Head Motion Estimation Research Articles

A Deformation Field-Based Approach for Expression Processing in Head Motion Correction

It is a routine procedure to use Iterative Closest Point (ICP) algorithm to correct motion in medical imaging of the head. However, the basic assumption of ICP is the rigid body motion, and it is not strictly valid when there is non-rigid motion (facial expression). In this study, we propose a novel method to reduce the adverse effects of facial expressions on head motion estimation. First, we design a network named DFG-Net to generate a deformation field on the RGB domain and compute a confidence map. We then extend the original ICP by embedding the confidence map representing each point’s degree of non-rigid motion. Compared to other techniques, our proposed method eliminates the negative effects of facial expression while making the most of pose information. The experiments demonstrate that our approach reduces the position error by 19.23% and orientation error by 36.37% compared to the original ICP algorithm. They also show that our method is robust to variations of expressions.

An objective evaluation method for head motion estimation in PET—Motion corrected centroid-of-distribution

Head motion presents a continuing problem in brain PET studies. A wealth of motion correction (MC) algorithms had been proposed in the past, including both hardware-based methods and data-driven methods. However, in most real brain PET studies, in the absence of ground truth or gold standard of motion information, it is challenging to objectively evaluate MC quality. For MC evaluation, image-domain metrics, e.g., standardized uptake value (SUV) change before and after MC are commonly used, but this measure lacks objectivity because 1) other factors, e.g., attenuation correction, scatter correction and parameters used in the reconstruction, will confound MC effectiveness; 2) SUV only reflects final image quality, and it cannot precisely inform when an MC method performed well or poorly during the scan time period; 3) SUV is tracer-dependent and head motion may cause increases or decreases in SUV for different tracers, so evaluating MC effectiveness is complicated. Here, we present a new algorithm, i.e., motion corrected centroid-of-distribution (MCCOD) to perform objective quality control for measured or estimated rigid motion information. MCCOD is a three-dimensional surrogate trace of the center of tracer distribution after performing rigid MC using the existing motion information. MCCOD is used to inform whether the motion information is accurate, using the PET raw data only, i.e., without PET image reconstruction, where inaccurate motion information typically leads to abrupt changes in the MCCOD trace. MCCOD was validated using simulation studies and was tested on real studies acquired from both time-of-flight (TOF) and non-TOF scanners. A deep learning-based brain mask segmentation was implemented, which is shown to be necessary for non-TOF MCCOD generation. MCCOD is shown to be effective in detecting abrupt translation motion errors in slowly varying tracer distribution caused by the motion tracking hardware and can be used to compare different motion estimation methods as well as to improve existing motion information.

Optimizing a Feature-Based Motion Tracking System for Prospective Head Motion Estimation in MRI and PET/MRI

Patient head motion during magnetic resonance imaging (MRI) of the brain causes artifacts that limit diagnostic value. Prospective motion correction can, in principle, allow artifact-free imaging for any MRI sequence. Optical motion tracking systems are effective at measuring head motion for prospective motion correction but normally rely on attached markers, which are cumbersome and can move relative to the head. In this study, we optimized and benchmarked an optical motion tracking system that uses in-bore stereo-optical cameras to track feature patches on the forehead rather than attached markers. Feature patches were applied to the forehead in the form of an easily removable ink stamp or temporary tattoo. This approach has flexibility in camera positioning and allows tracking of a variety of surfaces on the human face, even those lacking prominent contours. This is advantageous given the limited space and line-of-sight with typical head coils. We optimized the accuracy and efficiency of two key components of the method: 1) the geometry of the stereo camera setup and 2) the feature detection and matching algorithm used for pose estimation. The absolute accuracy of the optimized feature-based method was compared with a commercial marker-based motion tracking system. Estimates of head motion of 15 volunteers simultaneously tracked by the two systems resulted in average positional discrepancies of less than 3 mm using a stamp and submillimeter average discrepancy using a feature-dense tattoo. These results suggest the feasibility of obtaining accurate head motion data for prospective motion compensated MRI using optimized feature-based motion tracking.

Correction of respiratory artifacts in MRI head motion estimates

Head motion represents one of the greatest technical obstacles in magnetic resonance imaging (MRI) of the human brain. Accurate detection of artifacts induced by head motion requires precise estimation of movement. However, head motion estimates may be corrupted by artifacts due to magnetic main field fluctuations generated by body motion. In the current report, we examine head motion estimation in multiband resting state functional connectivity MRI (rs-fcMRI) data from the Adolescent Brain and Cognitive Development (ABCD) Study and comparison ‘single-shot’ datasets. We show that respirations contaminate movement estimates in functional MRI and that respiration generates apparent head motion not associated with functional MRI quality reductions. We have developed a novel approach using a band-stop filter that accurately removes these respiratory effects from motion estimates. Subsequently, we demonstrate that utilizing a band-stop filter improves post-processing fMRI data quality. Lastly, we demonstrate the real-time implementation of motion estimate filtering in our FIRMM (Framewise Integrated Real-Time MRI Monitoring) software package.

