Wu Jiaze, Li Cheng, Huang Su, Liu Feng, Tan Bien Soo, Ooi London Lucien, Yu Haoyong, Liu Jimin
Singapore Bioimaging Consortium, Agency for Science, Technology and Research, #08-01, Matrix, 30 Biopolis Street, Singapore, 138671, Singapore,
Int J Comput Assist Radiol Surg. 2013 Nov;8(6):1027-35. doi: 10.1007/s11548-013-0902-y. Epub 2013 Jun 8.
In model-based respiratory motion estimation for the liver or other abdominal organs, the surrogate respiratory signal is usually obtained by using special tracking devices from skin or diaphragm, and subsequently applied to parameterize a 4D motion model for prediction or compensation. However, due to the intrinsic limits and economical costs of these tracking devices, the identification of the respiratory signal directly from intra-operative ultrasound images is a more attractive alternative.
We propose a fast and robust method to extract the respiratory motion of the liver from an intra-operative 2D ultrasound image sequence. Our method employs a preprocess to remove speckle-like noises in the ultrasound images and utilizes the normalized cross-correlation to measure the image similarity fast. More importantly, we present a novel adaptive search strategy, which makes full use of the inter-frame dependency of the image sequence. This search strategy narrows the search range of the optimal matching, thus greatly reduces the search time, and makes the matching process more robust and accurate.
The experimental results on four volunteers demonstrate that our method is able to extract the respiratory signal from an image sequence of 256 image frames in 5 s. The quantitative evaluation using the correlation coefficient reveals that the respiratory motion, extracted near the liver boundaries and vessels, is highly consistent with the reference motion tracked by an EM device.
Our method can use 2D ultrasound to track natural landmarks from the liver as surrogate respiratory signal and hence provide a feasible solution to replace special tracking devices.
在基于模型的肝脏或其他腹部器官呼吸运动估计中，通常通过使用特殊跟踪设备从皮肤或膈肌获取替代呼吸信号，随后将其用于参数化四维运动模型以进行预测或补偿。然而，由于这些跟踪设备的固有局限性和经济成本，直接从术中超声图像识别呼吸信号是一种更具吸引力的替代方法。
我们提出了一种快速且稳健的方法，用于从术中二维超声图像序列中提取肝脏的呼吸运动。我们的方法采用预处理来去除超声图像中的斑点状噪声，并利用归一化互相关快速测量图像相似度。更重要的是，我们提出了一种新颖的自适应搜索策略，该策略充分利用了图像序列的帧间依赖性。这种搜索策略缩小了最优匹配的搜索范围，从而大大减少了搜索时间，并使匹配过程更加稳健和准确。
对四名志愿者的实验结果表明，我们的方法能够在5秒内从包含256个图像帧的图像序列中提取呼吸信号。使用相关系数进行的定量评估表明，在肝脏边界和血管附近提取的呼吸运动与电磁设备跟踪的参考运动高度一致。
我们的方法可以使用二维超声跟踪肝脏的自然标志物作为替代呼吸信号，从而提供一种可行的解决方案来替代特殊跟踪设备。
