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Using a deep neural network for four-dimensional CT artifact reduction in image-guided radiotherapy
https://repo.qst.go.jp/records/76489
https://repo.qst.go.jp/records/764899441f038-d5c7-48dc-b192-cf0d6e5caf35
| Item type | 学術雑誌論文 / Journal Article(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2019-08-18 | |||||
| タイトル | ||||||
| タイトル | Using a deep neural network for four-dimensional CT artifact reduction in image-guided radiotherapy | |||||
| 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 | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Mori, Shinichiro
× Mori, Shinichiro× Hirai, Ryusuke× Sakata, Yukinobu× Mori, Shinichiro× Hirai, Ryusuke× Sakata, Yukinobu |
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| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | Introduction: Breathing artifact may affect the quality of four-dimensional computed tomography (4DCT) images. We developed a deep neural network (DNN)-based artifact reduction method. Methods: We used 857 thoracoabdominal 4DCT data sets scanned with 320-section CT with no 4DCT artifact within any volume (ground-truth image). The limitations of graphics processing unit (GPU) memory prevent importation of CT volume data into the DNN. To simulate 4DCT artifact, we interposed 4DCT images from other breathing phases at selected couch positions. Two DNNs, DNN1 and DNN2, were trained to maintain the quality of the output image to that of the ground truth by importing a single and 10 CT images, respectively. A third DNN consisting of an artifact classifier and image generator networks was added. The classifier network was based on residual networks and trained to detect CT section interposition-caused artifacts (artifact map). The generator network reduced artifacts by im- porting the coronal image data and the artifact map. Results: By repeating the 4DCT artifact reduction with coronal images, the geometrical accuracy in the sagittal sections could be improved, especially with DNN3. Diaphragm position was most accurate when DNN3 was applied. DNN2 corrected artifacts by using CT images from other phases, but DNN2 also modified artifact-free regions. Conclusions: Additional information related to the 4DCT artifact, including information from other respiratory phases (DNN2) and/or artifact regions (DNN3), provided substantial improvement over DNN1. Interposition- related artifacts were reduced by use of an artifact positional map (DNN3). |
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| 書誌情報 |
Physica Medica 巻 65, p. 67-75, 発行日 2019-08 |
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| 出版者 | ||||||
| 出版者 | Elsevier | |||||
| ISSN | ||||||
| 収録物識別子タイプ | ISSN | |||||
| 収録物識別子 | 1120-1797 | |||||
| DOI | ||||||
| 識別子タイプ | DOI | |||||
| 関連識別子 | 10.1016/j.ejmp.2019.08.008 | |||||
| 関連サイト | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | https://www.sciencedirect.com/science/article/abs/pii/S1120179719301887?dgcid=author | |||||
