| 洪楠.深度学习全模型迭代算法(AIIR)临床应用价值[J].中国医学影像技术,2025,41(4):513~514 |
| 深度学习全模型迭代算法(AIIR)临床应用价值 |
| Clinical value of artificial intelligence iterative reconstruction (AIIR) |
| 投稿时间：2025-02-19 修订日期：2025-02-19 |
| DOI：10.13929/j.issn.1003-3289.2025.04.001 |
| 中文关键词: 体层摄影术,X线计算机 图像处理,计算机辅助 深度学习 辐射剂量 图像质量 |
| 英文关键词:tomography, X-ray computed image processing, computer-assisted deep learning radiation dosage image quality |
| 基金项目: |
|
| 摘要点击次数: 711 |
| 全文下载次数: 146 |
| 中文摘要: |
| 深度学习全模型迭代算法(AIIR)创新性地将全模型迭代与深度学习技术相结合,以弥补传统CT重建方法在噪声伪影抑制及纹理表现等方面的局限而提高图像质量;其用于低剂量成像及复杂解剖结构成像表现不俗,可为患者安全及精准诊断提供有力保障。本文针对AIIR临床应用价值进行述评。 |
| 英文摘要: |
| Artificial intelligence iterative reconstruction (AIIR) innovatively combines model-based iterative reconstruction with deep learning technique, overcomes the limitations of noise suppression, artifacts reduction and texture preservation commonly encountered with conventional CT reconstruction methods, leading to a significant improvement of image quality. AIIR has shown remarkable performance in low-dose CT imaging and in depiction of complex anatomical structures, thereby ensuring patients safety and precise diagnosis. The clinical value of AIIR were briefly described in this article. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
