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图像重建使用深度学习用于近红外光学断层扫描:概括评估
Meret Ackermann1, Jingjing Jiang2, Emanuele Russomanno2
1Biomedical Optics Research Laboratory, Department of Neonatology, University Hospital Zurich, University of Zurich, Zurich, Switzerland. meret.ackermann@usz.ch.
Advances in experimental medicine and biology
|October 16, 2023
概括
一种混合深度学习模型显著改善了近红外光学断层扫描 (NIROT) 对于早产婴儿大脑氧化监测. 这一进步提高了检测缺氧缺血的速度和准确性,这对于及时干预至关重要.
科学领域:
- 生物医学光学 生物医学光学
- 医学成像医学成像
- 新生儿护理 新生儿护理
背景情况:
- 缺氧缺血症对早产婴儿构成严重威胁,需要快速诊断.
- 近红外光学断层扫描 (NIROT) 提供了监测大脑氧化量的潜力,但在高维数据处理方面面临挑战.
- 由于治疗窗口狭窄,及时干预至关重要.
研究的目的:
- 评估用于近红外光学断层扫描 (NIROT) 图像重建的混合深度学习模型.
- 用合成和幻影数据评估模型的性能和概括能力.
- 确定该模型在提高新生儿大脑氧化监测速度和准确性的有效性.
主要方法:
- 开发用于NIROT图像重建的混合卷积神经网络 (CNN).
- 使用合成数据训练CNN模型.
- 测试使用不同几何形状和源探测器排列的物理幻象进行概括测试,与训练数据不同.
主要成果:
- 混合CNN在重建速度方面取得了实质性的改进.
- 即使在不同的测量条件下,也可以实现更高的定位精度.
- 尽管使用了看不见的,非球形的包含形状和不同的表面拓,但仍然保持了高图像质量.
结论:
- 混合深度学习模型对于加速NIROT图像重建是有效的.
- 开发的CNN显示了各种新生儿脑成像场景的强大概括能力.
- 这种方法有望改善早产婴儿缺氧缺血的早期检测.
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