Mr. Martin Hultman Profile

Martin Hultman

PhD student at Linkoping Univ

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Area of Expertise:

Laser speckle , FPGA , Machine learning

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I am a PhD student at the Department of Biomedical Engineering at Linköping University, Sweden. I have a Master of Science degree in applied physics and electronic engineering, with a focus on system-on-chip development. My research is focused on imaging microcirculation by using high-performance hardware like FPGAs, as well as machine learning to accelerate the image processing algorithms.

SPIE Journal Paper | 20 March 2023 Open Access

Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning

Martin Hultman, Marcus Larsson, Tomas Strömberg, Ingemar Fredriksson

JBO, Vol. 28, Issue 03, 036007, (March 2023) https://doi.org/10.1117/12.10.1117/1.JBO.28.3.036007

KEYWORDS: Tissues, Laser speckle contrast imaging, Artificial neural networks, Doppler effect, Red blood cells, Perfusion imaging, Data modeling, Education and training, Blood, Skin

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SignificanceLaser speckle contrast imaging (LSCI) gives a relative measure of microcirculatory perfusion. However, due to the limited information in single-exposure LSCI, models are inaccurate for skin tissue due to complex effects from e.g. static and dynamic scatterers, multiple Doppler shifts, and the speed-distribution of blood. It has been demonstrated how to account for these effects in laser Doppler flowmetry (LDF) using inverse Monte Carlo (MC) algorithms. This allows for a speed-resolved perfusion measure in absolute units %RBC × mm/s, improving the physiological interpretation of the data. Until now, this has been limited to a single-point LDF technique but recent advances in multi-exposure LSCI (MELSCI) enable the analysis in an imaging modality.AimTo present a method for speed-resolved perfusion imaging in absolute units %RBC × mm/s, computed from multi-exposure speckle contrast images.ApproachAn artificial neural network (ANN) was trained on a large simulated dataset of multi-exposure contrast values and corresponding speed-resolved perfusion. The dataset was generated using MC simulations of photon transport in randomized skin models covering a wide range of physiologically relevant geometrical and optical tissue properties. The ANN was evaluated on in vivo data sets captured during an occlusion provocation.ResultsSpeed-resolved perfusion was estimated in the three speed intervals 0 to 1 mm / s, 1 to 10 mm / s, and >10 mm / s, with relative errors 9.8%, 12%, and 19%, respectively. The perfusion had a linear response to changes in both blood tissue fraction and blood flow speed and was less affected by tissue properties compared with single-exposure LSCI. The image quality was subjectively higher compared with LSCI, revealing previously unseen macro- and microvascular structures.ConclusionsThe ANN, trained on modeled data, calculates speed-resolved perfusion in absolute units from multi-exposure speckle contrast. This method facilitates the physiological interpretation of measurements using MELSCI and may increase the clinical impact of the technique.

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SPIE Journal Paper | 15 November 2020 Open Access

Real-time video-rate perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning

Martin Hultman, Marcus Larsson, Tomas Strömberg, Ingemar Fredriksson

JBO, Vol. 25, Issue 11, 116007, (November 2020) https://doi.org/10.1117/12.10.1117/1.JBO.25.11.116007

KEYWORDS: Laser speckle contrast imaging, Doppler effect, Real time imaging, Data modeling, Cameras, Image processing, Imaging systems, Speckle, Sensors, Field programmable gate arrays

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Proceedings Article | 6 March 2020 Presentation

Video-rate multi-exposure laser speckle contrast imaging for real-time microcirculation blood flow assessment in skin (Conference Presentation)

Martin Hultman, Ingemar Fredriksson, Marcus Larsson, Tomas Strömberg

Proceedings Volume 11211, 112110Y (2020) https://doi.org/10.1117/12.2541659

KEYWORDS: Blood circulation, Laser speckle contrast imaging, Skin, Doppler effect, In vivo imaging, Blood vessels, Oxygen, Blood, Medical device development, Measurement devices

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SPIE Journal Paper | 23 January 2019 Open Access

Machine learning in multiexposure laser speckle contrast imaging can replace conventional laser Doppler flowmetry

Ingemar Fredriksson, Martin Hultman, Tomas Strömberg, Marcus Larsson

JBO, Vol. 24, Issue 01, 016001, (January 2019) https://doi.org/10.1117/12.10.1117/1.JBO.24.1.016001

KEYWORDS: Laser speckle contrast imaging, Doppler effect, Data modeling, Speckle, Stochastic processes, Machine learning, In vivo imaging, Tissues, Sensors, Calibration

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Proceedings Article | 20 February 2018 Paper

Evaluation of a high framerate multi-exposure laser speckle contrast imaging setup

Martin Hultman, Ingemar Fredriksson, Tomas Strömberg, Marcus Larsson

Proceedings Volume 10505, 1050518 (2018) https://doi.org/10.1117/12.2286248

KEYWORDS: Data modeling, Laser speckle contrast imaging, Field programmable gate arrays, Cameras, Imaging systems, Speckle, Blood, Systems modeling, Doppler effect, Light scattering

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