Head Motion Correction Devices
Tess Wallace1
1Siemens Medical Solutions USA Inc., Boston, MA, United States

Synopsis

Keywords: Image acquisition: Motion correction

This talk will provide an overview of sensors used for monitoring head motion in MRI. We will discuss how sensor information may be used to adapt the acquisition and/or reconstruction to improve image quality, as well as sensor requirements to accurately compensate for motion. Here, we classify external sensors into three broad categories – optical, electromagnetic, and mechanical, and discuss the principles by which each sensor measures motion. Finally, we will discuss the advantages and challenges of integrating external sensors with the MRI acquisition and future directions, including sensor fusion and the role of AI in improving sensor-based motion compensation.

Background and Motivation

  • Prevalence of head motion in MRI, impact on diagnostic image quality, economic impact, sedation rates (1-6)
  • Characterization of head movement as 6 DOF rigid body motion (position + orientation = pose)

Types of Sensors

Sensors may be broadly classified into three groups based on physical properties that they measure (7):
  • Optical: marker-based and marker-less, light and infrared
  • Electromagnetic: detection of magnetic field and tissue properties
  • Mechanical: acceleration, speed, torque

Integrating Sensors with MRI Acquisition and Reconstruction

  • Retrospective correction: sensor information is recorded and employed during image reconstruction to improve quality; rigid body motion changes described according to Fourier theorem (8)
  • Prospective correction: information from sensors can guide real-time decisions e.g., steering the acquisition (9, 10)
  • Detection of motion: sensor information is used to make reject/reacquire decisions e.g., functional MRI (11, 12)

Sensor Characteristics and Requirements

  • Accuracy: difference between true pose and measured pose (13)
  • Precision: level of jitter/noise in data
  • Frequency: rate at which tracking system generates pose estimates
  • Latency: delay between when motion occurs and availability of coordinate update
  • Ideal characteristics: high accuracy and precision, independent from sequence, no patient or technologist interaction (9)

Synchronization and Calibration

  • Time synchronization: need for a common clock
  • Coordinate transformation: transforming motion estimates from sensor coordinate system to coordinate frame of MRI scanner (14)
  • Calibration: transforming qualitative signals into quantitative measurements

Optical Sensors

  • Marker-based tracking: high contrast optical markers facilitate fast-automated landmark detection (15) using a single camera (16, 17) or stereoscopic camera system (18)
  • Marker-less tracking: leverages natural features on subject’s face to track motion in 3D using structured light to create a 3D point cloud that may be co-registered over time (19-21) or via landmark detection using stereovision (22)
  • Measurement considerations: compatibility between MR/camera system, cross-calibration, line of sight, non-rigid coupling (marker-based), low lighting in MR environment

Electromagnetic Sensors

Field detection methods: use MR gradient fields to localize device within scanner bore
  • Hall effect magnetometer: measures relative orientation with respect to B0 (23)
  • Tiny RF coils: detect signals from nearby spins; can solve for position (assuming known gradients) (24) or gradient fields (assuming known position e.g., field probes) (25)
  • Pick-up coils: short gradient pulses used to obtain position information from small coils placed on the subject to yield rigid body motion estimates (26)
  • Measurement considerations: requires scanner specific calibration and pulse sequence modification (unless native gradients used); wired vs. wireless techniques (27, 28)
Devices probing tissue properties: electrical properties of tissue change due to motion
  • Pilot tone: continuous wave RF source (frequency inside application bandwidth but outside imaging FOV), recorded by multiple detectors (RF coils); modulated by tissue properties (inductive coupling); quantitative motion estimation requires patient-specific calibration model (29-31)
  • Radar: measures reflections of RF waves when crossing tissue boundaries (dependent on angle of incidence, electromagnetic properties of tissues and size of wave) (32)

Mechanical Sensors

  • Basic principles: micro-electromechanical systems (MEMS) sensors rely on detecting forces acting on a mass (33); motion of mass relative to sensor frame can be used to detect acceleration (typically < 1 g; double integration to find position; gravity used to align sensor measurements to gradient coils) and/or angular rate (rate of change in orientation of the sensor frame) (34)
  • Measurement considerations: two integration steps lead to uncertainty in measurements; companion to displacement measurement techniques to track drifts

Advantages and Challenges of Sensors

  • Advantages: external devices typically do not affect image contrast or scan time; measurements largely independent of sequence with high temporal resolution
  • Challenges: marker attachment and positioning, not directly coupled to motion of the brain (skin motion, lever effect); need for patient/technologist interaction; motion affects coil loading/B0 field (susceptibility effects) (35); specialized hardware limits widespread availability

Outlook and Future Directions

  • Sensor fusion: hybrid devices (27, 36) and combination with navigator measurements e.g., optical tracking plus FID navigators for combined motion and B0 field estimation (37)
  • Role of AI: improving tracking data in marker-less tracking systems; correcting residual errors after prospective motion correction (38)

Acknowledgements

No acknowledgement found.

References

1. J. Andre et al., Toward Quantifying the Prevalence, Severity, and Cost Associated With Patient Motion During Clinical MR Examinations. Journal of the American College of Radiology : JACR 12, (2015).

2. J. Slipsager et al., Quantifying the Financial Savings of Motion Correction in Brain MRI: A Model-Based Estimate of the Costs Arising From Patient Head Motion and Potential Savings From Implementation of Motion Correction. Journal of magnetic resonance imaging : JMRI 52, (2020).

3. L. Wald, Ultimate MRI. Journal of magnetic resonance (San Diego, Calif. : 1997) 306, (2019).

4. C. Jaimes, M. Gee, Strategies to minimize sedation in pediatric body magnetic resonance imaging. Pediatric radiology 46, (2016).

