A parallel MR imaging method using multilayer perceptron
DC Field | Value | Language |
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dc.contributor.author | Kwon, Kinam | - |
dc.contributor.author | Kim, Dongchan | - |
dc.contributor.author | Park, HyunWook | - |
dc.date.available | 2020-02-27T16:41:35Z | - |
dc.date.created | 2020-02-06 | - |
dc.date.issued | 2017-12 | - |
dc.identifier.issn | 0094-2405 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/5390 | - |
dc.description.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. (c) 2017 American Association of Physicists in Medicine | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | WILEY | - |
dc.relation.isPartOf | MEDICAL PHYSICS | - |
dc.subject | DEEP NEURAL-NETWORKS | - |
dc.subject | RECONSTRUCTION | - |
dc.subject | SENSE | - |
dc.title | A parallel MR imaging method using multilayer perceptron | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000425379200012 | - |
dc.identifier.doi | 10.1002/mp.12600 | - |
dc.identifier.bibliographicCitation | MEDICAL PHYSICS, v.44, no.12, pp.6209 - 6224 | - |
dc.identifier.scopusid | 2-s2.0-85037810584 | - |
dc.citation.endPage | 6224 | - |
dc.citation.startPage | 6209 | - |
dc.citation.title | MEDICAL PHYSICS | - |
dc.citation.volume | 44 | - |
dc.citation.number | 12 | - |
dc.contributor.affiliatedAuthor | Kim, Dongchan | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | artificial neural networks (ANN) | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | magnetic resonance imaging (MRI) | - |
dc.subject.keywordAuthor | multilayer perceptron (MLP) | - |
dc.subject.keywordAuthor | parallel imaging | - |
dc.subject.keywordPlus | DEEP NEURAL-NETWORKS | - |
dc.subject.keywordPlus | RECONSTRUCTION | - |
dc.subject.keywordPlus | SENSE | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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