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

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 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. Aim: To present a method for speed-resolved perfusion imaging in absolute units %RBC × 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.

Comprehensive imaging of microcirculatory changes in the foot during endovascular intervention - A technical feasibility study.

Chronic limb-threatening ischemia (CLTI) has a major impact on patients' 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 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.

Driver sleepiness detection with deep neural networks using electrophysiological data.

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 ϵ [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.

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

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 (R2 = 0.992). CONCLUSIONS: The presented MELSCI system allows for real-time acquisition and calculation of high-quality perfusion at 15.6 frames per second.

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

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.

A 15.6 frames per second 1-megapixel multiple exposure laser speckle contrast imaging setup.

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.