A learning-based metal artifacts correction method for MRI using dual-polarity readout gradients and simulated data
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kwon, K. | - |
dc.contributor.author | Kim, D. | - |
dc.contributor.author | Park, H.W. | - |
dc.date.available | 2020-02-27T12:42:47Z | - |
dc.date.created | 2020-02-12 | - |
dc.date.issued | 2018 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/4297 | - |
dc.description.abstract | In MRI, metallic implants can generate magnetic field distortions and interfere in the spatial encoding of gradient magnetic fields. This results in image distortions, such as bulk shifts, pile-up and signal-loss artifacts. Three-dimensional spectral imaging methods can reduce the bulk shifts to a single-voxel level, but they still suffer from residual artifacts such as pile-up and signal-loss artifacts. Fully phase encoding methods suppress metal-induced artifacts, but they require impractically long imaging times. In this paper, we applied a deep learning method to correct metal artifacts. A neural network is proposed to map two distorted images obtained by dual-polarity readout gradients into a distortion-free image obtained by fully phase encoding. Simulated data were utilized to supplement and substitute real MR data for training the proposed network. Phantom experiments were performed to compare the quality of reconstructed images from several methods at high and low readout bandwidths. © Springer Nature Switzerland AG 2018. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Springer Verlag | - |
dc.relation.isPartOf | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.subject | Deep learning | - |
dc.subject | Encoding (symbols) | - |
dc.subject | Image coding | - |
dc.subject | Magnetic fields | - |
dc.subject | Medical computing | - |
dc.subject | Metals | - |
dc.subject | Piles | - |
dc.subject | Signal encoding | - |
dc.subject | Spectroscopy | - |
dc.subject | Correction method | - |
dc.subject | Gradient magnetic field | - |
dc.subject | Image distortions | - |
dc.subject | Magnetic field distortion | - |
dc.subject | Metallic implants | - |
dc.subject | Phantom experiment | - |
dc.subject | Phase encoding methods | - |
dc.subject | Quality of reconstructed images | - |
dc.subject | Medical imaging | - |
dc.title | A learning-based metal artifacts correction method for MRI using dual-polarity readout gradients and simulated data | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000477770600022 | - |
dc.identifier.doi | 10.1007/978-3-030-00928-1_22 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.11070 LNCS, pp.189 - 197 | - |
dc.identifier.scopusid | 2-s2.0-85054061330 | - |
dc.citation.endPage | 197 | - |
dc.citation.startPage | 189 | - |
dc.citation.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.volume | 11070 LNCS | - |
dc.contributor.affiliatedAuthor | Kim, D. | - |
dc.type.docType | Proceedings Paper | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Encoding (symbols) | - |
dc.subject.keywordPlus | Image coding | - |
dc.subject.keywordPlus | Magnetic fields | - |
dc.subject.keywordPlus | Medical computing | - |
dc.subject.keywordPlus | Metals | - |
dc.subject.keywordPlus | Piles | - |
dc.subject.keywordPlus | Signal encoding | - |
dc.subject.keywordPlus | Spectroscopy | - |
dc.subject.keywordPlus | Correction method | - |
dc.subject.keywordPlus | Gradient magnetic field | - |
dc.subject.keywordPlus | Image distortions | - |
dc.subject.keywordPlus | Magnetic field distortion | - |
dc.subject.keywordPlus | Metallic implants | - |
dc.subject.keywordPlus | Phantom experiment | - |
dc.subject.keywordPlus | Phase encoding methods | - |
dc.subject.keywordPlus | Quality of reconstructed images | - |
dc.subject.keywordPlus | Medical imaging | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.description.journalRegisteredClass | scopus | - |
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