基于复值损失函数的并行MRI的深度重建

Deep parallel MRI reconstruction based on a complex-valued loss function

目的研究一种新的以幅值和相位均方误差和为损失函数的深度重建方法,用以加快磁共振图像重建速度及提高重建精度。方法考虑到磁共振不同线圈之间的数据噪声并不完全独立,本文首先将多线圈数据合并成单线圈幅值数据,以消除不同线圈噪声间的相关性,并以此作为训练数据的样本标签;同时考虑到相位信息的重要性,我们将损失函数定义为幅值和相位均方误差的加权和,权重系数用来平衡两者在不同应用中的重要程度。为了验证方法的有效性,本文利用FastMRI数据集中真实的脑部和膝部K空间数据进行训练和测试,并与一些以均方误差或平均绝对误差作为损失函数的相关方法进行了比较。结果所提方法能够有效去除噪声及重叠伪影,同时较好地保留图像细节。定量结果显示,相比于已有的以均方误差或平均绝对误差作为损失函数的方法,本文提出的方法可以提高重建图像的峰值信噪比(PSNR)1dB左右。结论本文针对深度磁共振重建提出了一种新的损失函数,以适应并行磁共振数据中的噪声特点,利用该方法可以加快重建速度,同时可以有效地提高重建质量。

Objective To propose a new method for fast MRI reconstruction based on deep learning in parallel MRI data using a new loss function defined as the summation of the mean squared errors of the magnitude and phase.Methods The multicoil image data were combined into single-coil image data to eliminate the correlation between noises and used as a label in the training process.Considering the importance of the phase information in some applications,where the phase information was lost when combining multicoil data using sum of square method,a new loss function was introduced,defined as the weighted sum of the mean squared error(MSE)of the magnitude and phase.The single weight in the loss function was used to balance the importance of the magnitude and phase in different applications.To validate the proposed method,real brain and knee data in FastMRI dataset were used for training and testing.We also compared this proposed method with two other methods that used MSE or mean absolute error(MAE)as a loss function.Results The experimental results showed that the proposed method was capable of accurate reconstruction of multicoil MR images with significantly reduced artifacts compared with the other two methods.Quantitative analysis showed that the propose method increased the peak signal-to-noise ratio(PSNR)of the reconstructed images by about 1 dB.Conclusion The proposed deep MRI reconstruction method using a new loss function to fit the noise in parallel MRI data can accelerate MRI reconstruction and significantly improve the quality of the reconstructed images.