Mr. Anupam Bisht Profile

Anupam Bisht

Graduate Student

Area of Expertise:

Intrinsic Optical Imaging , Signal processing , Behavioral experiments , Deep brain stimulation , Motor system , Embedded system

Anupam Bisht is a PhD student in the Department of Biomedical Engineering at the University of Calgary, Canada. He completed his bachelor’s degree in electrical and electronics engineering at the Vellore Institute of Technology (VIT), Vellore, India. He is currently working on instrumentation to enable freely moving animal studies under the supervision of Dr. Kartikeya Murari.

SPIE Journal Paper | 24 May 2024 Open Access

Distinguishing motion artifacts during optical fiber-based in-vivo hemodynamics recordings from brain regions of freely moving rodents

Anupam Bisht, Kathryn Simone, Jaideep S. Bains, Kartikeya Murari

NPh, Vol. 11, Issue S1, S11511, (May 2024) https://doi.org/10.1117/12.10.1117/1.NPh.11.S1.S11511

KEYWORDS: Simulations, Hemodynamics, Optical fibers, Brain, Motion measurement, Backscatter, Tissues, Animals, Motion models, Algorithm development

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SignificanceMotion 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.ApproachMonte-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.ResultsWe found that the 670 to 680 nm wavelength region is relatively less sensitive to blood absorption. The standard deviation of DM (σDM) 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.ConclusionsWe 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.

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Proceedings Article | 13 March 2024 Presentation + Paper

System for simultaneous measurement of fluorescence and hemodynamics (SSMFH) from deep brain regions of freely moving rodents

Anupam Bisht, Govind Peringod, Grant Gordon, Jaideep Bains, Kartikeya Murari

Proceedings Volume 12828, 128280A (2024) https://doi.org/10.1117/12.3002776

KEYWORDS: Fluorescence, Hemodynamics, Brain, Light emitting diodes, Blood, Animals, Oxygen, Photometry, Design

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Proceedings Article | 4 March 2022 Presentation + Paper

Investigating presence of motion artifacts in the oxygen saturation signal during in-vivo fiber photometry

Anupam Bisht, Kathryn Simone, Govind Peringod, Grant Gordon, Jaideep Bains, Kartikeya Murari

Proceedings Volume 11946, 1194603 (2022) https://doi.org/10.1117/12.2609877

KEYWORDS: Reflectivity, Brain, Tissue optics, Mathematical modeling, Hemodynamics, Blood, Tissues, Oxygen, In vivo imaging, Photometry

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