A parallel MR imaging method using multilayer perceptron

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.

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