Motion Compensation Scheme Research Articles

Diffusion-weighted MR spectroscopy of the prostate.

Prostate tissue has a complex microstructure, mainly composed of epithelial and stromal cells, and of extracellular (acinar-luminal) spaces. Diffusion-weighted MR spectroscopy (DW-MRS) is ideally suited to explore complex microstructure in vivo with metabolites selectively distributed in different subspaces. To date, this technique has been applied to brain and muscle. This study presents the development and pioneering utilization of 1H-DW-MRS in the prostate, accompanied by in vitro studies to support interpretations of in vivo findings. Nine healthy volunteers underwent a prostate MR examination (mean age, 56 years; range, 31-66). Metabolic complexation was studied in vitro using solutions with major compounds found in prostatic fluid of the lumen. DW-MRS was performed at 3 T with a non-water-suppressed single-voxel sequence with metabolite-cycling to concurrently measure metabolite and water signals. The water signal was used in postprocessing as a reference in a motion-compensation scheme. The spectra were fitted simultaneously in the spectral and diffusion-weighting dimensions. Apparent diffusion coefficients (ADCs) were derived by fitting signal decays that were assumed to be mono-exponential for metabolites and biexponential for water. DW-MRS of the prostate revealed relatively low ADCs for Cho and Cr compounds, aligning with their intracellular location and higher ADCs for citrate and spermine supporting their luminal origin. In vitro assessments of the ADCs of citrate and spermine demonstrated their complex formation and protein binding. Tissue concentrations of MRS-detectable metabolites were as expected for the voxel location. This work successfully demonstrates the feasibility of 1H-DW-MRS of the prostate and its potential for providing valuable microstructural information.

A Motion Compensation Scheme for Compressed Sensing SAR Image Restoration Using Measured Antenna Phase Center Data

A Motion Compensation Scheme for Compressed Sensing SAR Image Restoration Using Measured Antenna Phase Center Data

Rapid motion estimation and correction using self-encoded FID navigators in 3D radial MRI.

To develop a self-navigated motion compensation strategy for 3D radial MRI that can compensate for continuous head motion by measuring rigid body motion parameters with high temporal resolution from the central k-space acquisition point (self-encoded FID navigator) in each radial spoke. A forward model was created from low-resolution calibration data to simulate the effect of relative motion between the coil sensitivity profiles and the underlying object on the self-encoded FID navigator signal. Trajectory deviations were included in the model as low spatial-order field variations. Three volunteers were imaged at 3 T using a modified 3D gradient-echo sequence acquired with a Kooshball trajectory while performing abrupt and continuous head motion. Rigid body-motion parameters were estimated from the central k-space signal of each spoke using a least-squares fitting algorithm. The accuracy of self-navigated motion parameters was assessed relative to an established external tracking system. Quantitative image quality metrics were computed for images with and without retrospective correction using external and self-navigated motion measurements. Self-encoded FID navigators achieved mean absolute errors of 0.69 ± 0.82 mm and 0.73 ± 0.87° relative to external tracking for maximum motion amplitudes of 12 mm and 10°. Retrospective correction of the 3D radial data resulted in substantially improved image quality for both abrupt and continuous motion paradigms, comparable to external tracking results. Accurate rigid body motion parameters can be rapidly obtained from self-encoded FID navigator signals in 3D radial MRI to continuously correct for head movements. This approach is suitable for robust neuroanatomical imaging in subjects that exhibit patterns of large and frequent motion.

Motion-compensated scheme for sequential scanned statistical iterative dual-energy CT reconstruction

