Journal List > J Korean Soc Magn Reson Med > v.16(1) > 1011863

TOOLS
Pae, Park, and Kim: Head Motion Detection and Alarm System during MRI scanning
Original Article
Journal of the Korean Society of Magnetic Resonance in Medicine

Journal of the Korean Society of Magnetic Resonance in Medicine 2012; 16(1): 55-66.

Published online: 30 April 2012

DOI: https://doi.org/10.0000/jksmrm.2012.16.1.55

Head Motion Detection and Alarm System during MRI scanning

Chongwon Pae1, 2, Hae-Jeong Park1, 2, Dae-Jin Kim2, 3

1Brain Korea 21 Project for Medical Science, Yonsei University, Seoul, Korea.

2Department of Radiology and Division of Nuclear Medicine, College of Medicine, Yonsei University, Korea.

3Psychological and Brain Sciences, Indiana University, 1101 East 10th Street, Bloomington Indiana 47401, USA.

Corresponding author: Dae-Jin Kim, Ph.D., Department of Radiology and Division of Nuclear Medicine, College of Medicine, Yonsei University, 134 Shinchon-dong, Sudaemoon-gu, Seoul 120-749, Korea. Tel. 82-2-2228-2363, Fax. 82-2-312-0578, daejkim@indiana.edu

Received 13 December 2011       Revised 13 April 2012       Accepted 17 April 2012

Copyright © 2012 Korean Society of Magnetic Resonance in Medicine

Abstract

Purpose

During brain MRI scanning, subject's head motion can adversely affect MRI images. To minimize MR image distortion by head movement, we developed an optical tracking system to detect the 3-D movement of subjects.

Materials and Methods

The system consisted of 2 CCD cameras, two infrared illuminators, reflective sphere-type markers, and frame grabber with desktop PC. Using calibration which is the procedure to calculate intrinsic/extrinsic parameters of each camera and triangulation, the system was desiged to detect 3-D coordinates of subject's head movement. We evaluated the accuracy of 3-D position of reflective markers on both test board and the real MRI scans.

Results

The stereo system computed the 3-D position of markers accurately for the test board and for the subject with glasses with attached optical reflective marker, required to make regular head motion during MRI scanning. This head motion tracking didn't affect the resulting MR images even in the environment varying magnetic gradient and several RF pulses.

Conclusion

This system has an advantage to detect subject's head motion in real-time. Using the developed system, MRI operator is able to determine whether he/she should stop or intervene in MRI acquisition to prevent more image distortions.

Keywords: Magnetic resonance imaging, Functional MRI, Head motion, Motion detection

References

1. Korean Society of Magnetic Resonance in Medicine. Magnetic Resonance Imaging. 2008. Ilchokak.
2. Allen ED, Bryd SE, Darling CF, et al. The clinical and radiological evaluation of primary brain neoplasm in children, Part II: radiological evaluation. J Natl Med Assoc. 1993. 85:546–553.
3. Deck MD, Hanschke C, Lee BC, et al. Computed tomography versus magnetic resonance imaging of the brain. Clin Imaging. 1989. 13:2–15.
4. Bandettini PA, Wong EC, Hinks RS, Tikofsky RS, Hyde JS. Time course EPI of human brain-function during task activation. Magn Reson Med. 1992. 25:390–397.
5. Ogawa S, Tank DW, Menon R, et al. Intrinsic signal changes accompanying sensory stimulation-functional brain mapping with magnetic-resonance-imaging. Proc Natl Acad Sci USA. 1992. 89:5951–5955.
6. Na SM, Park HJ, Chang Y. Low Frequency fluctuation component analysis in active stimulation fMRI paradigm. J Korean Soc Magn Reson Med. 2010. 14:115–120.
7. Sudhir G, Banerjee S, Zisserman A. Finding point correspondence in motion sequence preserving affine structure. Computer Vision and Image Understanding. 1997. 68:237–246.
8. Christpoulos VA, DeMuynck P, Cornelis J. Contour simplification for segmented stillimage and video coding: algorithms and experimental results. Signal Processing : Image Communication. 1999. 14:335–357.
9. Chen H, Ye D, et al. A technique for binocular stereo vision system calibration by the nonlinear optimization and calibration points with accurate coordinates. J Phys Conf Ser. 2006. 48.
10. Tsai RY. A versatile camera calibration technique for high accuracy 3D machine vision metrology using offtheshelf TV cameras and lenses. IEEE Jouenal of Robotics and Automation. 1987. RA-3(4):323–344.
11. Zhang Zhengyou. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002. Technical report, Microsoft Research.
12. Jean Yves B, Pietro P. Camera calibration from points and ones in dual-space geometry. European conference on computer vision. 1998.
13. Hartley RI, Zisserman A. Multiple view geometry in computer vision. 2000. Cambridge University Press;237–361.
14. Faugeras O. Three-dimensional computer vision: a geometric viewpoint. 1993. MIT Press.
15. Koks Don. A roundabout route to geometric algebra, explorations in Mathematical Physics. 2006. Springer Science+Business Media, LLC;144–183.
16. Chaurasia BD, Mathur BB. Eyedness. Acta Anat (Basel). 1976. 96(2):301–305.
17. SPM, Statistical Parametric Mapping. http://www.fil.ion.ucl.ac.uk/spm/.
18. Qin L, van Gelderen P, Derbyshire JA, et al. Prospective headmovement correction for high-resolution MRI using an in-bore optical tracking system. Magn Reson Med. 2009. 62:924–934.
19. Forman C, Aksoy M, Hornegger J, Bammer R. Self-encoded marker for optical prospective head motion correcetion in MRI. Med Image Anal. 2011. 15:708–719.
20. Zaitsev M, Dold C, Sakas G, et al. Magnetic resonance imaging of freely moving objects: prospective real-time motion correction using an external optical motion tracking system. Neuroimage. 2006. 31:1038–1050.
22. Maybank SJ, Faugeras OD. A theory of selfcalibration of a moving camera. The international Journal of Computer Vision. 1992. 8:123–152.
23. Luong Q-T, Faugeras O. Self-calibration of a moving camera from point correspondences and fundamental matrices. The International Journal of Computer Vision. 1997. 22:261–289.
24. Hartley RI. An algorithm for self calibration from several views. 1994. 06. In : In Proc. IEEE Conference on Computer Vision and Pattern Recognition; Seattle, WA. 908–912.
25. Bounoux S. From projective to euclidean space under any practical situation, a criticism of self-calibration. 1998. In : In Proc. 6th International Conference on Computer Vision; 790–796.
TOOLS
Similar articles