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.

中文翻译：

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用于手绘 3D 超声成像的 GPU 加速核回归重建

体积重建方法在提高手绘三维 (3D) 超声成像的重建体积图像质量方面起着重要作用。通过利用可编程图形处理单元（GPU）的能力，我们可以以每秒25-50帧（fps）的速度实现实时增量体积重建。在增量重建和可视化之后，在 GPU 上执行孔填充以填充剩余的空体素。然而，传统的基于像素最近邻的孔填充无法以高图像质量重建体积。相反，核回归为 3D 超声成像提供了一种准确的体积重建方法，但代价是计算复杂度高。在本文中，针对手绘超声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，核窗口大小为5×5×5、核带宽为1.0的参数设置可以提高散斑减少和细节保留的图像质量。所提出的基于 GPU 的方法的计算性能可以比中央处理器 (CPU) 快 200 倍以上，并且可以在 10 秒内重建我们实验中 5000 万体素大小的体积。