Self-encoded Marker for Optical Prospective Head Motion Correction in MRI

Abstract

Abstract. The tracking and compensation of patient motion during a magnetic resonance imaging (MRI) acqusition is an unsolved problem. For brain MRI, a promising approach recently suggested is to track the patient using an in-bore camera and a checkerboard marker attached to the patient's forehead. However, the possible tracking range of the head pose is limited by the locally attached marker that must be entirely visible inside the camera's narrow field of view (FOV). To overcome this shortcoming, we developed a novel self-encoded marker where each feature on the pattern is augmented with a 2-D barcode. Hence, the marker can be tracked even if it is not completely visible in the camera image. Furthermore, it offers considerable advantages over the checkerboard marker in terms of processing speed, since it makes the correspondence search of feature points and marker-model coordinates, which are required for the pose estimation, redundant. The motion correction with the novel self-encoded marker recovered a rotation of 18 • around the principal axis of the cylindrical phantom in-between two scans. After rigid registration of the resulting volumes, we measured a maximal error of 0.39 mm and 0.15 • in translation and rotation, respectively. In in-vivo experiments, the motion compensated images in scans with large motion during data acquisition indicate a correlation of 0.982 compared to a corresponding motion-free reference.

Prospective motion correction in functional MRI

“…MR-visible markers (Ooi et al, 2013a, 2011; Sengupta et al, 2014), where the detected motion is still in MR reference frame, but rigid coupling between the markers and the object is not necessarily present and additional measures need to be taken in order to ensure such coupling. Finally, for truly external motion tracking devices (Forman et al, 2010; Maclaren et al, 2012; Rotenberg et al, 2013; Schulz et al, 2012; Zaitsev et al, 2006), both marker fixation and coordinate frame matching need to be ensured. The latter is typically achieved by a cross-calibration procedure (Zahneisen et al, 2014b; Zaitsev et al, 2006).…”

Prospective active marker motion correction improves statistical power in BOLD fMRI

“…These methods aim to track the 6-df motions and correct the acquisition online. Broadly speaking they can be classified into image-based methods [46] which can only correct for inter-volume movements, navigator-based methods [11], which acquire extra data along various three-dimensional k-space trajectories to estimate the 6-df [35,51,52,48,53], and marker-based methods which follow in real time the position of external markers attached to the head either by optical tracking [54,42,36,14,27] or using MRI [10,55,13,12,25,7,31,32]. Our group has developed prospective active-marker motion correction (PRAMMO) for structural [31] and echo-planar brain scans and demonstrated potential advantages of the approach for functional imaging [32,33].…”

Echo‐planar imaging with prospective slice‐by‐slice motion correction using active markers

“…Cloverleaf navigators reduce the delay to ~ 18 ms, but require an initial 12 s reference map to be acquired while the patient is stationary (13). Alternatively, optical systems use cameras to track external markers (15-19); while there is no extra pulse-sequence load, this comes at the cost of an additional cross-calibration procedure to determine the coordinate transform between camera-space (where markers are tracked) and magnet-space (where scan-plane orientation is defined).…”