Chatrasingh Maria, Suthakorn Jackrit
Department of Biomedical Engineering, Center for Biomedical and Robotics Technology (BART LAB), Faculty of Engineering, Mahidol University, Salaya, Thailand.
J Med Phys. 2019 Jul-Sep;44(3):191-200. doi: 10.4103/jmp.JMP_92_18.
Freehand ultrasound (US) is a technique used to acquire three-dimensional (3D) US images using a tracked 2D US probe. Calibrating the probe with a proper calibration phantom improves the precision of the technique and allows several applications in computer-assisted surgery. N-fiducial phantom is widely used due to the robustness of precise fabrication and convenience of use. In principle, the design supports single-frame calibration by providing at least three noncollinear points in 3D space at once. Due to this requirement, most designs contain multiple N-fiducials in unpatterned and noncollinear arrangements. The unpatterned multiple N-fiducials appearing as scattered dots in the US image are difficult to extract, and the extracted data are usually contaminated with noise. In practice, the extraction mostly relied on manual interventions, and calibration with N-fiducial phantom has not yet achieved high accuracy with single or few frame calibrations due to noise contamination.
In this article, we propose a novel design of the N-fiducial US calibration phantom to enable automatic feature extraction with comparable accuracy to multiple frame calibration.
Along with the design, the Random Sample Consensus (RANSAC) algorithm was used for feature extraction with both 2D and 3D models estimation. The RANSAC feature extraction algorithm was equipped with a closed-form calibration method to achieve automatic calibration.
The accuracy, precision, and shape reconstruction errors of the calibration acquired from the experiment were significantly matched with the previous literature reports.
The results showed that our proposed method has a high efficiency to perform automatic feature extraction compared to conventional extraction performed by humans.
徒手超声(US)是一种使用跟踪式二维超声探头获取三维(3D)超声图像的技术。使用合适的校准体模对探头进行校准可提高该技术的精度,并使其在计算机辅助手术中有多种应用。N型基准体模因其精确制造的稳健性和使用便利性而被广泛使用。原则上,该设计通过一次在三维空间中提供至少三个非共线点来支持单帧校准。由于这一要求,大多数设计包含多个呈无图案且非共线排列的N型基准。在超声图像中呈现为散点的无图案多个N型基准难以提取,并且提取的数据通常会被噪声污染。在实际操作中,提取大多依赖人工干预,并且由于噪声污染,使用N型基准体模进行校准在单帧或少数帧校准时尚未达到高精度。
在本文中,我们提出一种新型的N型基准超声校准体模设计,以实现具有与多帧校准相当精度水平的自动特征提取。
除了该设计外,随机抽样一致性(RANSAC)算法用于二维和三维模型估计的特征提取。RANSAC特征提取算法配备了一种闭式校准方法以实现自动校准。
从实验中获得的校准的准确性、精密度和形状重建误差与先前的文献报告显著匹配。
结果表明,与人工进行的传统提取相比,我们提出的方法在执行自动特征提取方面具有很高的效率。