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

Dynamics of individual inkjet printed picoliter droplet elucidated by high speed laser speckle imaging

Inkjet printing is a ubiquitous consumer and industrial process that involves concomitant processes of droplet impact, wetting, evaporation, and imbibement into a substrate as well as consequential substrate rearrangements and remodeling. In this work, we perform a study on the interaction between ink dispersions of different composition on substrates of increasing complexity to disentangle the motion of the liquid from the dynamic response of the substrate. We print three variations of pigmented inks and follow the ensuing dynamics at millisecond and micron time and length scales until complete drying using a multiple scattering technique, laser speckle imaging (LSI). Measurements of the photon transport mean free path, l*, for the printed inks and substrates show that the spatial region of information capture is the entire droplet volume and a depth within the substrate of a few μm beneath the droplet. Within this spatial confinement, LSI is an ideal approach for studying the solid-liquid transition at these small length and time scales by obtaining valid g2 and d2 autocorrelation functions and interpreting these dynamic changes under through kymographs. Our in situ LSI results show that droplets undergo delamination and cracking processes arising during droplet drying, which are confirmed by post mortem SEM imaging.

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.

Comparison of numerical-integration-based methods for blood flow estimation in diffuse correlation spectroscopy

BACKGROUND AND OBJECTIVE

Diffuse correlation spectroscopy (DCS) is an optical blood flow monitoring technology that has been utilized in various biomedical applications. In signal processing of DCS, nonlinear fitting of the experimental data and the theoretical model can be a hindrance in real-time blood flow monitoring. As one of the approaches to resolve the issue, INISg1, the inverse of numerical integration of squared g1 (a normalized electric field autocorrelation function), that could surpass the state-of-the-art technique at the time in terms of signal processing speed, has been introduced. While it is possible to implement INISg1 using various numerical integration methods, no relevant studies have been performed. Meanwhile, INISg1 was only tested within limited experimental conditions, which cannot guarantee the robustness of INISg1 in various experimental conditions. Thus, this study aims to introduce variants of INISg1 and perform a thorough comparison of the original INISg1 and its variants.

METHODS

In this study, based on the right Riemann sum (RR) and trapezoid rule (TR) of numerical integration, INISg1_RR and INISg1_TR are suggested. They are thoroughly compared with the original INISg1 using model-based simulations that offer us control of most of the experimental conditions, including integration time, β, and photon count rate.

RESULTS

Except for some extreme cases, INISg1 performed more robustly than INISg1_RR and INISg1_TR. However, in extreme conditions, variants of INISg1 performed better than INISg1. With the same condition, the signal processing speed of INISg1 was 1.63 and 1.98 times faster than INISg1_RR and INISg1_TR, respectively.

CONCLUSION

This study shows that INISg1 is robust in most cases and the study can be a guide for researchers using INISg1 and its variants in different types of DCS applications.

Design and validation of a convolutional neural network for fast, model-free blood flow imaging with multiple exposure speckle imaging

Multiple exposure speckle imaging has demonstrated its improved accuracy compared to single exposure speckle imaging for relative quantitation of blood flow in vivo. However, the calculation of blood flow maps relies on a pixelwise non-linear fit of a multi-parametric model to the speckle contrasts. This approach has two major drawbacks. First, it is computer-intensive and prevents real time imaging and, second, the mathematical model is not universal and should in principle be adapted to the type of blood vessels. We evaluated a model-free machine learning approach based on a convolutional neural network as an alternative to the non-linear fit approach. A network was designed and trained with annotated speckle contrast data from microfluidic experiments. The neural network performances are then compared to the non-linear fit approach applied to in vitro and in vivo data. The study demonstrates the potential of convolutional networks to provide relative blood flow maps from multiple exposure speckle data in real time.

Quasi-analytic solution for real-time multi-exposure speckle imaging of tissue perfusion

Laser speckle contrast imaging (LSCI) is a widefield imaging technique that enables high spatiotemporal resolution measurement of blood flow. Laser coherence, optical aberrations, and static scattering effects restrict LSCI to relative and qualitative measurements. Multi-exposure speckle imaging (MESI) is a quantitative extension of LSCI that accounts for these factors but has been limited to post-acquisition analysis due to long data processing times. Here we propose and test a real-time quasi-analytic solution to fitting MESI data, using both simulated and real-world data from a mouse model of photothrombotic stroke. This rapid estimation of multi-exposure imaging (REMI) enables processing of full-frame MESI images at up to 8 Hz with negligible errors relative to time-intensive least-squares methods. REMI opens the door to real-time, quantitative measures of perfusion change using simple optical systems.

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 imaging is 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 the condition 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.

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.

