GPU-accelerated Kernel Regression Reconstruction for Freehand 3D Ultrasound Imaging
GPU-accelerated Kernel Regression Reconstruction for Freehand 3D Ultrasound Imaging
复制标题
用于徒手 3D 超声成像的 GPU 加速内核回归重建
DOI：
10.1177/0161734616689464
复制
发表时间：
2017-07-01
影响因子：
2.3
通讯作者：
Xie，Yaoqin
中科院分区：
文献类型：
--
作者：
Wen，Tiexiang;Li，Ling;Xie，Yaoqin
体重建方法在提高手绘三维超声成像重建体图像质量方面发挥着重要作用。利用可编程图形处理单元（GPU）的能力，我们可以实现25-50帧/秒（fps）的实时增量体重建。在增量重建和可视化之后，在GPU上进行补孔，填充剩余的空体素。然而，传统的基于像素最近邻的补孔方法无法实现高图像质量的体重构。相反，核回归为三维超声成像提供了一种精确的体积重建方法，但代价是计算复杂度高。针对徒手超声图像增量重建后的高质量体积，提出了一种基于gpu的快速核回归方法。实验结果表明，将核窗口大小设置为5 × 5 × 5，核带宽设置为1.0，可以获得更好的图像质量，以减少斑点和保留细节。该方法的计算速度比CPU快200倍以上，实验中5000万体素的体积可以在10秒内重建出来。
Volume reconstruction method plays an important role in improving reconstructed volumetric image quality for freehand three-dimensional (3D) ultrasound imaging. By utilizing the capability of programmable graphics processing unit (GPU), we can achieve a real-time incremental volume reconstruction at a speed of 25-50 frames per second (fps). After incremental reconstruction and visualization, hole-filling is performed on GPU to fill remaining empty voxels. However, traditional pixel nearest neighbor–based hole-filling fails to reconstruct volume with high image quality. On the contrary, the kernel regression provides an accurate volume reconstruction method for 3D ultrasound imaging but with the cost of heavy computational complexity. In this paper, a GPU-based fast kernel regression method is proposed for high-quality volume after the incremental reconstruction of freehand ultrasound. The experimental results show that improved image quality for speckle reduction and details preservation can be obtained with the parameter setting of kernel window size of 5 × 5 × 5 and kernel bandwidth of 1.0. The computational performance of the proposed GPU-based method can be over 200 times faster than that on central processing unit (CPU), and the volume with size of 50 million voxels in our experiment can be reconstructed within 10 seconds.