Huang J, Zhou G, Yu Z, Hu W
School of Mathematics and Computer Science, Gannan Normal University/Key Laboratory of Jiangxi Province for Numerical Simulation and Emulation Techniques/Ganzhou Key Laboratory of Computational Imaging, Ganzhou 341000, China.
Nan Fang Yi Ke Da Xue Xue Bao. 2022 Dec 20;42(12):1755-1764. doi: 10.12122/j.issn.1673-4254.2022.12.02.
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.
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.
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.
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.
提出一种基于深度学习的快速磁共振成像（MRI）重建新方法，该方法应用于并行MRI数据，并使用一种新的损失函数，该损失函数定义为幅度和相位的均方误差之和。
将多线圈图像数据合并为单线圈图像数据，以消除噪声之间的相关性，并在训练过程中用作标签。考虑到相位信息在某些应用中的重要性，在使用平方和方法合并多线圈数据时相位信息会丢失，因此引入了一种新的损失函数，定义为幅度和相位的均方误差（MSE）的加权和。损失函数中的单个权重用于平衡幅度和相位在不同应用中的重要性。为验证所提出的方法，使用FastMRI数据集中的真实脑部和膝盖数据进行训练和测试。我们还将该方法与另外两种使用MSE或平均绝对误差（MAE）作为损失函数的方法进行了比较。
实验结果表明，与其他两种方法相比，所提出的方法能够准确重建多线圈MR图像，且伪影明显减少。定量分析表明，所提出的方法使重建图像的峰值信噪比（PSNR）提高了约1 dB。
所提出的使用新损失函数来拟合并行MRI数据中的噪声的深度MRI重建方法可以加速MRI重建，并显著提高重建图像的质量。
