4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies"

Research Article

Hardware Accelerated Rician Denoise Algorithm for High Performance Magnetic Resonance Imaging

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  • @INPROCEEDINGS{10.4108/icst.mobihealth.2014.257361,
        author={Efstathios Sotiriou-Xanthopoulos and Sotirios Xydis and Kostas Siozios and George Economakos and Dimitrios Soudris},
        title={Hardware Accelerated Rician Denoise Algorithm for High Performance Magnetic Resonance Imaging},
        proceedings={4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies"},
        publisher={IEEE},
        proceedings_a={MOBIHEALTH},
        year={2014},
        month={12},
        keywords={magnetic resonance imaging rician denoise hardware acceleration high performance},
        doi={10.4108/icst.mobihealth.2014.257361}
    }
    
  • Efstathios Sotiriou-Xanthopoulos
    Sotirios Xydis
    Kostas Siozios
    George Economakos
    Dimitrios Soudris
    Year: 2014
    Hardware Accelerated Rician Denoise Algorithm for High Performance Magnetic Resonance Imaging
    MOBIHEALTH
    IEEE
    DOI: 10.4108/icst.mobihealth.2014.257361
Efstathios Sotiriou-Xanthopoulos1,*, Sotirios Xydis1, Kostas Siozios1, George Economakos1, Dimitrios Soudris1
  • 1: National Technical University of Athens
*Contact email: stasot@microlab.ntua.gr

Abstract

Rician denoising is a mandatory task of Magnetic Resonance Imaging (MRI), as it enables higher-quality image processing, which is crucial for correct diagnosis. However, denoising is a slow task, especially because of the increased image resolution and the need for high image clarity. A solution towards this need is the implementation of rician denoise algorithm onto hardware. In this paper, we propose a hardware implementation of rician denoise, which processes the MR image into segments in a pipelined manner, while avoiding further processing on already denoised pixels of the image. Using a synthetic MRI scan separated into 16 segments, the proposed implementation achieves a speedup of 6.8x with comparable image quality, as compared to a software-only approach running on Intel Core2Duo.