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CUDA-Based Parallel Computation Framework for Phase Root Seeking Algorithm

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This paper presents a CUDA-based parallel computation framework for phase root seeking algorithm to estimate axial tissue displacement. Using this framework, initial displacement estimates are first computed for two frames of echo signals using two-dimensional cross-correlation method. The estimated axial displacement is then used as an initial guess for the two-dimensional phase root seeking. The first step of this framework makes the computation process of the two-dimensional phase root seeking method more data independent. Finally, the two-dimensional phase root seeking is then employed to compute the refined displacement. The experimental results illustrated that the proposed parallel phase root seeking strategy is capable of generating a fine displacement map. At the same time, it is also suitable for parallel computation on a graphics processing unit (GPU) platform. In comparison with the central processing unit (CPU)-based method and GPU-based coarse-grained parallel processing method, the proposed GPU-based parallel phase root seeking approach achieved a promising increase in speed. This work shows that the proposed parallel estimation strategy may generate fine elastograms and improve computational efficiency.
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Keywords: ARALLEL COMPUTATION; CROSS CORRELATION METHOD; GRAPHICS PROCESSING UNIT; PHASE ROOT SEEKING; ULTRASOUND ELASTOGRAPHY

Document Type: Research Article

Publication date: 2014-12-01

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  • Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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