Synopsis

Head and brain motion is usually described as rigid with 6 degrees of freedom (translation and rotation). Optical tracking systems use cameras for pose tracking inside the MRI system to determine the 6 motion parameters with relatively high update rate. Optical motion tracking approaches include multiple cameras-based stereoscopic reconstruction of few markers incl. infrared tracking with cameras outside or inside the magnet bore. Alternatively, single camera approaches using encoded checkerboard markers moiré phase tracking (MPT) markers has been presented. Structured light is another alternative. These methods are now being commercialized.

TARGET AUDIENCE

OBJECTIVES

METHODS

Although most subjects can hold their head relatively still during an MRI exam (compared to abdominal imaging), even this part of the body is prone to motion artifacts. Artefacts arise from subject’s head motion either due to non-compliance or inability to hold still or due to unavoidable small physiological motion in particular for very high resolution scans. Since the brain is enclosed by the skull, motion is usually described as rigid with 6 degrees of freedom (translation and rotation). 6 motion parameters need to be known to correct for motion either prospectively or retrospectively.

Optical tracking systems use cameras for pose tracking inside the MRI system to determine the 6 motion parameters with relatively high update rate (according to the camera frame rate). A well-known optical motion tracking approach is based on multiple cameras and stereoscopic reconstruction of few markers attached to the object. A number of such systems are used in the motion picture industry or in surgery to track subject poses, devices or patient position. In 2005 Tremblay et al. (2005) applied an infrared tracking system within the MR environment. Two cameras placed at known positions outside the scanner bore detected three or four reflective markers. A similar approach was published by Zaitsev et al. (2006) using a stereoscopic infrared tracking system positioned outside the MR scanner and reflective markers. The main limitations of these methods are the large distance of the cameras from the tracking target and the small angle between the views of less than 20°. These reduce the possible accuracy of the tracking data, in particular the depth information. In addition, at least three markers are required to obtain 3D motion information.

To address this challenge Qin et al. (2009) mounted two cameras to the head coil and thus much closer to the markers and with larger view angle. Yet a rigid target with multiple markers was required. Schulz et al. (2012) extended this approach using three cameras mounted at close distance to the head, each viewing only one marker independently. Since then, single camera systems have gained interest due to their smaller footprint and simpler handling. With only one camera the requirements for line of sight are reduced and calibration is simplified. With a single camera, however, additional information needs to be encoded through the marker. A checkerboard, for example, contains many feature points with known geometry and was proposed by Aksoy et al. (2010) together with a single camera mounted to the head coil. The large self-encoded marker is visible to the camera for a large range of motion even at close distance. To resolve ambiguities in the regular pattern, each square of the checkerboard contains a binary code to encode position information of each square on the marker (see Forman et al. (2011)). The authors quote a tracking precision of ~0.1mm in translation and ~0.1° in rotation. Maclaren et al. (2012) introduced another single camera tracking system mounted inside the scanner bore tracking a single small 15mm marker. The moiré phase tracking (MPT) is based on moiré patterns generated by gratings on both sides of a transparent substrate. The phase of these patterns is generated by interference between the gratings and very sensitive to rotations. The accuracy reported by the authors is ~0.01mm in translation and ~0.01° in rotation.

Structured light was already proposed in 2008 by Zaremba et al. and has received more attention recently (Benjaminsen et al 2016) due to its first commercialization.

RESULTS

CONCLUSION

Acknowledgements

References

Aksoy, M., 2010. Real Time Prospective Motion--Correction II – Practical Solutions. In Current Concepts of Motion Correction for MRI & MRS; ISMRM Workshop Series

Benjaminsen C., et al. ISMRM 2016, 1860 Forman, C. et al., 2011. Self-encoded marker for optical prospective head motion correction in MRI. Medical Image Analysis, 15(5), pp. 708–719.

Godenschweger F., et al, 2016. Motion correction in MRI of the brain. Phys Med Biol, 61(5), pp.R32-56.

Maclaren, J. et al., 2012. Measurement and Correction of Microscopic Head Motion during Magnetic Resonance Imaging of the Brain. PLoS ONE, 7(11): e48088.

Qin, L. et al., 2009. Prospective head-movement correction for high-resolution MRI using an in-bore optical tracking system. Magnetic Resonance in Medicine, 62(4), pp.924–934.

Schulz, J. et al., 2012. An embedded optical tracking system for motion-corrected magnetic resonance imaging at 7T. Magnetic Resonance Materials in Physics, Biology and Medicine, 25(6), pp.443–453.

Tremblay, M., Tam, F. and Graham, S.J., 2005. Retrospective coregistration of functional magnetic resonance imaging data using external monitoring. Magnetic Resonance in Medicine, 53(1), pp.141–149.

Zaremba AA et al. 2008. Optical head tracking for functional magnetic resonance imaging using structured light. J Opt Soc Am A Opt Image Sci Vis. 2008 Jul;25(7):1551-7.

Zaitsev, M. et al., 2006. Magnetic resonance imaging of freely moving objects: prospective real-time motion correction using an external optical motion tracking system. Neuroimage, 31(3), pp.1038–1050.

Zaitsev, M., Maclaren, J. and Herbst, M., 2015. Motion artifacts in MRI: A complex problem with many partial solutions. Magnetic Resonance Imaging, Available at: http://dx.doi.org/10.1002/jmri.24850