Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation
DC Field | Value | Language |
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dc.contributor.author | Wang, Yan | - |
dc.contributor.author | Zhang, Pei | - |
dc.contributor.author | An, Le | - |
dc.contributor.author | Ma, Guangkai | - |
dc.contributor.author | Kang, Jiayin | - |
dc.contributor.author | Shi, Feng | - |
dc.contributor.author | Wu, Xi | - |
dc.contributor.author | Zhou, Jiliu | - |
dc.contributor.author | Lalush, David S. | - |
dc.contributor.author | Lin, Weili | - |
dc.contributor.author | Shen, Dinggang | - |
dc.date.accessioned | 2021-09-04T03:43:25Z | - |
dc.date.available | 2021-09-04T03:43:25Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2016-01-21 | - |
dc.identifier.issn | 0031-9155 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/89772 | - |
dc.description.abstract | Positron emission tomography (PET) has been widely used in clinical diagnosis for diseases and disorders. To obtain high-quality PET images requires a standard-dose radionuclide (tracer) injection into the human body, which inevitably increases risk of radiation exposure. One possible solution to this problem is to predict the standard-dose PET image from its low-dose counterpart and its corresponding multimodal magnetic resonance (MR) images. Inspired by the success of patch-based sparse representation (SR) in super-resolution image reconstruction, we propose a mapping-based SR (m-SR) framework for standard-dose PET image prediction. Compared with the conventional patch-based SR, our method uses a mapping strategy to ensure that the sparse coefficients, estimated from the multimodal MR images and low-dose PET image, can be applied directly to the prediction of standard-dose PET image. As the mapping between multimodal MR images (or low-dose PET image) and standard-dose PET images can be particularly complex, one step of mapping is often insufficient. To this end, an incremental refinement framework is therefore proposed. Specifically, the predicted standard-dose PET image is further mapped to the target standard-dose PET image, and then the SR is performed again to predict a new standard-dose PET image. This procedure can be repeated for prediction refinement of the iterations. Also, a patch selection based dictionary construction method is further used to speed up the prediction process. The proposed method is validated on a human brain dataset. The experimental results show that our method can outperform benchmark methods in both qualitative and quantitative measures. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | IOP PUBLISHING LTD | - |
dc.subject | ATTENUATION CORRECTION | - |
dc.subject | INCIDENTAL FINDINGS | - |
dc.subject | BRAIN | - |
dc.subject | CLASSIFICATION | - |
dc.subject | RECONSTRUCTION | - |
dc.subject | SEGMENTATION | - |
dc.subject | SELECTION | - |
dc.subject | ACCURACY | - |
dc.subject | PET/MRI | - |
dc.title | Predicting standard-dose PET image from low-dose PET and multimodal MR images using mapping-based sparse representation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Shen, Dinggang | - |
dc.identifier.doi | 10.1088/0031-9155/61/2/791 | - |
dc.identifier.scopusid | 2-s2.0-84955514942 | - |
dc.identifier.wosid | 000369516600025 | - |
dc.identifier.bibliographicCitation | PHYSICS IN MEDICINE AND BIOLOGY, v.61, no.2, pp.791 - 812 | - |
dc.relation.isPartOf | PHYSICS IN MEDICINE AND BIOLOGY | - |
dc.citation.title | PHYSICS IN MEDICINE AND BIOLOGY | - |
dc.citation.volume | 61 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 791 | - |
dc.citation.endPage | 812 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Engineering, Biomedical | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.subject.keywordPlus | ATTENUATION CORRECTION | - |
dc.subject.keywordPlus | INCIDENTAL FINDINGS | - |
dc.subject.keywordPlus | BRAIN | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | RECONSTRUCTION | - |
dc.subject.keywordPlus | SEGMENTATION | - |
dc.subject.keywordPlus | SELECTION | - |
dc.subject.keywordPlus | ACCURACY | - |
dc.subject.keywordPlus | PET/MRI | - |
dc.subject.keywordAuthor | positron emission tomography (PET) | - |
dc.subject.keywordAuthor | sparse representation | - |
dc.subject.keywordAuthor | mapping-based sparse representation | - |
dc.subject.keywordAuthor | incremental refinement | - |
dc.subject.keywordAuthor | standard-dose PET prediction | - |
dc.subject.keywordAuthor | multimodal MR images | - |
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