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Updated: Jun 25, 2025
In Vivo Fiber-Coupled Pre-Clinical Confocal Laser-scanning Endomicroscopy pCLE of Hippocampal Capillaries in Awake Mice
Published on: April 21, 2023
Distinguishing motion artifacts during optical fiber-based in-vivo hemodynamics recordings from brain regions of
Anupam Bisht1,2, Kathryn Simone1,2, Jaideep S Bains2,3
1University of Calgary, Biomedical Engineering Graduate Program, Calgary, Alberta, Canada.
Significance:
Motion artifacts in the signals recorded during optical fiber-based measurements can lead to misinterpretation of data. In this work, we address this problem during in-vivo rodent experiments and develop a motion artifacts correction (MAC) algorithm for single-fiber system (SFS) hemodynamics measurements from the brains of rodents.
Aim:
(i) To distinguish the effect of motion artifacts in the SFS signals. (ii) Develop a MAC algorithm by combining information from the experiments and simulations and validate it.
Approach:
Monte-Carlo (MC) simulations were performed across 450 to 790 nm to identify wavelengths where the reflectance is least sensitive to blood absorption-based changes. This wavelength region is then used to develop a quantitative metric to measure motion artifacts, termed the dissimilarity metric (DM). We used MC simulations to mimic artifacts seen during experiments. Further, we developed a mathematical model describing light intensity at various optical interfaces. Finally, an MAC algorithm was formulated and validated using simulation and experimental data.
Results:
We found that the 670 to 680 nm wavelength region is relatively less sensitive to blood absorption. The standard deviation of DM () can measure the relative magnitude of motion artifacts in the SFS signals. The artifacts cause rapid shifts in the reflectance data that can be modeled as transmission changes in the optical lightpath. The changes observed during the experiment were found to be in agreement to those obtained from MC simulations. The mathematical model developed to model transmission changes to represent motion artifacts was extended to an MAC algorithm. The MAC algorithm was validated using simulations and experimental data.
Conclusions:
We distinguished motion artifacts from SFS signals during in vivo hemodynamic monitoring experiments. From simulation and experimental data, we showed that motion artifacts can be modeled as transmission changes. The developed MAC algorithm was shown to minimize artifactual variations in both simulation and experimental data.
