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  • 1.
    Hultman, Martin
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Perimed AB, Sweden.
    Larsson, Marcus
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Strömberg, Tomas
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Henricson, Joakim
    Linköpings universitet, Institutionen för biomedicinska och kliniska vetenskaper, Avdelningen för klinisk kemi och farmakologi. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Närsjukvården i centrala Östergötland, Akutkliniken i Linköping.
    Iredahl, Fredrik
    Linköpings universitet, Medicinska fakulteten. Region Östergötland, Primärvårdscentrum, Vårdcentralen Åby. Linköpings universitet, Institutionen för hälsa, medicin och vård, Avdelningen för samhälle och hälsa.
    Fredriksson, Ingemar
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Perimed AB, Sweden.
    Flowmotion imaging analysis of spatiotemporal variations in skin microcirculatory perfusion2023Ingår i: Microvascular Research, ISSN 0026-2862, E-ISSN 1095-9319, Vol. 146, artikel-id 104456Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background: Flowmotion is the rhythmical variations in measured skin blood flow that arise due to global and local regulation of the vessels and can be studied using frequency analysis of time-resolved blood flow signals. It has the potential to reveal clinically useful information about microvascular diseases, but the spatial heteroge-neous nature of the microvasculature makes interpretation difficult. However, recent technological advances in multi-exposure laser speckle contrast imaging (MELSCI) enable new possibilities for simultaneously studying spatial and temporal variations in flowmotion.Aim: To develop a method for flowmotion analysis of MELSCI perfusion images. Furthermore, to investigate the spatial and temporal variations in flowmotion in forearm baseline skin perfusion.Method: In four healthy subjects, forearm skin perfusion was imaged at 15.6 fps for 10 min in baseline. The time -trace signal in each pixel was analyzed using the wavelet transform and summarized in five physiologically relevant frequency intervals, resulting in images of flowmotion. Furthermore, a method for reducing the effect of motion artifacts in the flowmotion analysis was developed.Results: The flowmotion images displayed patterns of high spatial heterogeneity that differed between the fre-quency intervals. The spatial variations in flowmotion, quantified as the coefficient of variation, was between 11 % and 31 % in four subjects. Furthermore, significant temporal variations in flowmotion were also observed, indicating the importance of a spatiotemporal analysis.Conclusion: The new imaging technique reveals significant spatial differences in flowmotion that cannot be ob-tained with single-point measurements. The results indicate that global statistics of flowmotion, such as the mean value in a large region of interest, is more representative of the microcirculation than data measured only in a single point. Therefore, imaging techniques have potential to increase the clinical usefulness of flowmotion analysis.

