogate reference. This phenomenon indicates a learning-from-noise capability of deep networks for ASL CBF image denoising, which can be further enhanced by network optimization. In this study, we proposed a new ASLDN to test whether similar or even better ASL CBF image quality can be achieved in the case of highly noisy training reference. Different experiments were performed to validate the learning-from-noise hypothesis. The results showed that the learning-from-noise strategy produced better output quality than ASLDN trained with relatively high SNR reference.
