Coagulation depth estimation using a line scanner for depth-resolved laser speckle contrast imaging.

Partial-thickness burn wounds extend partially through the dermis, leaving many pain receptors intact and making the injuries very painful. Due to the painfulness, quick assessment of the burn depth is important to not delay surgery of the wound if needed. Laser speckle imaging (LSI) of skin blood flow can be helpful in finding severe coagulation zones with impaired blood flow.

Robust analysis of microcirculatory flowmotion during post-occlusive reactive hyperemia.

Background: Flowmotion analysis of the microcirculatory blood flow is a method to extract information about the vessel regulatory function. It has previously shown promise when applied to measurements during a post-occlusive reactive hyperemia. However, the reperfusion peak and the following monotonic decline introduces false low frequencies that should not be interpreted as rhythmic vasomotion effect.

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.

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.

Driver sleepiness detection with deep neural networks using electrophysiological data.

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.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.

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.

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.

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.