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  • 2.
    Hultman, Martin
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Perimed AB, Sweden.
    Larsson, Marcus
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Strömberg, Tomas
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Fredriksson, Ingemar
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Perimed AB, Sweden.
    Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning2023Ingår i: Journal of Biomedical Optics, ISSN 1083-3668, E-ISSN 1560-2281, Vol. 28, nr 3, artikel-id 036007Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Significance: Laser 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 inverseMonte Carlo (MC) algorithms. This allows for a speed-resolved perfusion measure in absolute units %RBC x mm/s, improving the physiological interpretation of the data. Until now, this has been limited to a single-point LDF technique but recent advances inmulti-exposure LSCI (MELSCI) enable the analysis in an imaging modality. Aim: To present a method for speed-resolved perfusion imaging in absolute units %RBC x mm/s, computed from multi-exposure speckle contrast images. Approach: An 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. Results: Speed-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. Conclusions: The 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. (c) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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  • 3.
    Hultman, Martin
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Aronsson, Sofie
    Linköpings universitet, Institutionen för hälsa, medicin och vård, Avdelningen för diagnostik och specialistmedicin. Linköpings universitet, Medicinska fakulteten.
    Fredriksson, Ingemar
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Perimed AB, Järfälla, Stockholm, Sweden.
    Zachrisson, Helene
    Linköpings universitet, Institutionen för hälsa, medicin och vård, Avdelningen för diagnostik och specialistmedicin. Region Östergötland, Hjärtcentrum, Fysiologiska kliniken US. Linköpings universitet, Medicinska fakulteten.
    Pärsson, Håkan N.
    Linköpings universitet, Institutionen för biomedicinska och kliniska vetenskaper, Avdelningen för kirurgi, ortopedi och onkologi. Linköpings universitet, Medicinska fakulteten.
    Larsson, Marcus
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Strömberg, Tomas
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Comprehensive imaging of microcirculatory changes in the foot during endovascular intervention - A technical feasibility study2022Ingår i: Microvascular Research, ISSN 0026-2862, E-ISSN 1095-9319, Vol. 141, artikel-id 104317Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Chronic limb-threatening ischemia (CLTI) has a major impact on patient's lives and is associated with a heavy health care burden with high morbidity and mortality. Treatment by endovascular intervention is mostly based on macrocirculatory information from angiography and does not consider the microcirculation. Despite successful endovascular intervention according to angiographic criteria, a proportion of patients fail to heal ischemic lesions. This might be due to impaired microvascular perfusion and variations in the supply to different angiosomes. Non-invasive optical techniques for microcirculatory perfusion and oxygen saturation imaging have the potential to provide the interventionist with additional information in real-time, supporting clinical decisions during the intervention. This study presents a novel multimodal imaging system, based on multi-exposure laser speckle contrast imaging and multi-spectral imaging, for continuous use during endovascular intervention. The results during intervention display spatiotemporal changes in the microcirculation compatible with expected physiological reactions during balloon dilation, with initially induced ischemia followed by a restored perfusion, and local administration of a vasodilator inducing hyperemia. We also present perioperative and postoperative follow-up measurements with a pulsatile microcirculation perfusion. Finally, cases of spatial heterogeneity in the observed oxygen saturation and perfusion are discussed. In conclusion, this technical feasibility study shows the potential of the methodology to characterize changes in microcirculation before, during, and after endovascular intervention.

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  • 4.
    Hultman, Martin
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Johansson, Ida
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Lindqvist, Frida
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Ahlström, Christer
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Swedish Natl Rd & Transport Res Inst VTI, Linkoping, Sweden.
    Driver sleepiness detection with deep neural networks using electrophysiological data2021Ingår i: Physiological Measurement, ISSN 0967-3334, E-ISSN 1361-6579, Vol. 42, nr 3, artikel-id 034001Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Objective. The objective of this paper is to present a driver sleepiness detection model based on electrophysiological data and a neural network consisting of convolutional neural networks and a long short-term memory architecture. Approach. The model was developed and evaluated on data from 12 different experiments with 269 drivers and 1187 driving sessions during daytime (low sleepiness condition) and night-time (high sleepiness condition), collected during naturalistic driving conditions on real roads in Sweden or in an advanced moving-base driving simulator. Electrooculographic and electroencephalographic time series data, split up in 16 634 2.5 min data segments was used as input to the deep neural network. This probably constitutes the largest labeled driver sleepiness dataset in the world. The model outputs a binary decision as alert (defined as <= 6 on the Karolinska Sleepiness Scale, KSS) or sleepy (KSS >= 8) or a regression output corresponding to KSS epsilon [1-5, 6, 7, 8, 9]. Main results. The subject-independent mean absolute error (MAE) was 0.78. Binary classification accuracy for the regression model was 82.6% as compared to 82.0% for a model that was trained specifically for the binary classification task. Data from the eyes were more informative than data from the brain. A combined input improved performance for some models, but the gain was very limited. Significance. Improved classification results were achieved with the regression model compared to the classification model. This suggests that the implicit order of the KSS ratings, i.e. the progression from alert to sleepy, provides important information for robust modelling of driver sleepiness, and that class labels should not simply be aggregated into an alert and a sleepy class. Furthermore, the model consistently showed better results than a model trained on manually extracted features based on expert knowledge, indicating that the model can detect sleepiness that is not covered by traditional algorithms.

