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Conference Paper: A Novel Cross-Subject Transformer Denoising Method

TitleA Novel Cross-Subject Transformer Denoising Method
Authors
Issue Date5-Jun-2023
Abstract

In this work, we propose a new denoising method named Cross-Subject Transformer Denoising (CSTD), which transfers the texture of a reference image retrieved from a large database to the noisy image with soft attention mechanisms. The experiments on the fastMRI dataset with various noise levels show that our method is likely superior to many competing denoising algorithms including current the state-of-the-art NAFNet. Moreover, our method exhibits excellent generalizability when directly applied to in-vivo low-field data without retraining. Due to the flexibility, the method is expected to have a wide range of applications. 


Persistent Identifierhttp://hdl.handle.net/10722/337786

 

DC FieldValueLanguage
dc.contributor.authorHuang, S-
dc.contributor.authorLiu, S-
dc.contributor.authorMei, L-
dc.contributor.authorTang, C-
dc.contributor.authorWu, EX-
dc.contributor.authorLyu, M-
dc.date.accessioned2024-03-11T10:23:52Z-
dc.date.available2024-03-11T10:23:52Z-
dc.date.issued2023-06-05-
dc.identifier.urihttp://hdl.handle.net/10722/337786-
dc.description.abstract<p>In this work, we propose a new denoising method named Cross-Subject Transformer Denoising (CSTD), which transfers the texture of a reference image retrieved from a large database to the noisy image with soft attention mechanisms. The experiments on the fastMRI dataset with various noise levels show that our method is likely superior to many competing denoising algorithms including current the state-of-the-art NAFNet. Moreover, our method exhibits excellent generalizability when directly applied to in-vivo low-field data without retraining. Due to the flexibility, the method is expected to have a wide range of applications. <br></p>-
dc.languageeng-
dc.relation.ispartof2023 ISMRM & ISMRT Annual Meeting & Exhibition (03/06/2023-08/06/2023, Toronto)-
dc.titleA Novel Cross-Subject Transformer Denoising Method-
dc.typeConference_Paper-

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