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Updated: May 29, 2026
Simultaneous Imaging of Microglial Dynamics and Neuronal Activity in Awake Mice
Published on: August 23, 2022
Motion correction for cellular-resolution multi-photon fluorescence microscopy imaging of awake head-restrained mice
Taoyi Chen1, Zhong Xue, Changhong Wang
1Department of Control Science and Engineering, Harbin Institute of Technology, Heilongjiang, China. taoyichen@gmail.com
Abstract:
Multi-photon fluorescence microscopy (MFM) captures high-resolution fluorescence image sequences and can be used for the intact brain imaging of small animals. Recently, it has been extended from anesthetized and head-stabilized mice to awake and head-restrained ones for in vivo neurological study. In these applications, motion correction is an important pre-processing step since brain pulsation and body movement can cause motion artifact and prevent stable serial image acquisition at such high spatial resolution. This paper proposes a speed embedded Hidden Markov model (SEHMM) for motion correction in MFM imaging of awake head-restrained mice. The algorithm extends the traditional Hidden Markov model (HMM) method by embedding a motion prediction model to better estimate the state transition probability. The novelty of the method lies in using adaptive probability to estimate the linear motion, while the state-of-the-art method assumes that the highest probability is assigned to the case with no motion. In experiments we demonstrated that SEHMM is more accurate than the traditional HMM using both simulated and real MFM image sequences.
Insights
This study introduces a Speed Embedded Hidden Markov Model (SEHMM) for correcting motion artifacts in multi-photon fluorescence microscopy (MFM) brain imaging of awake mice. SEHMM improves motion correction accuracy compared to traditional methods.
Area of Science:
- Neuroscience
- Biomedical Imaging
- Computational Biology
Background:
- Multi-photon fluorescence microscopy (MFM) enables high-resolution imaging of intact small animal brains.
- Recent advances allow MFM in awake, head-restrained mice for in vivo neurological studies.
- Motion artifacts from brain pulsation and movement degrade image quality and hinder analysis.
Purpose of the Study:
- To develop an advanced motion correction algorithm for MFM imaging of awake, head-restrained mice.
- To improve the accuracy and stability of serial image acquisition in high-resolution MFM.
Main Methods:
- A novel Speed Embedded Hidden Markov Model (SEHMM) was developed for motion correction.
- SEHMM incorporates a motion prediction model to enhance state transition probability estimation.
- The algorithm utilizes adaptive probability for linear motion estimation, unlike prior methods assuming no motion.
Main Results:
- SEHMM demonstrated superior accuracy in motion correction compared to the traditional Hidden Markov Model (HMM).
- Performance was validated using both simulated and real MFM image sequences.
- The proposed method effectively mitigates motion artifacts in MFM data.
Conclusions:
- SEHMM offers a more accurate and robust solution for motion correction in MFM imaging of awake, head-restrained mice.
- This advancement facilitates more reliable in vivo neurological studies using high-resolution brain imaging.
- The adaptive motion estimation provides a significant improvement over existing HMM-based approaches.
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