Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3539-3542. doi: 10.1109/EMBC46164.2021.9630853.
3D Ultrasound (US) contains rich spatial information which is helpful for medical diagnosis. However, current reconstruction methods with tracking devices are not suitable for clinical application. The sensorless freehand methods reconstruct based on US images which is less accuracy. In this paper, we proposed a network which reconstructs the US volume based on US images features and optical flow features. We proposed the pyramid warping layer which merges the image features and optical flow features with warping operation. To fuse the warped features of different scales in different pyramid levels, we adopted the fusion module using the attention mechanism. Meanwhile, we adopted the channel attention and spatial attention to our network. Our method was evaluated in 100 freehand US sweeps of human forearms which exhibits the efficient performance on volume reconstruction compared with other methods.
3D 超声（US）包含丰富的空间信息，有助于医学诊断。然而，带有跟踪设备的当前重建方法并不适合临床应用。无传感器的自由手方法基于 US 图像进行重建，准确性较低。在本文中，我们提出了一种基于 US 图像特征和光流特征重建 US 体数据的网络。我们提出了金字塔扭曲层，通过扭曲操作合并图像特征和光流特征。为了融合不同金字塔层中不同尺度的扭曲特征，我们采用了使用注意力机制的融合模块。同时，我们在网络中采用了通道注意力和空间注意力。我们的方法在 100 个人体前臂的自由手 US 扫描中进行了评估，与其他方法相比，在体积重建方面表现出了高效的性能。