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  • 5. Beställ onlineKöp publikationen >>
    Hultman, Martin
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Real-time multi-exposure laser speckle contrast imaging of skin microcirculatory perfusion2021Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    The microcirculation, the blood flow in the smallest blood vessels in the body, has a vital function as this is where oxygen and nutrients diffuses from the blood to to the surrounding cells. An important quantity is the tissue perfusion, a measure of the microcirculation’s capacity to provide oxygen and nutrients to the cells. Laser speckle contrast imaging (LSCI) is a non-invasive optical technique that captures images of the microcirculatory perfusion by analysing the local contrast in the laser speckle pattern that forms when tissue is illuminated by a laser. LSCI has seen extensive use in clinical research due to the easy and cheap measurement setup, and high spatial and temporal resolution. Despite this, clinical acceptance and routine use remains low. Some of the drawbacks of the technique is a limitation to relative measurements in arbitrary units, as well as high susceptibility to measurement noise and confounding properties of the tissue. This makes comparisons difficult, especially between patients. An extension of LSCI called multi-exposure laser speckle contrast imaging (MELSCI) was proposed to deal with some of these issues, although the more complicated data acquisition and models prevented real-time use. MELSCI has in-stead been used exclusively as an offline technique where data is post-processed, and the clinical use has been non-existent. Furthermore, existing models for LSCI and MELSCI are designed for tissues where individual vessels are visible, such as the surface of the brain or on the retina. For measurements in the diffuse regime, such as on skin tissue, these models are no longer physiologically accurate, resulting in incorrect perfusion estimates.

    This thesis presents a MELSCI-based perfusion imaging instrument that is simultaneously fast and physiologically accurate for measurements of skin. There are three main parts to this work; development of a real-time MELSCI system, development of perfusion models for skin, and demonstration of the system in a clinical feasibility study.

    A real-time MELSCI instrument was developed based on a high-speed CMOS camera tightly integrated with algorithms in a field programmable gate array (FPGA). The algorithm was based on synthetic multi-exposure, where a set of 64 individual 1-ms images were digitally added to create multi-exposure images at 1, 2, 4, 8, 16, 32, and 64 ms. The resulting multi-exposure data was demonstrated to have high quality and less susceptibility to measurement noise than previous models. The instrument enabled continuous acquisition and analysis of MELSCI data in real-time at 15.6 frames per second, sufficiently fast to capture the temporal dynamics of the skin perfusion.

    To enable real-time estimation of accurate and physiologically relevant perfusion from the MELSCI data, two artificial neural networks were trained on synthetic data from a mathematical model of skin. The first estimated perfusion as computed by conventional laser Doppler flowmetry (LDF), demonstrating a high correlation between the two methods. The second estimated true perfusion in absolute units %RBC × mm/s separated into three distinct speed components, 0-1 mm/s, 1-10 mm/s and >10 mm/s. The ANNs removed the need for iterative optimization algorithms, resulting in more than 1000x speed-up over previous methods, and enabled real-time use in an imaging setting.

    The instrument was demonstrated in controlled experiments on healthy volunteers, using standardized occlusion-release provocations, and in a clinical feasibility study where the foot perfusion was monitored during endovascular interventions in patients with chronic limb-threatening ischemia. The instrument enabled continuous imaging of perfusion, with sufficiently high framerate to capture the pulsatile dynamics, or lack thereof, at each point in time. The necessity for both high spatial and temporal resolution to properly asses the microcirculation was demonstrated.

    The advancements to MELSCI proposed in this thesis has the potential to improve the clinical viability of the technique, increase interpretability of the results, and might lead to improved treatments based on a better understanding of the complex processes in the microcirculation.

