Multi-exposure Speckle Contrast Imaging Research Articles

Multiexposure laser speckle contrast analysis system calibration limited by perfusion-dependent scattering on the skin.

Application of multiexposure speckle contrast imaging (MESI) methods for perfusion measurements can correct for the contribution of static scattering of the skin, at the expense of reduced temporal resolution as compared to classical single-exposure methods. Persistence of tissue scattering properties during the measurements could allow for an initial calibration and enhancement of the temporal resolution of the measurements. We aim to study the influence of the perfusion on the light scattering of the forearm skin and to use the obtained data for the enhancement of the temporal resolution. A wide range of skin perfusion states was induced while monitoring the changes in the dynamic range of the exposure-dependent contrast. Different measurement and evaluation methods were tested based on an initial MESI calibration followed by image recording with reduced number of exposure time values. The changes in the skin perfusion can alter not only the contribution of the static scattering to the speckle images but also the short-exposure time contrast limit. The perfusion-dependent scattering of the skin can invalidate the precalibrations (e.g., calibration) characterizing the combination of the given tissue and the measurement system.

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.