| Item type |
デフォルトアイテムタイプ_（フル）(1) |
| 公開日 |
2023-03-18 |
| タイトル |
|
|
タイトル |
Image synthesis with deep convolutional generative adversarial networks for material decomposition in dual-energy CT from a kilovoltage CT |
|
言語 |
en |
| 作成者 |
Kawahara, Daisuke
Saito, Akito
Ozawa, Shuichi
Nagata, Yasushi
|
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 権利情報 |
|
|
権利情報 |
© 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| 権利情報 |
|
|
権利情報 |
This is not the published version. Please cite only the published version. この論文は出版社版ではありません。引用の際には出版社版をご確認、ご利用ください。 |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
Deep learning |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
Medical imaging |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
Artificial intelligence |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
Dual-energy CT |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
Material decomposition |
| 内容記述 |
|
|
内容記述 |
Generative Adversarial Networks (GANs) have been widely used and it is expected to use for the clinical examination and image. The objective of the current study was to synthesize material decomposition images of bone-water (bone(water)) and fat-water (fat(water)) reconstructed from dual-energy computed tomography (DECT) using an equivalent kilovoltage-CT (kV-CT) image and a deep conditional GAN. The effective atomic number images were reconstructed using DECT. We used 18,084 images of 28 patients divided into two datasets: the training data for the model included 16,146 images (20 patients) and the test data for evaluation included 1938 images (8 patients). Image prediction frameworks of the equivalent single energy CT images at 120 kVp to the effective atomic number images were created. The image-synthesis framework was based on a CNN with a generator and discriminator. The mean absolute error (MAE), relative mean square error (MSE), relative root mean square error (RMSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mutual information (MI) were evaluated. The Hounsfield unit (HU) difference between the synthesized and reference material decomposition images of bone(water) and fat(water) were within 5.3 HU and 20.3 HU, respectively. The average MAE, MSE, RMSE, SSIM, and MI of the synthesized and reference material decomposition of the bone(water) images were 0.8, 1.3, 0.9, 0.9, 55.3, and 0.8, respectively. The average MAE, MSE, RMSE, SSIM, and MI of the synthesized and reference material decomposition of the fat(water) images were 0.0, 0.0, 0.1, 0.9, 72.1, and 1.4, respectively. The proposed model can act as a suitable alternative to the existing methods for the reconstruction of material decomposition images of bone(water) and fat(water) reconstructed via DECT from kV-CT. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
Elsevier |
| 言語 |
|
|
言語 |
eng |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版タイプ |
|
|
出版タイプ |
AO |
|
出版タイプResource |
http://purl.org/coar/version/c_b1a7d7d4d402bcce |
| 関連情報 |
|
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|
識別子タイプ |
DOI |
|
|
関連識別子 |
10.1016/j.compbiomed.2020.104111 |
| 関連情報 |
|
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|
識別子タイプ |
PMID |
|
|
関連識別子 |
33279790 |
| 関連情報 |
|
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|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1016/j.compbiomed.2020.104111 |
| 収録物識別子 |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
0010-4825 |
| 開始ページ |
|
|
開始ページ |
104111 |
| 書誌情報 |
Computers in Biology and Medicine
Computers in Biology and Medicine
巻 128,
p. 104111,
発行日 2021-01
|
| 旧ID |
50458 |
| 備考 |
Post-print version/PDF may be used in an institutional repository after an embargo period of 12 months. |
ComputBiolMed_128_104111.pdf (962.8 KB)