    Delarbeten
    1. A 15.6 frames per second 1 megapixel Multiple Exposure Laser Speckle Contrast Imaging setup
    Öppna denna publikation i ny flik eller fönster >>A 15.6 frames per second 1 megapixel Multiple Exposure Laser Speckle Contrast Imaging setup
    Visa övriga...
    2018 (Engelska)Ingår i: Journal of Biophotonics, ISSN 1864-063X, E-ISSN 1864-0648, Vol. 11, nr 2, artikel-id e201700069Artikel i tidskrift (Refereegranskat) Published
    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.

    Ort, förlag, år, upplaga, sidor
    Wiley-VCH Verlagsgesellschaft, 2018
    Nyckelord
    blood flow, blood perfusion, FPGA, LASCA, LSCI, microcirculation, multiexposure
    Nationell ämneskategori
    Annan medicinteknik
    Identifikatorer
    urn:nbn:se:liu:diva-141201 (URN)10.1002/jbio.201700069 (DOI)000424643600014 ()2-s2.0-85026753968 (Scopus ID)
    Forskningsfinansiär
    Vetenskapsrådet, 2014-6141
    Tillgänglig från: 2017-09-26 Skapad: 2017-09-26 Senast uppdaterad: 2021-12-28Bibliografiskt granskad
    2. Evaluation of a high framerate multi-exposure laser speckle contrast imaging setup
    Öppna denna publikation i ny flik eller fönster >>Evaluation of a high framerate multi-exposure laser speckle contrast imaging setup
    2018 (Engelska)Ingår i: 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, 2018Konferensbidrag, Publicerat paper (Refereegranskat)
    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

    Ort, förlag, år, upplaga, sidor
    SPIE - International Society for Optical Engineering, 2018
    Serie
    Progress in Biomedical Optics and Imaging - Proceedings of SPIE, ISSN 0277-786X ; 10505
    Nationell ämneskategori
    Medicinteknik
    Identifikatorer
    urn:nbn:se:liu:diva-148844 (URN)10.1117/12.2286248 (DOI)000446339000015 ()978-1-5106-1496-3 (ISBN)
    Konferens
    SPIE BIOS 27 January - 1 February 2018 San Francisco, California, United States
    Tillgänglig från: 2018-06-20 Skapad: 2018-06-20 Senast uppdaterad: 2021-12-28
    3. Machine learning in multiexposure laser speckle contrast imaging can replace conventional laser Doppler flowmetry
    Öppna denna publikation i ny flik eller fönster >>Machine learning in multiexposure laser speckle contrast imaging can replace conventional laser Doppler flowmetry
    2019 (Engelska)Ingår i: Journal of Biomedical Optics, ISSN 1083-3668, E-ISSN 1560-2281, Vol. 24, nr 1, artikel-id 016001Artikel i tidskrift (Refereegranskat) Published
    Abstract [en]

    Laser speckle contrast imaging (LSCI) enables video rate imaging of blood flow. However, its relation to tissue blood perfusion is nonlinear and depends strongly on exposure time. By contrast, the perfusion estimate from the slower laser Doppler flowmetry (LDF) technique has a relationship to blood perfusion that is better understood. Multiexposure LSCI (MELSCI) enables a perfusion estimate closer to the actual perfusion than that using a single exposure time. We present and evaluate a method that utilizes contrasts from seven exposure times between 1 and 64 ms to calculate a perfusion estimate that resembles the perfusion estimate from LDF. The method is based on artificial neural networks (ANN) for fast and accurate processing of MELSCI contrasts to perfusion. The networks are trained using modeling of Doppler histograms and speckle contrasts from tissue models. The importance of accounting for noise is demonstrated. Results show that by using ANN, MELSCI data can be processed to LDF perfusion with high accuracy, with a correlation coefficient R = 1.000 for noise-free data, R = 0.993 when a moderate degree of noise is present, and R = 0.995 for in vivo data from an occlusion-release experiment. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

