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UltrasOM:一个基于mamba的网络,用于使用光流进行3D自由手超声重建
Rui Sun1, Chuanba Liu2, Wenshuo Wang1
1Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300354, PR China.
Computer methods and programs in biomedicine
|May 14, 2025
概括
这项研究介绍了UltrasOM,这是一种新的3D超声波重建网络,可以在没有跟踪设备的情况下准确估计空间相对运动. 它显著减少了3D超声波成像中的错误,提高了诊断准确度.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 生物医学工程 生物医学工程
背景情况:
- 三维 (3D) 超声波 (US) 重建对于临床诊断至关重要,因为它具有安全性,便携性和实时功能.
- 自由手3D美国重建旨在避免跟踪设备,仅依赖图像数据.
- 固有的扫描动会导致当前方法中的不准确性和累积错误,导致重建卷中的文物.
研究的目的:
- 提出UltrasOM,3D超声波重建网络,旨在准确地估计空间相对运动.
- 克服现有方法在精确预测超声波图像框架的空间运动方面的局限性.
- 为了减少由于扫描不准确而导致的重建的3D US卷中的变形和工件.
主要方法:
- 开发了一个视频嵌入模块,将光流动力学与静态信息集成在一起,以增强运动功能.
- 实现了基于Mamba的时空注意模块,并堆叠了时空块,以捕捉全球的时空相关性.
- 嵌入的相关性损失和运动速度损失,以防止过拟合和增强模型通用化.
主要成果:
- 在200个前臂案例 (58,011) 中,UltrasOM实现了10.24%的最终漂移率 (FDR),7.34毫米的对距离误差 (DE),10.81毫米的对称豪斯多夫距离误差 (HD) 和2.05°的平均角度误差 (MEA).
- 优于最先进的方法的性能为13.24% (FDR),15.11% (DE),3.57% (HD) 和6.32% (MEA).
结论:
- 拟议的网络准确地预测了超声波图像框架之间的相对运动,而无需外部跟踪.
- 光流和时空依赖的整合提高了3D超声波重建的准确性.
- UltrasOM在准确度上超越了现有的方法,提供了改进的3D US体积重建.
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