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アイテム
Noise reduction by multiple path neural network using Attention mechanisms with an emphasis on robustness against Errors: A pilot study on brain Diffusion-Weighted images
https://repo.qst.go.jp/records/2000910
https://repo.qst.go.jp/records/2000910eef91c22-0748-4cdd-bbc3-b9742298a05e
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||||||||||||
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| 公開日 | 2025-04-28 | |||||||||||||||||||
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| タイトル | Noise reduction by multiple path neural network using Attention mechanisms with an emphasis on robustness against Errors: A pilot study on brain Diffusion-Weighted images | |||||||||||||||||||
| 言語 | en | |||||||||||||||||||
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| 言語 | eng | |||||||||||||||||||
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| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||||||
| 資源タイプ | journal article | |||||||||||||||||||
| 著者 |
Tachibana Yasuhiko
× Tachibana Yasuhiko
× Yujiro Otsuka
× Hayato Nozaki
× Koji Kamagata
× Mori Shinichiro
× Yuya Saito
× Shigeki Aoki
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| 抄録 | ||||||||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||||||||
| 内容記述 | Purpose In deep learning-based noise reduction, larger networks offer advanced and complex functionality by utilizing its greater degree of freedom, but come with increased unpredictability, raising the potential risk of unforeseen errors. Here, we introduce a novel denoising model for diffusion-weighted images that intentionally limits the network output freedom by incorporating multiple pathways with varying degrees of freedom, with the aim of minimizing the chance of unintended alterations to the input. The purpose of this pilot study is to assess the model’s ability to perform effective denoising under the constraints. Methods Images from 10 healthy volunteers were used. Key innovations in our model development include: (1) neural network architecture that separated the function for calculating the specific output values from the function for adjusting the calculation for each pixel and (2) training that optimised the network based on both image and secondary obtained diffusion tensor. The generated images were compared with the original ones by measuring the deviation from ground truth images (averaged across eight acquisitions). Results The generated images demonstrated closer alignment with the ground truth images, both visually and statistically (Q < 0.05), compared to the original images. Furthermore, the advantage of the generated images over the original images was also found in the secondary obtained quantitative parameter maps with significance (Q < 0.05). Conclusion The usefulness of the proposed method was suggested because it was successful in improving both the quality of the generated images and accuracy of the major diffusion parameter maps under the given restrictions. |
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| 書誌情報 |
Physica Medica 巻 116, p. 103176, 発行日 2023-11 |
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| 出版者 | Elsevier | |||||||||||||||||||
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| 収録物識別子タイプ | ISSN | |||||||||||||||||||
| 収録物識別子 | 1120-1797 | |||||||||||||||||||
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| 識別子タイプ | PMID | |||||||||||||||||||
| 関連識別子 | 37989043 | |||||||||||||||||||
| DOI | ||||||||||||||||||||
| 識別子タイプ | DOI | |||||||||||||||||||
| 関連識別子 | 10.1016/j.ejmp.2023.103176 | |||||||||||||||||||
