一个深度转移学习框架用于全环光声学断层扫描中的音速偏差校正
Jie Yin1, Yingjie Feng2, Qi Feng3
1School of Electrical and Control Engineering, Nanjing Polytechnic Institute, Nanjing 210048, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究提出了一个深度学习框架,用于纠正由声速 (SoS) 变化引起的光声断层扫描 (PAT) 中的工件. 该方法显著提高了图像质量,并减少了全环PAT重建中的文物.
科学领域:
- 医疗成像医学成像
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 音速 (SoS) 变化导致全环光声学断层扫描 (PAT) 中的显著工件,限制了其临床应用.
- 文物降低了图像的准确性,并阻碍了可靠的解释PAT重建.
研究的目的:
- 开发和评估一个深度神经框架,用于PAT的端到端的文物校正.
- 提高受SoS异质影响的全视图PAT重建的准确性和质量.
主要方法:
- 设计了一个基于转移学习的深度神经框架,将ResNet-50编码器与解卷式解码器结合起来.
- 采用了两阶段的课程学习策略:对统一的SoS不匹配进行预训练和对异质的SoS领域进行微调.
- 该框架使用数值模型,物理幻影和体内实验来验证.
主要成果:
- 拟议的框架显示了与传统的反向投影和U-Net基线相比的实质性改进.
- 关键指标,如平均平方误差,结构相似度指数和皮尔森相关系数,显示出显著的增长.
- 该方法实现了每17毫秒的快速平均推断时间.
结论:
- 深度学习框架有效地降低了全环PAT对SoS不均性的敏感性.
- 该方法显著提高了全视图重建质量,为临床PAT应用提供了有前途的解决方案.
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