论文标题
使用阻尼Denoising Vector AMP恢复MRI图像
MRI Image Recovery using Damped Denoising Vector AMP
论文作者
论文摘要
由磁共振成像（MRI）中的图像恢复的动机，我们提出了一种基于迭代称为深神经网络的线性逆问题的新方法，有时被称为插件恢复。我们的方法基于矢量近似消息传递（VAMP）算法，该算法以于点误差（MSE）在某些条件下的最佳恢复而闻名。但是，MRI中的远期操作员无法满足这些条件，因此我们设计了新的阻尼和初始化方案以帮助vamp。在从FastMRI数据库中恢复图像时，所得的DD-VAMP ++算法在收敛速度和准确性方面表现出胜过现有的算法，以实现笛卡尔采样的实际情况。
Motivated by image recovery in magnetic resonance imaging (MRI), we propose a new approach to solving linear inverse problems based on iteratively calling a deep neural-network, sometimes referred to as plug-and-play recovery. Our approach is based on the vector approximate message passing (VAMP) algorithm, which is known for mean-squared error (MSE)-optimal recovery under certain conditions. The forward operator in MRI, however, does not satisfy these conditions, and thus we design new damping and initialization schemes to help VAMP. The resulting DD-VAMP++ algorithm is shown to outperform existing algorithms in convergence speed and accuracy when recovering images from the fastMRI database for the practical case of Cartesian sampling.