    Ort, förlag, år, upplaga, sidor
    SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, 2019
    Nyckelord
    blood flow; microcirculation; laser speckle contrast analysis; artificial neural networks
    Nationell ämneskategori
    Medicinsk laboratorie- och mätteknik
    Identifikatorer
    urn:nbn:se:liu:diva-156587 (URN)10.1117/1.JBO.24.1.016001 (DOI)000463884000006 ()30675771 (PubMedID)
    Anmärkning

    Funding Agencies|Swedish Research Council [2014-6141]; Swedens Innovation Agency VINNOVA [2016-02211, 2017-01435]

    Tillgänglig från: 2019-05-14 Skapad: 2019-05-14 Senast uppdaterad: 2021-12-28
    4. Real-time video-rate perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning
    Öppna denna publikation i ny flik eller fönster >>Real-time video-rate perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning
    2020 (Engelska)Ingår i: Journal of Biomedical Optics, ISSN 1083-3668, E-ISSN 1560-2281, Vol. 25, nr 11, artikel-id 116007Artikel i tidskrift (Refereegranskat) Published
    Abstract [en]

    Significance: Multi-exposure laser speckle contrast imaging (MELSCI) estimates microcirculatory blood perfusion more accurately than single-exposure LSCI. However, the technique has been hampered by technical limitations due to massive data throughput requirements and nonlinear inverse search algorithms, limiting it to an offline technique where data must be postprocessed. Aim: To present an MELSCI system capable of continuous acquisition and processing of MELSCI data, enabling real-time video-rate perfusion imaging with high accuracy. Approach: The MELSCI algorithm was implemented in programmable hardware (field programmable gate array) closely interfaced to a high-speed CMOS sensor for real-time calculation. Perfusion images were estimated in real-time from the MELSCI data using an artificial neural network trained on simulated data. The MELSCI perfusion was compared to two existing single-exposure metrics both quantitatively in a controlled phantom experiment and qualitatively in vivo. Results: The MELSCI perfusion shows higher signal dynamics compared to both single-exposure metrics, both spatially and temporally where heartbeat-related variations are resolved in much greater detail. The MELSCI perfusion is less susceptible to measurement noise and is more linear with respect to laser Doppler perfusion in the phantom experiment (R-2 = 0.992). Conclusions: The presented MELSCI system allows for real-time acquisition and calculation of high-quality perfusion at 15.6 frames per second. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

    Ort, förlag, år, upplaga, sidor
    SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, 2020
    Nyckelord
    microcirculation; perfusion; multi-exposure laser speckle contrast imaging; laser speckle contrast imaging; laser speckle contrast analysis; laser Doppler
    Nationell ämneskategori
    Medicinsk laboratorie- och mätteknik
    Identifikatorer
    urn:nbn:se:liu:diva-172436 (URN)10.1117/1.JBO.25.11.116007 (DOI)000595595000017 ()33191685 (PubMedID)
    Anmärkning

    Funding Agencies|Swedish Research CouncilSwedish Research Council [2014-6141]; Swedens Innovation Agency VINNOVAvia the programs Swelife and MedTech4Health [2017-01435, 2019-01522]

