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  • 1.
    Hultman, Martin
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Fredriksson, Ingemar
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Perimed AB, Järfälla-Stockholm, Sweden.
    Larsson, Marcus
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Alvandpour, Atila
    Linköping University, Department of Electrical Engineering, Integrated Circuits and Systems. Linköping University, Faculty of Science & Engineering.
    Strömberg, Tomas
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    A 15.6 frames per second 1 megapixel Multiple Exposure Laser Speckle Contrast Imaging setup2018In: Journal of Biophotonics, ISSN 1864-063X, E-ISSN 1864-0648, Vol. 11, no 2, article id e201700069Article in journal (Refereed)
    Abstract [en]

    A multiple exposure laser speckle contrast imaging (MELSCI) setup for visualizing blood perfusion was developed using a field programmable gate array (FPGA), connected to a 1000 frames per second (fps) 1-megapixel camera sensor. Multiple exposure time images at 1, 2, 4, 8, 16, 32 and 64 milliseconds were calculated by cumulative summation of 64 consecutive snapshot images. The local contrast was calculated for all exposure times using regions of 4 × 4 pixels. Averaging of multiple contrast images from the 64-millisecond acquisition was done to improve the signal-to-noise ratio. The results show that with an effective implementation of the algorithm on an FPGA, contrast images at all exposure times can be calculated in only 28 milliseconds. The algorithm was applied to data recorded during a 5 minutes finger occlusion. Expected contrast changes were found during occlusion and the following hyperemia in the occluded finger, while unprovoked fingers showed constant contrast during the experiment. The developed setup is capable of massive data processing on an FPGA that enables processing of MELSCI data in 15.6 fps (1000/64 milliseconds). It also leads to improved frame rates, enhanced image quality and enables the calculation of improved microcirculatory perfusion estimates compared to single exposure time systems.

  • 2.
    Hultman, Martin
    et al.
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Fredriksson, Ingemar
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Perimed AB, Järfälla-Stockholm, Sweden.
    Strömberg, Tomas
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Larsson, Marcus
    Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
    Evaluation of a high framerate multi-exposure laser speckle contrast imaging setup2018In: High-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management / [ed] Kevin K. Tsia, Keisuke Goda, SPIE - International Society for Optical Engineering, 2018Conference paper (Refereed)
    Abstract [en]

    We present a first evaluation of a new multi-exposure laser speckle contrast imaging (MELSCI) system for assessing spatial variations in the microcirculatory perfusion. The MELSCI system is based on a 1000 frames per second 1-megapixel camera connected to a field programmable gate arrays (FPGA) capable of producing MELSCI data in realtime. The imaging system is evaluated against a single point laser Doppler flowmetry (LDF) system during occlusionrelease provocations of the arm in five subjects. Perfusion is calculated from MELSCI data using current state-of-the-art inverse models. The analysis displayed a good agreement between measured and modeled data, with an average error below 6%. This strongly indicates that the applied model is capable of accurately describing the MELSCI data and that the acquired data is of high quality. Comparing readings from the occlusion-release provocation showed that the MELSCI perfusion was significantly correlated (R=0.83) to the single point LDF perfusion, clearly outperforming perfusion estimations based on a single exposure time. We conclude that the MELSCI system provides blood flow images of enhanced quality, taking us one step closer to a system that accurately can monitor dynamic changes in skin perfusion over a large area in real-time

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