Bisht Anupam, Simone Kathryn, Bains Jaideep S, Murari Kartikeya
University of Calgary, Biomedical Engineering Graduate Program, Calgary, Alberta, Canada.
University of Calgary, Hotchkiss Brain Institute, Calgary, Alberta, Canada.
Neurophotonics. 2024 Sep;11(Suppl 1):S11511. doi: 10.1117/1.NPh.11.S1.S11511. Epub 2024 May 24.
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 rodent experiments and develop a motion artifacts correction (MAC) algorithm for single-fiber system (SFS) hemodynamics measurements from the brains of rodents.
(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.
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.
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.
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.
基于光纤测量过程中记录的信号中的运动伪影可能导致数据误判。在这项工作中，我们在啮齿动物实验中解决了这个问题，并开发了一种用于从啮齿动物大脑进行单光纤系统（SFS）血流动力学测量的运动伪影校正（MAC）算法。
（i）区分SFS信号中运动伪影的影响。（ii）通过结合实验和模拟信息开发一种MAC算法并进行验证。
在450至790nm范围内进行蒙特卡罗（MC）模拟，以确定反射率对基于血液吸收的变化最不敏感的波长。然后使用该波长区域开发一种定量指标来测量运动伪影，称为差异度量（DM）。我们使用MC模拟来模拟实验中看到的松伪影。此外，我们开发了一个描述各种光学界面处光强度的数学模型。最后，制定了一种MAC算法，并使用模拟和实验数据进行了验证。
我们发现670至680nm波长区域对血液吸收相对不敏感。DM的标准差（）可以测量SFS信号中运动伪影的相对大小。伪影会导致反射率数据的快速变化，这可以建模为光光路中的传输变化。实验中观察到的变化与MC模拟获得的变化一致。为模拟传输变化以表示运动伪影而开发的数学模型扩展为一种MAC算法。使用模拟和实验数据对MAC算法进行了验证。
我们在体内血流动力学监测实验中区分了SFS信号中的运动伪影。从模拟和实验数据中，我们表明运动伪影可以建模为传输变化。所开发的MAC算法在模拟和实验数据中均显示出可最小化伪影变化。