Development and characterization of a chick embryo chorioallantoic membrane (CAM) based platform for evaluation of vasoactive medications

Among various anti-cancer therapies, tumor vascular disrupting agents (VDAs) play a crucial role, for which their off-targeting effects on normal vessels need also to be investigated. The purpose of this study was to set up an in-ovo platform that combines a laser speckle contrast imaging (LSCI) modality with chick embryo chorioallantoic membrane (CAM) to real-time monitor vascular diameters and perfusion without and with intravascular injection. Two eggshell windows for both observation or measurement and injection were opened. Dynamic blood perfusion images and corresponding statistic graphs were acquired by using a LSCI unit on CAMs from embryo date (ED) 9 to ED15. A dedicated fine needle catheter was made for slow intravascular administration over 30 min with simultaneous LSCI acquisition. To verify the connectivity between CAM vessels and the embryonic circulations in the egg, contrast-enhanced 3D micro computed tomography (μCT), 2D angiography and histology were executed. This platform was successfully established to acquire, quantify and demonstrate vascular and hemodynamic information from the CAM. Chick embryos even with air cell opened remained alive from ED9 to ED15. Through collecting LSCI derived CAM vascular diameter and perfusion parameters, ED12 was determined as the best time window for vasoactive drug studies. A reverse correlation between CAM vessel diameter and blood perfusion rate was found (p < 0.002). Intravascular infusion and simultaneous LSCI acquisition for 30 min in ovo proved feasible. Contrast-enhanced angiography and histomorphology could characterize the connectivity between CAM vasculature and embryonic circulation. This LSCI-CAM platform was proved effective for investigating the in-ovo hemodynamics, which paves the road for further preclinical research on vasoactive medications including VDAs.

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 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.

Laser Doppler Spectrum Analysis Based on Calculation of Cumulative Sums Detects Changes in Skin Capillary Blood Flow in Type 2 Diabetes Melitus

In this article, we introduce a new method of signal processing and data analysis for the digital laser Doppler flowmetry. Our approach is based on the calculation of cumulative sums over the registered Doppler power spectra. The introduced new parameter represents an integral estimation for the redistribution of moving red blood cells over the range of speed. The prototype of the device implementing the technique is developed and tested in preliminary clinical trials. The methodology was verified with the involvement of two age groups of healthy volunteers and in a group of patients with type 2 diabetes mellitus. The main practical result of the study is the development of a set of binary linear classifiers that allow the method to identify typical patterns of the microcirculation for the healthy volunteers and diabetic patients based on the presented diagnostic algorithm.

Machine learning for direct oxygen saturation and hemoglobin concentration assessment using diffuse reflectance spectroscopy

SIGNIFICANCE

Diffuse reflectance spectroscopy (DRS) is frequently used to assess oxygen saturation and hemoglobin concentration in living tissue. Methods solving the inverse problem may include time-consuming nonlinear optimization or artificial neural networks (ANN) determining the absorption coefficient one wavelength at a time.

AIM

To present an ANN-based method that directly outputs the oxygen saturation and the hemoglobin concentration using the shape of the measured spectra as input.

APPROACH

A probe-based DRS setup with dual source-detector separations in the visible wavelength range was used. ANNs were trained on spectra generated from a three-layer tissue model with oxygen saturation and hemoglobin concentration as target.

RESULTS

Modeled evaluation data with realistic measurement noise showed an absolute root-mean-square (RMS) deviation of 5.1% units for oxygen saturation estimation. The relative RMS deviation for hemoglobin concentration was 13%. This accuracy is at least twice as good as our previous nonlinear optimization method. On blood-intralipid phantoms, the RMS deviation from the oxygen saturation derived from partial oxygen pressure measurements was 5.3% and 1.6% in two separate measurement series. Results during brachial occlusion showed expected patterns.

CONCLUSIONS

The presented method, directly assessing oxygen saturation and hemoglobin concentration, is fast, accurate, and robust to noise.

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.

Laser speckle contrast analysis (LASCA) technology for the semiquantitative measurement of angiogenesis in in-ovo-tumor-model

BACKGROUND

The process of angiogenesis is a key element for tumor growth and proliferation and therefore one of the determining factors for aggressiveness and malignancy. A better understanding of the underlying processes of tumor induced angiogenesis is crucial for superior cancer treatment. Furthermore, the PeriCam perfusion speckle imager (PSI) system high resolution (HR) model by PERIMED presents a noninvasive method for semi-quantitative measurement of blood perfusion, based on laser speckle contrast analysis (LASCA). Aim of the present study was to utilize the chick chorioallantoic membrane (CAM) model as an in-ovo-tumor-model which enables rapid neovascularization of tumors while allowing real-time observation of the microcirculation via LASCA.

METHODS

Fertilized chicken eggs were grafted with embryonal/alveolar rhabdomyosarcoma cells or primary sarcoma tumors. The blood perfusion was measured before and after tumor growth using LASCA. The procedure is accelerated and simplified through the integrated PIMSoft software which provides real-time graphs and color-coded images during the measurement.

RESULTS

Sarcoma cells and primary sarcoma tumors exhibited satisfactory growth processes on the CAM. LASCA visualized microcirculation accurately and enabled an extensive investigation of the angiogenic potential of sarcoma cells on the CAM. We were able to show that sarcoma cells and primary sarcoma tumors induced larger quantities of neovasculature on the CAM than the controls.

CONCLUSIONS

The utilization of LASCA for the investigation of tumor angiogenesis within the CAM model appears to be a highly beneficial, cost-efficient and easily practicable procedure. The proposed model can be used as a drug-screening model for individualized cancer therapy, especially with regards to anti-angiogenic agents.