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Multi‐contrast MR image denoising for parallel imaging using multilayer perceptron
Author(s) -
Kwon Kinam,
Kim Dongchan,
Park HyunWook
Publication year - 2016
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22158
Subject(s) - contrast (vision) , computer science , artificial intelligence , noise reduction , computer vision , multilayer perceptron , pattern recognition (psychology) , noise (video) , image quality , image (mathematics) , artificial neural network
ABSTRACT 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.