Multi-exposure Laser Speckle Contrast Imaging Research Articles

Enhancing blood flow prediction in multi-exposure laser speckle contrast imaging through ensemble learning with K-mean clustering

Purpose. Accurately visualizing and measuring blood flow is of utmost importance in maintaining optimal health and preventing the onset of various chronic diseases. One promising imaging technique that aids in visualizing perfusion in biological tissues is Multi-exposure Laser Speckle Contrast Imaging (MELSCI). MELSCI technique allows real-time quantitative measurements using multiple exposure times to obtain precise and reliable blood flow data. Additionally, the application of machine learning (ML) techniques can further enhance the accuracy of blood flow prediction in this imaging modality. Method. Our study focused on developing and evaluating Ensemble Learning ML techniques along with clustering algorithms for predicting blood flow rates in MELSCI. The effectiveness of these techniques was assessed using performance parameters, including accuracy, F1-score, precision, recall, specificity, and classification error rate. Result. Notably, the study revealed that Ensemble Learning with clustering emerged as the most accurate technique, achieving an impressive accuracy rate of 98.5%. Furthermore, it demonstrated a high recall of more than 91%, F1-score, the precision of more than 90%, higher specificity of 99%, and least classification error of 1.5%, highlighting its suitability and sustainability for flow prediction in MELSCI. Conclusion. The study’s findings imply that Ensemble Learning can significantly contribute to enhancing the accuracy of blood flow prediction in MELSCI. This advancement holds substantial promise for healthcare professionals and researchers, as it facilitates improved understanding and assessment of perfusion within biological tissues, which will contribute to the maintenance of good health and prevention of chronic diseases.

Evolutionary Gravitational Neocognitron Neural Network-Based Blood Flow Velocity Prediction Using Multi-Exposure Laser Speckle Contrast Imaging

Wide-field and noncontact imaging technique called laser speckle contrast imaging (LSCI) is utilized to map blood flow. To greatly improve prediction accuracy, the block-matching along 3-dimensional transform domain collaborative filtering-dependent denoising technique is developed. To implement real-time denoising, the processing time makes complexity. Due to the existence of considerable noise and artifacts, this is challenging to achieve the acceptable level with less raw speckle images. Notwithstanding, it acts poorly while learning from the original LSCI speckle contrast images because of the uneven noise distribution. To overcome this issue, an evolutionary gravitational neocognitron neural network-based blood flow velocity prediction using multiple exposure laser speckle contrast imaging (EGNNN-BFVP-MeLSCI) is proposed. Initially, the input MeLSCI datasets are collected from real-time dataset. The input picture is enhanced and noise is removed using pre-processing technique called altered phase preserving dynamic range compression (APPDRC). Gray level co-occurrence matrix (GLCM) window adaptive approach-dependent feature extraction approach is used for the preprocessed pictures. Using GLCM window adaptive method, the picture characteristics, such as intensity information, images derivative, geodesic information, contrasts, energy, correlations, homogeneity, and entropy, are retrieved. Then, the extracted features are transferred into the EGNNN classifier for accurately predicting the blood flow velocity. The performance of proposed method is executed at python and evaluated under certain performance metrics, such as accuracy, precision, sensitivity, specificity, [Formula: see text]-measure, ROC, computation time, mean squared error, root mean squared error, predicted velocity. The proposed EGNNN-BFVP-MeLSCI method attains higher accuracy of 96.31%, 97.06%, and 93.52%, lower computational time of 97.22%, 91.39%, 96.41% compared with existing approaches, like DnCNN-BFVP-MeLSCI, CFD-BFVP-MeLSCI, DLS-BFVP-MeLSCI, CNN-BFVP-MeLSCI, DBN-BFVP-MeLSCI, CGAN-BFVP-MeLSCI and MVN-BFVP-MeLSCI, respectively.

Multi-exposure Laser Speckle Contrast Imaging (MECI)-Based Prediction of Blood Flow Using Random Forest (RF) With K-Means (KM).