A head motion estimation algorithm for motion artifact correction in dental CT imaging

A small head motion of the patient can compromise the image quality in a dental CT, in which a slow cone-beam scan is adopted. We introduce a retrospective head motion estimation method by which we can estimate the motion waveform from the projection images without employing any external motion monitoring devices. We compute the cross-correlation between every two successive projection images, which results in a sinusoid-like displacement curve over the projection view when there is no patient motion. However, the displacement curve deviates from the sinusoid-like form when patient motion occurs. We develop a method to estimate the motion waveform with a single parameter derived from the displacement curve with aid of image entropy minimization. To verify the motion estimation method, we use a lab-built micro-CT that can emulate major head motions during dental CT scans, such as tilting and nodding, in a controlled way. We find that the estimated motion waveform conforms well to the actual motion waveform. To further verify the motion estimation method, we correct the motion artifacts with the estimated motion waveform. After motion artifact correction, the corrected images look almost identical to the reference images, with structural similarity index values greater than 0.81 in the phantom and rat imaging studies.

Head Motion Estimation by Monocular SLAM for Endoscope Holding Robot

Head Motion Estimation by Monocular SLAM for Endoscope Holding Robot

Head motion correction based on filtered backprojection for x-ray CT imaging.

For head x-ray CT imaging, the head needs to remain motionless during the scan. In clinical practice, however, head motion is sometimes unavoidable depending on the patient. The motion can occur abruptly during the scan and can be unpredictable. It thereby causes motion artifacts such as tissue blurring or doubled edges around the skull area. To mitigate this problem, we propose a 3D head motion estimation (ME) and compensation algorithm based on filtered backprojection. If a patient moves his or her head during the scan, a motion-corrupted sinogram is obtained. Modeling the head motion as a 3D rigid transformation, we develop a motion-compensated (MC) reconstruction algorithm based on the FDK algorithm. To determine the head motion of a rigid transformation, we propose two optimization-based ME schemes depending on the degree of head motion, both of which are performed by updating motion parameters and the corresponding MC reconstructed image alternatively until the proposed cost function is minimized for the MC reconstructed image. In particular, to improve the robustness in the case of large motion, we propose attaching a fiducial marker to the head so that more reliable motion parameters can be initialized by determining the marker position, before the optimization. To evaluate the proposed algorithm, a numerical phantom with realistic, continuous, and smoothly varying motion, and a moving physical phantom are used with a gantry rotation time of 1s. In the simulation using a numerical phantom and in the experiment using a physical phantom, the proposed algorithm provides well-restored 3D motion-compensated images in both cases of small and large motion. In particular, in the case of large motion of the physical phantom, using a fiducial marker, we obtain remarkable improvement of image quality in cerebral arteries and a lesion as well as the skull. Quantitative evaluations using the image sharpness and root-mean-square error also show noticeable improvement of image quality in both simulations and experiments. We propose a framework for head motion correction in an axial CT scan, which consists of motion estimation and compensation steps. Two image-based ME algorithms for rigid motion tracking are developed according to the degree of head motion. The estimated motion information is then used for MC image reconstruction. Both motion estimation and compensation algorithms are based on computationally efficient filtered backprojection. Excellent performance of the proposed framework is illustrated by means of simulations using a numerical phantom and experiments using a physical phantom.

Facial model adaptation from a monocular image sequence using a textured polygonal model

Although several algorithms have been proposed for facial model adaptation from image sequences, the insufficient feature set to adapt a full facial model, imperfect matching of feature points, and imprecise head motion estimation may degrade the accuracy of model adaptation. In this paper, we propose to resolve these difficulties by integrating facial model adaptation, texture mapping, and head pose estimation as cooperative and complementary processes. By using an analysis-by-synthesis approach, salient facial feature points and head profiles are reliably tracked and extracted to form a growing and more complete feature set for model adaptation. A more robust head motion estimation is achieved with the assistance of the textured facial model. The proposed scheme is performed with image sequences acquired with single uncalibrated camera and requires only little manual adjustment in the initialization setup, which proves to be a feasible approach for facial model adaptation.