Marker‐free optical stereo motion tracking for in‐bore MRI and PET‐MRI application

Abstract

Prospective motion correction is arguably the "silver bullet" solution for magnetic resonance imaging (MRI) studies impacted by motion, applicable to almost any pulse sequence and immune from the spin history artifacts introduced by a moving object. In prospective motion correction, the magnetic field gradients and radio frequency waveforms are adjusted in real time in response to measured head motion so as to maintain the head in a stationary reference frame relative to the scanner. Vital for this approach are accurate and rapidly sampled head pose measurements, which may be obtained optically using cameras. However, most optical methods are limited by the need to attach physical markers to the skin, which leads to decoupling of head and marker motion and reduces the effectiveness of correction. In this work we investigate the feasibility and initial performance of a stereo-optical motion tracking method which does not require any attached markers. Methods: The method relies on detecting distinctive natural features or amplified features (using skin stamps) directly on the forehead in multiple camera views, and then deriving pose estimates via a 3D-2D registration between the skin features and a database of forehead landmarks. To demonstrate the feasibility and potential accuracy of the marker-free method for discrete (step-wise) head motion, we performed out-of-bore and in-bore experiments using robotically and manually controlled phantoms in addition to in-bore testing on human volunteers. We also developed a convenient out-of-bore test bed to benchmark and optimize the motion tracking performance. Results: For out-of-bore phantom tests, the pose estimation accuracy (compared to robotic ground truth) was 0.14 mm and 0.23 degrees for incremental translation and rotation, respectively. For arbitrary motion, the pose accuracy obtained using the smallest forehead feature patch was equivalent to 0.21 AE 0.11 mm positional accuracy in the striatum. For in-bore phantom experiments, the accuracy of rigid-body motion parameters (compared to wireless MR-sensitive markers) was 0.

MR Imaging in the 21st Century: Technical Innovation over the First Two Decades

“…Recently, new motion detection technologies have been developed and become available for MRI motion correction. These devices perform the subject’s motion detection from a camera, 49 detecting coil load signal change by motion and B 0 field change monitoring. These advancements are expected to provide better motion robust acquisition technology.…”

Evaluation of Data-Driven Rigid Motion Correction in Clinical Brain PET Imaging

“…While patient motion with translations of up to 15 mm and rotations of up to 4 o are reported (1,2), smaller motions are quite common. Various motion tracking and correction techniques have been presented to account for head motion (2)(3)(4)(5)(6)(7). These usually use an external tracking device (such as a camera) to track a marker attached to the head (5) or directly track the head (6).…”

“…Various motion tracking and correction techniques have been presented to account for head motion (2)(3)(4)(5)(6)(7). These usually use an external tracking device (such as a camera) to track a marker attached to the head (5) or directly track the head (6). The motion estimates can then be used to perform frame-based reconstructions (8) or a full eventby-event motion corrected reconstruction (9,10).…”

Estimation of in-scanner head pose changes during structural MRI using a convolutional neural network trained on eye tracker video

“…Recent studies have utilized external optical signals or electromagnetic motion trackers to estimate changes in head pose, often with more than one camera or with markers attached to the study participant [9][10][11][12][13][14]. Our proposed approach is an attractive solution to the problem of head motion because eye trackers are widely available in clinical research centers and our technique does not require placing markers on the study participant.…”

“…These methods include prospective techniques that modify image acquisition parameters during scanning to reduce motion artifacts, and retrospective techniques that assess motion and correct for motion-related effects after imaging data was obtained [7, 8]. Recent studies have utilized external optical signals or electromagnetic motion trackers to estimate changes in head pose, often with more than one camera or with markers attached to the study participant [9–14]. Our proposed approach is an attractive solution to the problem of head motion because eye trackers are widely available in clinical research centers and our technique does not require placing markers on the study participant.…”