Blood flow prediction is very important for medical diagnosis, drug development, tissue engineering, and continuous monitoring. One commonly used method for studying blood flow is called multi-exposure laser speckle contrast imaging (MECI). It provides valuable insights into how blood flows through tissues and helps in diagnosing circulatory diseases. In our study, we used MECI to measure blood flow in real-time by taking multiple measurements with different exposure times and contrasts. To predict different blood flow rates ranging from 0.1 to 1 mm/s, we employed machine learning (ML) techniques like clustering and random forest (RF) or support vector machine (SVM) algorithms. The study showed that RF with K-means performance is found to be the most accurate technique for flow classification, with an accuracy of 98.5%, a precision of 92%, a specificity of 98.9%, and a classification error of 1.5%. Our study demonstrates that employing clustering and RF algorithms in MECI provides a robust and effective approach to predicting blood flow. This technique holds great potential for a wide range of applications in the medical and healthcare fields.

Blood Flow Prediction in Multi-Exposure Speckle Contrast Imaging Using Conditional Generative Adversarial Network.

Purpose Blood perfusion is an important physiological parameter that can be quantitatively assessed using various imaging techniques. Blood flow prediction in laser speckle contrast imagingis important for medical diagnosis, drug development, tissue engineering, biomedical research, and continuous monitoring. Deep learning is a new and promising approach for predicting blood flow whenever thecondition varies, but it comes with a high learning cost for real-world scenarios with a variable flow value derived from multi-exposure laser speckle contrast imaging (MECI) data. A generative adversarial network (GAN) is presented in this research for the reliable prediction of blood flows in diverse scenarios in MECI. Method We suggested a time-efficient approach using a low frame rate camera that can be used to predict blood flow in MECI data by using conditional GAN architecture. Our approach is implemented by extending our work to the entire flow as well as the specific region of interest (ROI) in the flow. Results Results show that conditional GAN exhibits improved generalization ability to predict blood flow in MECI when compared to classifications-based deep learning approaches with an accuracy of 98.5% with a relative mean error of 1.57% for the whole field and 7.53% for a specific ROI. Conclusion The conditional GAN is very effective in predicting blood flows in MECI, entirely or within ROI, compared with other deep learning approaches.

A quantitative laser speckle-based velocity prediction approach using machine learning

Laser speckle contrast imaging (LSCI) can be applied to non-invasive blood perfusion measurement with high resolution and fast speed. However, it is lack of measurement accuracy. The aim of this study is to enable quantitative measurement of LSCI by using Artificial Intelligence (AI), and this is achieved by using a set of experimental data obtained from a rotating diffuser (a tissue phantom mimicking blood flow under skins) within simulated flow velocity of 0.08–10.74 mm/s. These data were used to train a three-dimensional convolutional neural network (3D-CNN) to establish a LSCI velocities prediction model (CNN-LSCI) with behavioral feature learning. The trained model has 0.33 MSE (mean squared error) and 0.34 MAPE (mean absolute percentage error) and is verified by ten phantom velocities (0.2-4 mm/s, step is 0.445 mm/s) covering the typical blood flow velocity range of human body (0-2 mm/s) with the correlation of 0.98. The better performance of the proposed model is demonstrated by the results compared to traditional LSCI and multi-exposure laser speckle contrast imaging (MELSCI). This study shows the potential of LSCI to achieve quantitative blood perfusion measurement using machine learning.

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

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. To present a method for speed-resolved perfusion imaging in absolute units %RBC × mm/s, computed from multi-exposure speckle contrast images. 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. Speed-resolved perfusion was estimated in the three speed intervals 0 to , 1 to , and , 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. 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.

Flowmotion imaging analysis of spatiotemporal variations in skin microcirculatory perfusion.

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 heterogeneous 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. 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. 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. The flowmotion images displayed patterns of high spatial heterogeneity that differed between the frequency 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. The new imaging technique reveals significant spatial differences in flowmotion that cannot be obtained 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. • Flowmotion imaging displays significant spatiotemporal variations in forearm skin blood flow. • Flowmotion imaging has potential to be more accurate than single-point techniques. • A simple and effective method for motion artifact removal is presented.

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.

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

Multi-exposure laser speckle contrast imaging using a video-rate multi-tap charge modulation image sensor.

