A parallel MR imaging method using multilayer perceptron
A parallel MR imaging method using multilayer perceptron
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DOI：
10.1002/mp.12600
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发表时间：
2017-12-01
期刊：
影响因子：
3.8
通讯作者：
Park, HyunWook
中科院分区：
文献类型：
--
作者：
Kwon, Kinam;Kim, Dongchan;Park, HyunWook
目的：为了从子采样数据重建 MR 图像，我们提出了一种使用多层感知器 (MLP) 算法的快速重建方法。方法和材料：我们应用 MLP 来减少 k 空间中子采样产生的混叠伪影。 MLP 是从训练数据中学习的，以将有锯齿的输入图像映射到所需的无锯齿图像。 MLP的输入是来自子采样k空间数据的多通道实部和虚部图像的锯齿线中的所有体素，并且期望的输出是来自完全采样的k空间数据的多通道图像的平方根和的相应无锯齿线中的所有体素。通过对学习的 MLP 架构进行逐行处理，可以减少从子采样数据重建的图像中的混叠伪影。结果：在归一化均方根误差方面，所提出的方法重建的图像优于比较方法的重建图像。所提出的方法可以应用于相位编码方向上任何k空间子采样模式的图像重建。此外，为了进一步减少重建时间，可以通过并行处理轻松实现。结论：我们提出了一种利用机器学习来加速成像时间的重建方法，该方法从子采样的k空间数据重建高质量图像。它显示了 k 空间采样模式使用的灵活性，并且可以实时重建图像。 (c) 2017 年美国医学物理学家协会
Purpose: To reconstruct MR images from subsampled data, we propose a fast reconstruction method using the multilayer perceptron (MLP) algorithm.Methods and materials: 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.Results: 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.Conclusion: 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. (c) 2017 American Association of Physicists in Medicine