Tracking of rigid head motion duringMRIusing an EEGsystem

Abstract

To demonstrate a novel method for tracking of head movements during MRI using electroencephalography (EEG) hardware for recording signals induced by native imaging gradients. Theory and Methods: Gradient switching during simultaneous EEG-fMRI induces distortions in EEG signals, which depend on subject head position and orientation. When EEG electrodes are interconnected with high-impedance carbon wire loops, the induced voltages are linear combinations of the temporal gradient waveform derivatives. We introduce head tracking based on these signals (CapTrack) involving 3 steps: (1) phantom scanning is used to characterize the target sequence and a fast calibration sequence; (2) a linear relation between changes of induced signals and head pose is established using the calibration sequence; and (3) induced signals recorded during target sequence scanning are used for tracking and retrospective correction of head movement without prolonging the scan time of the target sequence. Performance of CapTrack is compared directly to interleaved navigators. Results: Head-pose tracking at 27.5 Hz during echo planar imaging (EPI) was demonstrated with close resemblance to rigid body alignment (mean absolute difference: [0.14 0.38 0.15]-mm translation, [0.30 0.27 0.22]-degree rotation).Retrospective correction of 3D gradient-echo imaging shows an increase of average edge strength of 12%/−0.39% for instructed/uninstructed motion with Cap-Track pose estimates, with a tracking interval of 1561 ms and high similarity to interleaved navigator estimates (mean absolute difference: [0.13 0.33 0.12] mm, [0.28 0.15 0.22] degrees). Conclusion:Motion can be estimated from recordings of gradient switching with little or no sequence modification, optionally in real time at low computational burden and synchronized to image acquisition, using EEG equipment already found at many research institutions.

The MotoNet: A 3 Tesla MRI-Conditional EEG Net with Embedded Motion Sensors

“…In this setting, the results corresponded well with our theoretical formulas. We believe that the generalization to the other two remaining axes is straightforward, as demonstrated by Laustsen et al [ 22 ], which can be explored in future work. Because we used conventional EEG acquisition equipment with hardware lowpass filters, we were limited to relatively low-frequency stimulation gradients, which added time to the imaging sequence.…”

“…In general, this does not hold, as in Equation (11), gradients and positions occur in a multivariate relationship. In addition, we developed a rigid motion algorithm to specifically take advantage of the new sensors, which has the benefit of not requiring a subject–specific calibration [ 22 ]. Our method does assume that movements are small; however, this assumption is not significantly restrictive in practice, since one can analyze smaller time differences if needed to ensure that this condition holds.…”

“…Navigators and camera-based systems are commonly used to track and correct motion in real-time [ 15 ], but these require sequence changes or a clear line-of-sight to the patient in the head coil. Small pick-up coils rigidly attached to the patient’s head have been used previously for motion tracking [ 20 ], and others have shown that motion tracking is feasible during simultaneous EEG-fMRI using standard MRI conditional EEG amplifiers [ 21 ] or an EEG cap with additional loops and high-speed signal sampling [ 22 ]. However, these methods sense motion in the same EEG channel, further complicating the BCG artifact rejection.…”

Is High-Fidelity Important for Human-like Virtual Avatars in Human Computer Interactions?

“…This is because the existing motion capture systems are very expensive and require large spaces for installation, while most tracking systems only need a web camera. Also, some have bridged the gap by developing human facial expression synthesis systems [60] and facial expression reconstruction systems [61] via existing facial tracking systems, which opens the path for transferring the motion from a tracking system directly to a corresponded synthesis system. For instance, if a system wants to synthesis happy facial expressions for a virtual avatar, it can directly reconstruct the intended facial expression for that avatar by using the motion [62], which makes the capture of motion possible without using cameras, all be it only with a specialist head mounted display (HMD) that can detect one's brain signals.…”

Rapid and accurate navigators for motion and B0 tracking using QUEEN: Quantitatively enhanced parameter estimation from navigators

“…For both prospective and retrospective motion and $$$ \delta {\mathbf{B}}_{\mathbf{0}} $$$ correction, estimates of these time‐varying parameters are needed. As a first category, external tracking devices, with or without markers, have been proposed to externally track motion or $$$ \delta {\mathbf{B}}_{\mathbf{0}} $$$ at a very high temporal resolution 11–15 . As a second category, data‐driven estimation methods estimate changes in motion and $$$ \delta {\mathbf{B}}_{\mathbf{0}} $$$ by fitting a motion—and $$$ \delta {\mathbf{B}}_{\mathbf{0}} $$$—informed signal model to the acquired imaging data 2,7,16,17 .…”