Motion artifact correction of multi-photon imaging of awake mice models using speed embedded HMM

  • Authors:

  • Taoyi Chen;Zhong Xue;Changhong Wang;Zhenshen Qu;Kelvin K. Wong;Stephen T. C. Wong

  • Affiliations:

  • Center for Bioengineering and Informatics, Methodist Hospital Research Institute and Department of Radiology, The Methodist Hospital, Weill Cornell Medical College, Houston, TX and Department of C ...;Center for Bioengineering and Informatics, Methodist Hospital Research Institute and Department of Radiology, The Methodist Hospital, Weill Cornell Medical College, Houston, TX;Department of Control Science and Engineering, Harbin Institute of Technology, China;Department of Control Science and Engineering, Harbin Institute of Technology, China;Center for Bioengineering and Informatics, Methodist Hospital Research Institute and Department of Radiology, The Methodist Hospital, Weill Cornell Medical College, Houston, TX;Center for Bioengineering and Informatics, Methodist Hospital Research Institute and Department of Radiology, The Methodist Hospital, Weill Cornell Medical College, Houston, TX

  • Venue:
  • MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
  • Year:
  • 2010

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Abstract

Multi-photon fluorescence microscopy (MFM) captures highresolution anatomical and functional fluorescence image sequences and can be used for the intact brain imaging of small animals. Recently, it has been extended from imaging anesthetized and head-stabilized animals 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 tiny body movement can cause motion artifacts and prevent stable serial image acquisition at such a 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 HMM method by embedding a motion prediction model to better estimate the state transition probability. SEHMM is a line-by-line motion correction algorithm, which is implemented within the in-focal-plane 2-D videos and can operate directly on the motion-distorted imaging data without external signal measurements such as the movement, heartbeat, respiration, or muscular tension. In experiments, we demonstrat that SEHMM is more accurate than traditional HMM using both simulated and real MFM image sequences.