| 李蕊,崔磊.深度学习CT图像迭代重建及其用于儿童CT进展[J].中国医学影像技术,2023,39(2):303~306 |
| 深度学习CT图像迭代重建及其用于儿童CT进展 |
| Progresses of CT image iterative reconstruction technique based on deep learning and applications in pediatric CT |
| 投稿时间：2022-06-25 修订日期：2022-09-03 |
| DOI：10.13929/j.issn.1003-3289.2023.02.036 |
| 中文关键词: 体层摄影术,X线计算机 深度学习 儿童 图像质量 |
| 英文关键词:tomography, X-ray computed deep learning child image quality |
| 基金项目: |
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| 摘要点击次数: 1859 |
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| 中文摘要: |
| 人工智能在分割、重建医学及图像处理等方面均发挥重要作用。儿童CT检查应遵循尽可能低辐射剂量原则，即在低辐射剂量下最大限度保持或获得更高图像质量。本文对基于人工智能的深度学习CT图像迭代重建技术及其用于儿童CT进展进行综述。 |
| 英文摘要: |
| Artificial intelligence plays an important role in segmentation, reconstruction and processing of medical imaging. Children's CT examination should follow the principle of low radiation dose as far as possible, that is to maintain or obtain higher image quality at low radiation dose. The progresses of CT image iterative reconstruction technique based on deep learning and applications in pediatric CT were reviewed in this article. |
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