基于卷积神经网络的欠采样脑部核磁共振图像重建方法

针对目前基于深度学习的欠采样磁共振（MR）图像重建方法都是基于单个切片的重建而忽略相邻切片间的数据冗余的问题，提出一种用于欠采样的多切片脑部MR图像重建的混合级联卷积神经网络（HC-CNN）。首先，将传统的重建方法拓展为基于深度学习的重建模型，并使用级联卷积神经网络来代替传统的迭代重建框架。然后，在每次迭代重建中，分别使用3D卷积模块和2D卷积模块来学习脑部MR图像序列中存在的相邻切片间与单幅切片内部的数据冗余。最后，在每次迭代中使用数据一致性（DC）模块来保持重建图像在k-空间的数据保真度。在单线圈脑部MR图像数据集上的仿真实验结果显示，相较于基于单幅MR图像的重建方法，所提方法在4倍加速因子下的峰值信噪比（PSNR）值平均提升了1.75 dB，在6倍降采样因子下的PSNR值平均提升了2.57 dB，而且该方法的单张图像重建平均用时为15.4 ms。实验结果表明：所提方法不仅能够有效利用切片间的数据冗余并重建出更高质量的图像，而且具有较高的实时性。

Undersampled brain magnetic resonance image reconstruction method based on convolutional neural network

Aiming at the problem that current deep learning based undersampled Magnetic Resonance (MR) image reconstruction methods mainly focus on the single slice reconstruction and ignore the data redundancy between adjacent slices, a Hybrid Cascaded Convolutional Neural Network (HC-CNN) was proposed for undersampled multi-slice brain MR image reconstruction. First, the traditional reconstruction method was extended to a deep learning based reconstruction model, and the traditional iterative reconstruction framework was replaced by a cascaded convolutional neural network. Then, in each iterative reconstruction, a 3D convolution module and a 2D convolution module were used to learn the data redundancy between adjacent slices and inside a single slice, respectively. Finally, Data Consistency (DC) module was used in each iteration to maintain the data fidelity of the reconstructed image in k-space. The simulation results on a single-coil brain MR image dataset show that compared with the reconstruction methods based on single slice reconstruction, the proposed method has the Peak Signal-to-Noise Ratio (PSNR) value at 4×acceleration factor increased by 1.75 dB averagely and the PSNR value at 6×acceleration factor increased by 2.57 dB averagely. At the same time, the image reconstruction time for a single slice by the proposed method is 15.4 ms. Experimental results show that the proposed method can not only effectively utilize the data redundancy between slices and reconstruct higher-quality images, but also has a higher real-time performance.