GPU-accelerated Kernel Regression Reconstruction for Freehand 3D Ultrasound Imaging.

Abstract:

: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 [Formula: see text] 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.

journal_name

Ultrason Imaging

journal_title

Ultrasonic imaging

authors

Wen T,Li L,Zhu Q,Qin W,Gu J,Yang F,Xie Y

doi

10.1177/0161734616689464

subject

Has Abstract

pub_date

2017-07-01 00:00:00

pages

240-259

issue

4

eissn

0161-7346

issn

1096-0910

journal_volume

39

pub_type

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