Multi-contrast MR image denoising for parallel imaging using multilayer perceptron

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For clinical diagnosis in MRI, multiple examinations are commonly performed to acquire various contrast images. This article presents a learning-based denoising method for parallel imaging to enhance the quality of multi-contrast images so that the imaging time can be accelerated highly. Multi-contrast images share structural information and coil geometry. The proposed method adopts the multilayer perceptron (MLP) model to save the sharable and redundant information among the multi-contrast images. The images are divided into patches, which are used as the input and output of MLP. A geometry factor map is additionally used to provide noise amplification information of the accelerated MR images. Computer simulation demonstrates that the use of multi-contrast images and geometry factor contributes to the quality of the reconstructed images. The proposed method reconstructs high-quality images without impairing details from the subsampled intermediate images, and it shows better results than previous denoising methods
Publisher
WILEY-BLACKWELL
Issue Date
2016-03
Language
English
Article Type
Article
Keywords

NEURAL-NETWORKS; RECONSTRUCTION; GRAPPA

Citation

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, v.26, no.1, pp.65 - 75

ISSN
0899-9457
DOI
10.1002/ima.22158
URI
http://hdl.handle.net/10203/209041
Appears in Collection
EE-Journal Papers(저널논문)
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