| 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 of monoenergetic CT image in dual-energy CT using kilovoltage CT with deep convolutional generative adversarial networks |
|
言語 |
en |
| 作成者 |
Kawahara, Daisuke
Ozawa, Shuichi
Kimura, Tomoki
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 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
deep learning |
| 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 |
image synthesis |
| 内容記述 |
|
|
内容記述 |
Purpose: To synthesize a dual-energy computed tomography (DECT) image from an equivalent kilovoltage computed tomography (kV-CT) image using a deep convolutional adversarial network. Methods: A total of 18,084 images of 28 patients are categorized into training and test datasets. Monoenergetic CT images at 40, 70, and 140 keV and equivalent kVCT images at 120 kVp are reconstructed via DECT and are defined as the reference images. An image prediction framework is created to generate monoenergetic computed tomography (CT) images from kV-CT images. The accuracy of the images generated by the CNN model is determined by evaluating the mean absolute error (MAE), mean square error (MSE), relative root mean square error (RMSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mutual information between the synthesized and reference monochromatic CT images. Moreover, the pixel values between the synthetic and reference images are measured and compared using a manually drawn region of interest (ROI). Results: The difference in the monoenergetic CT numbers of the ROIs between the synthetic and reference monoenergetic CT images is within the standard deviation values. The MAE, MSE, RMSE, and SSIM are the smallest for the image conversion of 120 kVp to 140 keV. The PSNR is the smallest and the MI is the largest for the synthetic 70 keV image. Conclusions: The proposed model can act as a suitable alternative to the existing methods for the reconstruction of monoenergetic CT images in DECT from single-energy CT images. |
|
言語 |
en |
| 出版者 |
|
|
出版者 |
Wiley Periodicals, Inc. |
| 出版者 |
|
|
出版者 |
American Association of Physicists in Medicine |
| 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 |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 関連情報 |
|
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|
識別子タイプ |
DOI |
|
|
関連識別子 |
10.1002/acm2.13190 |
| 関連情報 |
|
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|
識別子タイプ |
PMID |
|
|
関連識別子 |
33599386 |
| 関連情報 |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1002/acm2.13190 |
| 収録物識別子 |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
1526-9914 |
| 開始ページ |
|
|
開始ページ |
184 |
| 書誌情報 |
Journal of Applied Clinical Medical Physics
Journal of Applied Clinical Medical Physics
巻 22,
号 4,
p. 184-192,
発行日 2021-04-10
|
| 旧ID |
51051 |
JACMP_22_184.pdf (610.8 KB)
