| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2024-09-06 |
| タイトル |
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タイトル |
Deep neural network-based synthetic image digital fluoroscopy using digitally reconstructed tomography. |
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言語 |
en |
| 言語 |
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言語 |
eng |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 著者 |
Shinichiro Mori
Ryusuke Hirai
Yukinobu Sakata
Yasuhiko Tachibana
Masashi Koto
Hitoshi Ishikawa
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| 抄録 |
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内容記述タイプ |
Abstract |
|
内容記述 |
We developed a deep neural network (DNN) to generate X-ray flat panel detector (FPD) images from digitally reconstructed radiographic (DRR) images. FPD and treatment planning CT images were acquired from patients with prostate and head and neck (H&N) malignancies. The DNN parameters were optimized for FPD image synthesis. The synthetic FPD images' features were evaluated to compare to the corresponding ground-truth FPD images using mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). The image quality of the synthetic FPD image was also compared with that of the DRR image to understand the performance of our DNN. For the prostate cases, the MAE of the synthetic FPD image was improved (=?0.12?±?0.02) from that of the input DRR image (=?0.35?±?0.08). The synthetic FPD image showed higher PSNRs (=?16.81?±?1.54 dB) than those of the DRR image (=?8.74?±?1.56 dB), while SSIMs for both images (=?0.69) were almost the same. All metrics for the synthetic FPD images of the H&N cases were improved (MAE 0.08?±?0.03, PSNR 19.40?±?2.83 dB, and SSIM 0.80?±?0.04) compared to those for the DRR image (MAE 0.48?±?0.11, PSNR 5.74?±?1.63 dB, and SSIM 0.52?±?0.09). Our DNN successfully generated FPD images from DRR images. This technique would be useful to increase throughput when images from two different modalities are compared by visual inspection. |
| 書誌情報 |
Physical and engineering sciences in medicine
巻 46,
号 3,
p. 1227-1237,
発行日 2023-09
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| ISSN |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
2662-4737 |
| PubMed番号 |
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識別子タイプ |
PMID |
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関連識別子 |
37349631 |
