Back

Establishing the Effect of Vascular Structure on Laser Speckle Contrast Imagining

Jafari, C. Z.; Sullender, C. T.; Miller, D. R.; Mihelic, S. A.; Dunn, A. K.

2020-06-27 bioengineering
10.1101/2020.06.25.172114 bioRxiv
Show abstract

Laser Speckle Contrast Imaging (LSCI) is a powerful tool for non-invasive, real-time imaging of blood flow in tissue. However, the effect of tissue geometry on the form of the electric field autocorrelation function and speckle contrast values is yet to be investigated. In this paper, we present an ultrafast forward model for simulating a speckle contrast image with the ability to rapidly update the image for a desired illumination pattern and flow perturbation. We demonstrate the first simulated speckle contrast image and compare it against experimental results. We simulate three mouse-specific cerebral cortex decorrelation time images and implement three different schemes for analyzing the effects of homogenization of vascular structure on correlation decay times. Our results indicate that dissolving structure and assuming homogeneous geometry creates up to ∼ 10x shift in the correlation function decay times and alters its form compared with the case for which the exact geometry is simulated. These effects are more pronounced for point illumination and detection imaging schemes. Further analysis indicates that correlated multiple scattering events, on average, account for 50% of all dynamic scattering events for a detector over a vessel region and 31% that of a detector over parenchyma region, highlighting the significance of accurate modeling of the three-dimensional vascular geometry for accurate blood flow estimates.Competing Interest StatementThe authors have declared no competing interest.View Full Text

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.