5. S. Vanderby, P. Babyn, M. Carter, S. Jewell, P. McKeever, Effect of anesthesia and sedation on pediatric MR imaging patient flow. Radiology 256, (2010).

6. O. Afacan et al., Evaluation of motion and its effect on brain magnetic resonance image quality in children. Pediatric radiology 46, (2016).

7. B. Madore et al., External Hardware and Sensors, for Improved MRI. Journal of magnetic resonance imaging : JMRI 57, (2023).

8. F. Godenschweger et al., Motion correction in MRI of the brain. Physics in medicine and biology 61, (2016).

9. J. Maclaren, M. Herbst, O. Speck, M. Zaitsev, Prospective motion correction in brain imaging: a review. Magnetic resonance in medicine 69, (2013).

10. v. M. Zaitse, B. Akin, P. LeVan, B. Knowles, Prospective motion correction in functional MRI. NeuroImage 154, (2017).

11. N. Dosenbach et al., Real-time motion analytics during brain MRI improve data quality and reduce costs. NeuroImage 161, (2017).

12. R. Castella et al., Controlling motion artefact levels in MR images by suspending data acquisition during periods of head motion. Magnetic resonance in medicine 80, (2018).

13. J. Maclaren et al., Navigator accuracy requirements for prospective motion correction. Magnetic resonance in medicine 63, (2010).

14. J. Maclaren, M. Aksoy, M. Ooi, B. Zahneisen, R. Bammer, Prospective motion correction using coil-mounted cameras: Cross-calibration considerations. Magnetic resonance in medicine 79, (2018).

15. M. Zaitsev, C. Dold, G. Sakas, J. Hennig, O. Speck, Magnetic resonance imaging of freely moving objects: prospective real-time motion correction using an external optical motion tracking system. NeuroImage 31, (2006).

16. L. Qin et al., Prospective head-movement correction for high-resolution MRI using an in-bore optical tracking system. Magnetic resonance in medicine 62, (2009).

17. M. Aksoy et al., Real-time optical motion correction for diffusion tensor imaging. Magnetic resonance in medicine 66, (2011).

18. J. Schulz et al., An embedded optical tracking system for motion-corrected magnetic resonance imaging at 7T. Magma (New York, N.Y.) 25, (2012).

19. J. Slipsager et al., Markerless motion tracking and correction for PET, MRI, and simultaneous PET/MRI. PloS one 14, (2019).

20. J. Berglund et al., Prospective motion correction for diffusion weighted EPI of the brain using an optical markerless tracker. Magnetic resonance in medicine 85, (2021).

21. R. Frost et al., Markerless high-frequency prospective motion correction for neuroanatomical MRI. Magnetic resonance in medicine 82, (2019).

22. A. Kyme, M. Aksoy, D. Henry, R. Bammer, J. Maclaren, Marker-free optical stereo motion tracking for in-bore MRI and PET-MRI application. Medical physics 47, (2020).

23. J. Schell et al., Towards a Hall effect magnetic tracking device for MRI. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2013, (2013).

24. J. Derbyshire, G. Wright, R. Henkelman, R. Hinks, Dynamic scan-plane tracking using MR position monitoring. Journal of magnetic resonance imaging : JMRI 8, (1998).

25. A. Aranovitch et al., Prospective motion correction with NMR markers using only native sequence elements. Magnetic resonance in medicine 79, (2018).

26. O. Afacan, T. Wallace, S. Warfield, Retrospective correction of head motion using measurements from an electromagnetic tracker. Magnetic resonance in medicine 83, (2020).

27. A. van Niekerk, E. Meintjes, A. van der Kouwe, A Wireless Radio Frequency Triggered Acquisition Device (WRAD) for Self-Synchronised Measurements of the Rate of Change of the MRI Gradient Vector Field for Motion Tracking. IEEE transactions on medical imaging 38, (2019).

28. M. Ooi, M. Aksoy, J. Maclaren, R. Watkins, R. Bammer, Prospective motion correction using inductively coupled wireless RF coils. Magnetic resonance in medicine 70, (2013).

29. H. YT, P. Speier, T. Hilbert, T. Kober, in ISMRM. (London, UK, 2022).

30. T. Wallace et al., in ISMRM. (Toronto, Canada, 2023).

31. T. Wilkinson et al., in ISMRM. (London, UK, 2022), pp. 1955.

32. F. Thiel, M. Hein, U. Schwarz, J. Sachs, F. Seifert, Combining magnetic resonance imaging and ultrawideband radar: a new concept for multimodal biomedical imaging. The Review of scientific instruments 80, (2009).

33. X. Ru, N. Gu, H. Shang, H. Zhang, MEMS Inertial Sensor Calibration Technology: Current Status and Future Trends. Micromachines 13, (2022).

34. A. van Niekerk, A. van der Kouwe, E. Meintjes, A method for measuring orientation within a magnetic resonance imaging scanner using gravity and the static magnetic field (VectOrient). IEEE transactions on medical imaging 36, (2017).

35. J. Liu, J. de Zwart, P. van Gelderen, J. Murphy-Boesch, J. Duyn, Effect of head motion on MRI B0 field distribution. Magnetic resonance in medicine 80, (2018).

36. A. van Niekerk, A. van der Kouwe, E. Meintjes, Toward "plug and play" prospective motion correction for MRI by combining observations of the time varying gradient and static vector fields. Magnetic resonance in medicine 82, (2019).

37. L. Vaculčiaková et al., Combining navigator and optical prospective motion correction for high-quality 500 μm resolution quantitative multi-parameter mapping at 7T. Magnetic resonance in medicine 88, (2022).

38. J. Maclaren, in Motion Correction in MR, A. van der Kouwe, J. Andre, Eds. (2022), pp. 197-208.

Proc. Intl. Soc. Mag. Reson. Med. 31 (2023)