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Synthetic transmit aperture technique in medical ultrasound imaging implemented on a GPU

机译:在GPU上实现的医学超声成像中的合成传输孔径技术

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摘要

In the medical ultrasound imaging, the synthetic transmit aperture (STA) technique is very promising and has been a hot research topic. It is dynamically focused in both transmit and receive yielding an improvement in resolution. But this imaging technique sets high demands on processing capabilities and makes implementation of a full STA system very challenging and costly. Many attempts have been made to reduce the demands on the system making it a more realistic task to implement. In this paper we don't consider how to reduce the demands, but consider how to accelerate the processing speed of the system. The recent introduction of general-purpose graphic processing units (GPU) seems to be quite promising in this view, especially for the affordable programming complexity. In this paper we explain the main computational features of STA processing unit, trying to disclose the degree of parallelism in the operations. On the basis of the compute unified device architecture (CUDA) programming model and the extremely flexible structure of the Single Instruction Multiple Threads (SIMT) model, we show that the optimization of STA processing unit can be performed more efficiently. The input data is read from Matlab, the post-processing and display also use Matlab. Performance shows that, using a single NTVDIA GTX-650 GPU board, this amount to a speed up of more than a factor of 30 compared to a highly optimized beamformer running on our test workstation with a 3.20-GHz Intel Core-i5 processor.
机译:在医学超声成像中,合成传输孔径(STA)技术非常有前途,并且已经成为研究的热点。它动态地专注于发送和接收,从而提高了分辨率。但是,这种成像技术对处理能力提出了很高的要求,并且使完整的STA系统的实施非常具有挑战性且成本很高。已经进行了许多尝试来减少对系统的需求,从而使其成为更现实的任务。在本文中,我们不考虑如何减少需求,而是考虑如何加快系统的处理速度。从这种观点来看,最近推出的通用图形处理单元(GPU)似乎很有希望,尤其是对于负担得起的编程复杂性而言。在本文中,我们解释了STA处理单元的主要计算功能,试图揭示操作中的并行度。基于计算统一设备架构(CUDA)编程模型和单指令多线程(SIMT)模型的极其灵活的结构,我们表明STA处理单元的优化可以更有效地执行。输入数据从Matlab读取,后处理和显示也使用Matlab。性能显示,与在3.20 GHz Intel Core-i5处理器的测试工作站上运行的高度优化的波束形成器相比,使用单个NTVDIA GTX-650 GPU板可将速度提高30倍以上。

著录项

  • 来源
  • 会议地点 Beijing(CN)
  • 作者单位

    College of Precision Instrument Opto-electronics Engineering, Tianjin University, Key Laboratory of Opto-electronics Information and Technical Science (Tianjin University), Ministry of Education, Tianjin, China;

    College of Precision Instrument Opto-electronics Engineering, Tianjin University, Key Laboratory of Opto-electronics Information and Technical Science (Tianjin University), Ministry of Education, Tianjin, China;

    College of Precision Instrument Opto-electronics Engineering, Tianjin University, Key Laboratory of Opto-electronics Information and Technical Science (Tianjin University), Ministry of Education, Tianjin, China;

    College of Precision Instrument Opto-electronics Engineering, Tianjin University, Key Laboratory of Opto-electronics Information and Technical Science (Tianjin University), Ministry of Education, Tianjin, China;

    College of Precision Instrument Opto-electronics Engineering, Tianjin University, Key Laboratory of Opto-electronics Information and Technical Science (Tianjin University), Ministry of Education, Tianjin, China;

    College of Precision Instrument Opto-electronics Engineering, Tianjin University, Key Laboratory of Opto-electronics Information and Technical Science (Tianjin University), Ministry of Education, Tianjin, China;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    medical ultrasound imaging; synthetic transmit aperture; parallel processing; GPU; CUDA;

    机译:医学超声成像;合成发射孔径并行处理; GPU;卡达;

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