방사선의학 웹진

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Abstract

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.​ 

Author information

Kwon K1, Kim D2, Park H1.

1Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.2College of Medicine, Gachon University, Incheon, South Korea.