Department of Computer Engineering, German Jordanian University, Amman, Jordan.
Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany.
Med Phys. 2022 Aug;49(8):4999-5013. doi: 10.1002/mp.15725. Epub 2022 Jun 17.
Ultrasound is employed in needle interventions to visualize the anatomical structures and track the needle. Nevertheless, needle detection in ultrasound images is a difficult task, specifically at steep insertion angles.
A new method is presented to enable effective needle detection using ultrasound B-mode and power Doppler analyses.
A small buzzer is used to excite the needle and an ultrasound system is utilized to acquire B-mode and power Doppler images for the needle. The B-mode and power Doppler images are processed using Radon transform and local-phase analysis to initially detect the axis of the needle. The detection of the needle axis is improved by processing the power Doppler image using alpha shape analysis to define a region of interest (ROI) that contains the needle. Also, a set of feature maps is extracted from the ROI in the B-mode image. The feature maps are processed using a machine learning classifier to construct a likelihood image that visualizes the posterior needle likelihoods of the pixels. Radon transform is applied to the likelihood image to achieve an improved needle axis detection. Additionally, the region in the B-mode image surrounding the needle axis is analyzed to identify the needle tip using a custom-made probabilistic approach. Our method was utilized to detect needles inserted in ex vivo animal tissues at shallow [ ), moderate [ ), and steep [ ] angles.
Our method detected the needles with failure rates equal to 0% and mean angle, axis, and tip errors less than or equal to 0.7°, 0.6 mm, and 0.7 mm, respectively. Additionally, our method achieved favorable results compared to two recently introduced needle detection methods.
The results indicate the potential of applying our method to achieve effective needle detection in ultrasound images.
超声在针介入中用于可视化解剖结构并跟踪针。然而，在超声图像中检测针是一项困难的任务，特别是在陡峭的插入角度。
提出了一种新方法，以使用超声 B 模式和功率多普勒分析实现有效的针检测。
使用小蜂鸣器激发针，使用超声系统采集针的 B 模式和功率多普勒图像。使用 Radon 变换和局部相位分析处理 B 模式和功率多普勒图像，以初始检测针的轴。通过使用 alpha 形状分析处理功率多普勒图像来定义包含针的感兴趣区域（ROI），可以改善针轴的检测。此外，从 B 模式图像中的 ROI 中提取一组特征图。使用机器学习分类器处理特征图，以构建可视化像素后针概率的似然图像。将 Radon 变换应用于似然图像以实现改进的针轴检测。此外，分析围绕针轴的 B 模式图像区域，使用定制的概率方法识别针尖端。我们的方法用于检测在浅[），中[）和深[）角度插入离体动物组织中的针。
我们的方法以等于 0%的失败率检测到了针，平均角度、轴和尖端误差分别小于或等于 0.7°、0.6 毫米和 0.7 毫米。此外，与最近介绍的两种针检测方法相比，我们的方法取得了良好的结果。
结果表明，我们的方法有可能在超声图像中实现有效的针检测。
