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

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...

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Published in:Ultrasonic Imaging Vol. 39; no. 4; pp. 240 - 260
Main Authors: Wen, Tiexiang, Li, Ling, Zhu, Qingsong, Qin, Wenjian, Gu, Jia, Yang, Feng, Xie, Yaoqin
Format: research Journal Article
Published: Sage Publications Inc. Jul2017
Online Access:View this record in EBSCOhost
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        01617346
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      jtl: Ultrasonic Imaging
      issn: 01617346
      maglogo: Y
    pubinfo:
      dt: Jul2017
      vid: 39
      iid: 4
      pid: 344
      pub: Sage Publications Inc.
      place: Thousand Oaks, California
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        123856135
        123856135
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        123856135
        10.1177/0161734616689464
        NLM28627330
        123856135
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      formats:
      tig:
        atl: GPU-accelerated Kernel Regression Reconstruction for Freehand 3D Ultrasound Imaging.
      aug:
        au:
          Wen, Tiexiang
          Li, Ling
          Zhu, Qingsong
          Qin, Wenjian
          Gu, Jia
          Yang, Feng
          Xie, Yaoqin
        affil: 1 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, PR China
      sug:
        subj:
          Computer Graphics
          Ultrasonography Methods
          Image Processing, Computer Assisted Methods
          Imaging, Three-Dimensional Methods
          Algorithms
          Liver
          Phantoms, Imaging
      ab: 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.
      pubtype: Academic Journal
      doctype:
        research
        Journal Article
      ougenre: Article
    language: English
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