MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion
DC Field | Value | Language |
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dc.contributor.author | Chung, H. | - |
dc.contributor.author | Lee, E.S. | - |
dc.contributor.author | Ye, J.C. | - |
dc.date.accessioned | 2023-11-22T01:41:26Z | - |
dc.date.available | 2023-11-22T01:41:26Z | - |
dc.date.issued | 2023-04 | - |
dc.identifier.issn | 0278-0062 | - |
dc.identifier.issn | 1558-254X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/68631 | - |
dc.description.abstract | Patient scans from MRI often suffer from noise, which hampers the diagnostic capability of such images. As a method to mitigate such artifact, denoising is largely studied both within the medical imaging community and beyond the community as a general subject. However, recent deep neural network-based approaches mostly rely on the minimum mean squared error (MMSE) estimates, which tend to produce a blurred output. Moreover, such models suffer when deployed in real-world sitautions: out-of-distribution data, and complex noise distributions that deviate from the usual parametric noise models. In this work, we propose a new denoising method based on score-based reverse diffusion sampling, which overcomes all the aforementioned drawbacks. Our network, trained only with coronal knee scans, excels even on out-of-distribution <italic>in vivo</italic> liver MRI data, contaminated with complex mixture of noise. Even more, we propose a method to enhance the resolution of the denoised image with the <italic>same</italic> network. With extensive experiments, we show that our method establishes state-of-the-art performance, while having desirable properties which prior MMSE denoisers did not have: flexibly choosing the extent of denoising, and quantifying uncertainty. IEEE | - |
dc.format.extent | 1 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/TMI.2022.3220681 | - |
dc.identifier.bibliographicCitation | IEEE Transactions on Medical Imaging, v.42, no.4, pp 1 - 1 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.wosid | 000964765000003 | - |
dc.identifier.scopusid | 2-s2.0-85141598809 | - |
dc.citation.endPage | 1 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 1 | - |
dc.citation.title | IEEE Transactions on Medical Imaging | - |
dc.citation.volume | 42 | - |
dc.type.docType | Article | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Denoising | - |
dc.subject.keywordAuthor | Diffusion model | - |
dc.subject.keywordAuthor | Diffusion processes | - |
dc.subject.keywordAuthor | Magnetic resonance imaging | - |
dc.subject.keywordAuthor | Mathematical models | - |
dc.subject.keywordAuthor | MRI | - |
dc.subject.keywordAuthor | Noise measurement | - |
dc.subject.keywordAuthor | Noise reduction | - |
dc.subject.keywordAuthor | Numerical models | - |
dc.subject.keywordAuthor | Stochastic contraction | - |
dc.subject.keywordAuthor | Training | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Imaging Science & Photographic Technology | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Engineering, Biomedical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Imaging Science & Photographic Technology | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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