Accurate Transmission-Less Attenuation Correction Method for Amyloid-beta Brain PET Using Deep Neural Network
DC Field | Value | Language |
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dc.contributor.author | Choi, Bo-Hye | - |
dc.contributor.author | Hwang, Donghwi | - |
dc.contributor.author | Kang, Seung-Kwan | - |
dc.contributor.author | Kim, Kyeong-Yun | - |
dc.contributor.author | Choi, Hongyoon | - |
dc.contributor.author | Seo, Seongho | - |
dc.contributor.author | Lee, Jae-Sung | - |
dc.date.accessioned | 2021-08-13T13:40:15Z | - |
dc.date.available | 2021-08-13T13:40:15Z | - |
dc.date.created | 2021-08-09 | - |
dc.date.issued | 2021-08 | - |
dc.identifier.issn | 2079-9292 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/81859 | - |
dc.description.abstract | The lack of physically measured attenuation maps (μ-maps) for attenuation and scatter correction is an important technical challenge in brain-dedicated stand-alone positron emission to-mography (PET) scanners. The accuracy of the calculated attenuation correction is limited by the nonuniformity of tissue composition due to pathologic conditions and the complex structure of fa-cial bones. The aim of this study is to develop an accurate transmission-less attenuation correction method for amyloid-β (Aβ) brain PET studies. We investigated the validity of a deep convolutional neural network trained to produce a CT-derived μ-map (μ-CT) from simultaneously reconstructed activity and attenuation maps using the MLAA (maximum likelihood reconstruction of activity and attenuation) algorithm for Aβ brain PET. The performance of three different structures of U-net models (2D, 2.5D, and 3D) were compared. The U-net models generated less noisy and more uniform μ-maps than MLAA μ-maps. Among the three different U-net models, the patch-based 3D U-net model reduced noise and cross-talk artifacts more effectively. The Dice similarity coefficients between the μ-map generated using 3D U-net and μ-CT in bone and air segments were 0.83 and 0.67. All three U-net models showed better voxel-wise correlation of the μ-maps compared to MLAA. The patch-based 3D U-net model was the best. While the uptake value of MLAA yielded a high percentage error of 20% or more, the uptake value of 3D U-nets yielded the lowest percentage error within 5%. The proposed deep learning approach that requires no transmission data, anatomic image, or atlas/template for PET attenuation correction remarkably enhanced the quantitative accuracy of the simultaneously estimated MLAA μ-maps from Aβ brain PET. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | ELECTRONICS | - |
dc.title | Accurate Transmission-Less Attenuation Correction Method for Amyloid-beta Brain PET Using Deep Neural Network | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000681826100001 | - |
dc.identifier.doi | 10.3390/electronics10151836 | - |
dc.identifier.bibliographicCitation | ELECTRONICS, v.10, no.15 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85111464355 | - |
dc.citation.title | ELECTRONICS | - |
dc.citation.volume | 10 | - |
dc.citation.number | 15 | - |
dc.contributor.affiliatedAuthor | Choi, Bo-Hye | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | attenuation correction | - |
dc.subject.keywordAuthor | Alzheimer’s disease | - |
dc.subject.keywordAuthor | Amyloid | - |
dc.subject.keywordAuthor | Convolutional neural network | - |
dc.subject.keywordAuthor | Positron emission tomography | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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