    Tillgänglig från: 2021-01-10 Skapad: 2021-01-10 Senast uppdaterad: 2021-12-28
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  • 6.
    Hultman, Martin
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Larsson, Marcus
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Strömberg, Tomas
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Fredriksson, Ingemar
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Perimed AB, Sweden.
    Real-time video-rate perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning2020Ingår i: Journal of Biomedical Optics, ISSN 1083-3668, E-ISSN 1560-2281, Vol. 25, nr 11, artikel-id 116007Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Significance: Multi-exposure laser speckle contrast imaging (MELSCI) estimates microcirculatory blood perfusion more accurately than single-exposure LSCI. However, the technique has been hampered by technical limitations due to massive data throughput requirements and nonlinear inverse search algorithms, limiting it to an offline technique where data must be postprocessed. Aim: To present an MELSCI system capable of continuous acquisition and processing of MELSCI data, enabling real-time video-rate perfusion imaging with high accuracy. Approach: The MELSCI algorithm was implemented in programmable hardware (field programmable gate array) closely interfaced to a high-speed CMOS sensor for real-time calculation. Perfusion images were estimated in real-time from the MELSCI data using an artificial neural network trained on simulated data. The MELSCI perfusion was compared to two existing single-exposure metrics both quantitatively in a controlled phantom experiment and qualitatively in vivo. Results: The MELSCI perfusion shows higher signal dynamics compared to both single-exposure metrics, both spatially and temporally where heartbeat-related variations are resolved in much greater detail. The MELSCI perfusion is less susceptible to measurement noise and is more linear with respect to laser Doppler perfusion in the phantom experiment (R-2 = 0.992). Conclusions: The presented MELSCI system allows for real-time acquisition and calculation of high-quality perfusion at 15.6 frames per second. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

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  • 7.
    Fredriksson, Ingemar
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Perimed AB, Sweden.
    Hultman, Martin
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Strömberg, Tomas
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Larsson, Marcus
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Machine learning in multiexposure laser speckle contrast imaging can replace conventional laser Doppler flowmetry2019Ingår i: Journal of Biomedical Optics, ISSN 1083-3668, E-ISSN 1560-2281, Vol. 24, nr 1, artikel-id 016001Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Laser speckle contrast imaging (LSCI) enables video rate imaging of blood flow. However, its relation to tissue blood perfusion is nonlinear and depends strongly on exposure time. By contrast, the perfusion estimate from the slower laser Doppler flowmetry (LDF) technique has a relationship to blood perfusion that is better understood. Multiexposure LSCI (MELSCI) enables a perfusion estimate closer to the actual perfusion than that using a single exposure time. We present and evaluate a method that utilizes contrasts from seven exposure times between 1 and 64 ms to calculate a perfusion estimate that resembles the perfusion estimate from LDF. The method is based on artificial neural networks (ANN) for fast and accurate processing of MELSCI contrasts to perfusion. The networks are trained using modeling of Doppler histograms and speckle contrasts from tissue models. The importance of accounting for noise is demonstrated. Results show that by using ANN, MELSCI data can be processed to LDF perfusion with high accuracy, with a correlation coefficient R = 1.000 for noise-free data, R = 0.993 when a moderate degree of noise is present, and R = 0.995 for in vivo data from an occlusion-release experiment. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

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  • 8.
    Hultman, Martin
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Fredriksson, Ingemar
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Perimed AB, Järfälla-Stockholm, Sweden.
    Larsson, Marcus
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Alvandpour, Atila
    Linköpings universitet, Institutionen för systemteknik, Elektroniska Kretsar och System. Linköpings universitet, Tekniska fakulteten.
    Strömberg, Tomas
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    A 15.6 frames per second 1 megapixel Multiple Exposure Laser Speckle Contrast Imaging setup2018Ingår i: Journal of Biophotonics, ISSN 1864-063X, E-ISSN 1864-0648, Vol. 11, nr 2, artikel-id e201700069Artikel i tidskrift (Refereegranskat)
    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.

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  • 9.
    Hultman, Martin
    et al.
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Fredriksson, Ingemar
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten. Perimed AB, Järfälla-Stockholm, Sweden.
    Strömberg, Tomas
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Larsson, Marcus
    Linköpings universitet, Institutionen för medicinsk teknik, Avdelningen för medicinsk teknik. Linköpings universitet, Tekniska fakulteten.
    Evaluation of a high framerate multi-exposure laser speckle contrast imaging setup2018Ingår i: 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, 2018Konferensbidrag (Refereegranskat)
    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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