Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.
College of Medicine, Gachon University, Incheon, South Korea.
Med Phys. 2017 Dec;44(12):6209-6224. doi: 10.1002/mp.12600. Epub 2017 Oct 23.
To reconstruct MR images from subsampled data, we propose a fast reconstruction method using the multilayer perceptron (MLP) algorithm.
We applied MLP to reduce aliasing artifacts generated by subsampling in k-space. The MLP is learned from training data to map aliased input images into desired alias-free images. The input of the MLP is all voxels in the aliased lines of multichannel real and imaginary images from the subsampled k-space data, and the desired output is all voxels in the corresponding alias-free line of the root-sum-of-squares of multichannel images from fully sampled k-space data. Aliasing artifacts in an image reconstructed from subsampled data were reduced by line-by-line processing of the learned MLP architecture.
Reconstructed images from the proposed method are better than those from compared methods in terms of normalized root-mean-square error. The proposed method can be applied to image reconstruction for any k-space subsampling patterns in a phase encoding direction. Moreover, to further reduce the reconstruction time, it is easily implemented by parallel processing.
We have proposed a reconstruction method using machine learning to accelerate imaging time, which reconstructs high-quality images from subsampled k-space data. It shows flexibility in the use of k-space sampling patterns, and can reconstruct images in real time.
为了从欠采样数据中重建磁共振图像，我们提出了一种使用多层感知机（MLP）算法的快速重建方法。
我们应用 MLP 来减少 k 空间中欠采样产生的混叠伪影。MLP 通过从训练数据中学习，将混叠的输入图像映射到期望的无混叠图像。MLP 的输入是来自欠采样 k 空间数据的多通道实部和虚部的混叠线的所有体素，期望的输出是来自完全采样 k 空间数据的多通道图像的总和平方根的相应无混叠线的所有体素。通过学习的 MLP 架构逐行处理，减少了从欠采样数据重建图像中的混叠伪影。
与比较方法相比，所提出的方法重建的图像在归一化均方根误差方面更好。该方法可应用于任何相位编码方向的 k 空间欠采样模式的图像重建。此外，为了进一步减少重建时间，它可以通过并行处理轻松实现。
我们提出了一种使用机器学习加速成像时间的重建方法，该方法可以从欠采样的 k 空间数据中重建高质量的图像。它在 k 空间采样模式的使用上具有灵活性，可以实时重建图像。
