论文标题
交替的深度低级别方法进行指数函数重建及其生物医学磁共振应用
Alternating Deep Low-Rank Approach for Exponential Function Reconstruction and Its Biomedical Magnetic Resonance Applications
论文作者
论文摘要
散采样可以加速信号获取，但要付出工件的代价。去除这些工件是信号处理中的一个基本问题，此任务也称为信号重建。通过将信号作为叠加指数函数进行建模，深度学习通过训练从底层采样指数的映射到完全采样的映射来实现快速和高保真的信号重建。但是，训练和目标数据之间的不匹配，例如底面采样，器官和成像对比度的采样率将严重损害重建。为了解决这个问题，我们提出了交替的深度低级别（ADLR），将深度学习求解器和经典优化求解器结合在一起。关于合成和现实的生物医学磁共振的重建实验表明，ADLR可以有效缓解不匹配问题并获得比最新方法更低的重建误差。
Undersampling can accelerate the signal acquisition but at the cost of bringing in artifacts. Removing these artifacts is a fundamental problem in signal processing and this task is also called signal reconstruction. Through modeling signals as the superimposed exponential functions, deep learning has achieved fast and high-fidelity signal reconstruction by training a mapping from the undersampled exponentials to the fully sampled ones. However, the mismatch, such as the sampling rate of undersampling, the organ and the contrast of imaging, between the training and target data will heavily compromise the reconstruction. To address this issue, we propose Alternating Deep Low-Rank (ADLR), which combines deep learning solvers and classic optimization solvers. Experiments on the reconstruction of synthetic and realistic biomedical magnetic resonance signals demonstrate that ADLR can effectively mitigate the mismatch issue and achieve lower reconstruction errors than state-of-the-art methods.
