Chen Xiankang, Wen Tiexiang, Li Xingmin, Qin Wenjian, Lan Donglai, Pan Weizhou, Gu Jia
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 518055 Shenzhen, China.
Biomed Eng Online. 2014 Aug 28;13:124. doi: 10.1186/1475-925X-13-124.
Freehand three-dimensional (3D) ultrasound has the advantages of flexibility for allowing clinicians to manipulate the ultrasound probe over the examined body surface with less constraint in comparison with other scanning protocols. Thus it is widely used in clinical diagnose and image-guided surgery. However, as the data scanning of freehand-style is subjective, the collected B-scan images are usually irregular and highly sparse. One of the key procedures in freehand ultrasound imaging system is the volume reconstruction, which plays an important role in improving the reconstructed image quality.
A novel freehand 3D ultrasound volume reconstruction method based on kernel regression model is proposed in this paper. Our method consists of two steps: bin-filling and regression. Firstly, the bin-filling step is used to map each pixel in the sampled B-scan images to its corresponding voxel in the reconstructed volume data. Secondly, the regression step is used to make the nonparametric estimation for the whole volume data from the previous sampled sparse data. The kernel penalizes distance away from the current approximation center within a local neighborhood.
To evaluate the quality and performance of our proposed kernel regression algorithm for freehand 3D ultrasound reconstruction, a phantom and an in-vivo liver organ of human subject are scanned with our freehand 3D ultrasound imaging system. Root mean square error (RMSE) is used for the quantitative evaluation. Both of the qualitative and quantitative experimental results demonstrate that our method can reconstruct image with less artifacts and higher quality.
The proposed kernel regression based reconstruction method is capable of constructing volume data with improved accuracy from irregularly sampled sparse data for freehand 3D ultrasound imaging system.
与其他扫描协议相比，徒手三维（3D）超声具有灵活性优势，使临床医生能够在被检查身体表面更自由地操作超声探头。因此，它在临床诊断和图像引导手术中被广泛应用。然而，由于徒手扫描数据具有主观性，采集到的B扫描图像通常不规则且高度稀疏。徒手超声成像系统的关键步骤之一是体积重建，这对提高重建图像质量起着重要作用。
本文提出了一种基于核回归模型的新型徒手三维超声体积重建方法。我们的方法包括两个步骤：装箱填充和回归。首先，装箱填充步骤用于将采样B扫描图像中的每个像素映射到重建体积数据中其对应的体素。其次，回归步骤用于根据先前采样的稀疏数据对整个体积数据进行非参数估计。核函数惩罚局部邻域内远离当前近似中心的距离。
为了评估我们提出的用于徒手三维超声重建的核回归算法的质量和性能，使用我们的徒手三维超声成像系统对一个体模和一名人体受试者的活体肝脏器官进行了扫描。均方根误差（RMSE）用于定量评估。定性和定量实验结果均表明，我们的方法能够重建出伪影更少、质量更高的图像。
所提出的基于核回归的重建方法能够从徒手三维超声成像系统的不规则采样稀疏数据中构建出精度更高的体积数据。