Multi-exposure laser speckle contrast imaging (MELSCI) systems based on high frame rate cameras are suitable for wide-field quantitative measurement of blood flow. However, high-speed camera-based MELSCI requires high power consumption, large memory, and high processing capability, which may lead to relatively large and expensive hardware. To realize a compact and cost-efficient MELSCI system, we discuss an application of the multi-tap CMOS image sensor originally designed for time-of-flight range imaging. This image sensor operated in the global shutter mode and every pixel was provided with multiple charge-storage diodes. Multiple images for different exposures were acquired simultaneously because exposure patterns were programmable to implement an arbitrary exposure duration for each tap. The frame rate was close to video frame rates (30 frames per second (fps)) regardless of the exposure pattern. The feasibility of the proposed method was verified by simulations that were performed with real speckle images captured by a high-speed camera at 40 kfps. Experiments with a four-tap CMOS image sensor demonstrated that a flow speed map was obtained at a moderate frame rate such as 35 fps for a moving ground glass plate and 45 fps for flowing Intralipose, which were linearly moved at speeds of 1-5 mm/s.

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 for noise-free data, when a moderate degree of noise is present, and for in vivo data from an occlusion-release experiment.

Multi-exposure laser speckle contrast imaging using a high frame rate CMOS sensor with a field programmable gate array.

A system has been developed in which multi-exposure laser speckle contrast imaging (LSCI) is implemented using a high frame rate CMOS imaging sensor chip. Processing is performed using a field programmable gate array (FPGA). The system allows different exposure times to be simulated by accumulating a number of short exposures. This has the advantage that the image acquisition time is limited by the maximum exposure time and that regulation of the illuminating light level is not required. This high frame rate camera has also been deployed to implement laser Doppler blood flow processing, enabling a direct comparison of multi-exposure laser speckle imaging and laser Doppler imaging (LDI) to be carried out using the same experimental data. Results from a rotating diffuser indicate that both multi-exposure LSCI and LDI provide a linear response to changes in velocity. This cannot be obtained using single-exposure LSCI, unless an appropriate model is used for correcting the response.

High-speed multi-exposure laser speckle contrast imaging with a single-photon counting camera.

Laser speckle contrast imaging (LSCI) has emerged as a valuable tool for cerebral blood flow (CBF) imaging. We present a multi-exposure laser speckle imaging (MESI) method which uses a high-frame rate acquisition with a negligible inter-frame dead time to mimic multiple exposures in a single-shot acquisition series. Our approach takes advantage of the noise-free readout and high-sensitivity of a complementary metal-oxide-semiconductor (CMOS) single-photon avalanche diode (SPAD) array to provide real-time speckle contrast measurement with high temporal resolution and accuracy. To demonstrate its feasibility, we provide comparisons between in vivo measurements with both the standard and the new approach performed on a mouse brain, in identical conditions.

Rapid multiexposure in vivo brain imaging system using vertical cavity surface emitting lasers as a light source

We demonstrate an imaging technique implementing vertical cavity lasers with extremely low transient times for a greatly simplified realization of a multiexposure laser speckle contrast imaging system. Data from multiexposure laser speckle imaging was observed to more closely agree with absolute velocity measurements using time of flight technique, when compared to long-exposure laser speckle imaging. Furthermore, additional depth information of the vasculature morphology was inferred by accounting for the change in the static scattering from tissue above vessels with respect to the total scattering from blood flow and tissue.

Multiexposure laser speckle contrast imaging of the angiogenic microenvironment

We report the novel use of laser speckle contrast imaging (LSCI) at multiple exposure times (meLSCI) for enhanced in vivo imaging of the microvascular changes that accompany angiogenesis. LSCI is an optical imaging technique that can monitor blood vessels and the flow therein at a high spatial resolution without requiring the administration of an exogenous contrast agent. LSCI images are obtained under red (632 nm) laser illumination at seven exposure times (1-7 ms) and combined using a curve-fitting approach to obtain high-resolution meLSCI images of the rat brain vasculature. To evaluate enhancement in in vivo imaging performance, meLSCI images are statistically compared to individual LSCI images obtained at a single exposure time. We find that meLSCI reduced the observed variability in the LSCI-based blood-flow estimates by 30% and improved the contrast-to-noise ratio in regions with high microvessel density by 41%. The ability to better distinguish microvessels, makes meLSCI uniquely suited to longitudinal imaging of changes in the vascular microenvironment induced by pathological angiogenesis. We demonstrate this utility of meLSCI by sequentially monitoring, over days, the microvascular changes that accompany wound healing in a mouse ear model.