Objective. Dual-energy computed tomography (DECT) has been widely used to reconstruct numerous types of images due its ability to better discriminate tissue properties. Sequential scanning is a popular dual-energy data acquisition method as it requires no specialized hardware. However, patient motion between two sequential scans may lead to severe motion artifacts in DECT statistical iterative reconstructions (SIR) images. The objective is to reduce the motion artifacts in such reconstructions. Approach. We propose a motion-compensation scheme that incorporates a deformation vector field into any DECT SIR. The deformation vector field is estimated via the multi-modality symmetric deformable registration method. The precalculated registration mapping and its inverse or adjoint are then embedded into each iteration of the iterative DECT algorithm. Main results. Results from a simulated and clinical case show that the proposed framework is capable of reducing motion artifacts in DECT SIRs. Percentage mean square errors in regions of interest in the simulated and clinical cases were reduced from 4.6% to 0.5% and 6.8% to 0.8%, respectively. A perturbation analysis was then performed to determine errors in approximating the continuous deformation by using the deformation field and interpolation. Our findings show that errors in our method are mostly propagated through the target image and amplified by the inverse matrix of the combination of the Fisher information and Hessian of the penalty term. Significance. We have proposed a novel motion-compensation scheme to incorporate a 3D registration method into the joint statistical iterative DECT algorithm in order to reduce motion artifacts caused by inter-scan motion, and successfully demonstrate that interscan motion corrections can be integrated into the DECT SIR process, enabling accurate imaging of radiological quantities on conventional SECT scanners, without significant loss of either computational efficiency or accuracy.

Coherent range-dependent map-drift algorithm for improving SAR motion compensation

Synthetic aperture radar (SAR) is usually sensitive to trajectory deviations that cause serious motion error in the recorded data. In this paper, a coherent range-dependent mapdrift (CRDMD) algorithm is developed to accommodate the range-variant motion errors. By utilizing the algorithm as an estimate core, robust motion compensation strategy is proposed for unmanned aerial vehicle (UAV) SAR imagery. CRDMD outperforms the conventional map-drift algorithms in both accuracy and efficiency. Real data experiments show that the proposed approach is appropriate for precise motion compensation for UAV SAR.

Ray-Space Motion Compensation for Lenslet Plenoptic Video Coding.

Plenoptic images and videos bearing rich information demand a tremendous amount of data storage and high transmission cost. While there has been much study on plenoptic image coding, investigations into plenoptic video coding have been very limited. We investigate the motion compensation (or so-called temporal prediction) for plenoptic video coding from a slightly different perspective by looking at the problem in the ray-space domain instead of in the conventional pixel domain. Here, we develop a novel motion compensation scheme for lenslet video under two sub-cases of ray-space motion, that is, integer ray-space motion and fractional ray-space motion. The proposed new scheme of light field motion-compensated prediction is designed such that it can be easily integrated into well-known video coding techniques such as HEVC. Experimental results compared to relevant existing methods have shown remarkable compression efficiency with an average gain of 20.03% and 21.76% respectively under "Low delayed B " and "Random Access" configurations of HEVC.

Motion-Compensated Predictive RAHT for Dynamic Point Clouds.

We study the use of predictive approaches alongside the region-adaptive hierarchical transform (RAHT) in attribute compression of dynamic point clouds. The use of intra-frame prediction with RAHT was shown to improve attribute compression performance over pure RAHT and represents the state-of-the-art in attribute compression of point clouds, being part of MPEG's geometry-based test model. We studied a combination of inter-frame and intra-frame prediction for RAHT for the compression of dynamic point clouds. An adaptive zero-motion-vector (ZMV) scheme and an adaptive motion-compensated scheme are developed. The simple adaptive ZMV approach is able to achieve sizable gains over pure RAHT and over the intra-frame predictive RAHT (I-RAHT) for point clouds with little or no motion while ensuring similar compression performance to I-RAHT for point clouds with intense motion. The motion-compensated approach, more complex and more powerful, is able to achieve large gains across all of the tested dynamic point clouds.

A W-Band Active Phased Array Miniaturized Scan-SAR with High Resolution on Multi-Rotor UAVs

The smart unmanned aerial vehicle (UAV) with a mini-SAR payload provides an advanced earth observation capability for target detection and imaging. Simiar to large-scale SAR, mini-SAR also has an increasing requirement for high resolution and wide swath. However, due to the low cruising altitude of UAVs, small coverage angles in the direction range, and the insufficient operating range of mini-SAR, expanding the swath has become an urgent problem for mini-SAR. To solve this problem, this paper proposes a W-Band active phased array miniaturized SAR (APA mini-SAR), whose scanning capacity has been proven on the multi-rotor UAV platform. Many efforts, including the novel active phased array antenna scheme, the sparse triangular arrangement of antenna elements, the wideband chirp source, and the three-dimensional integration technology, have been made to develop this APA mini-SAR for the first time in the W-Band. The volume of this APA mini-SAR is 69 × 82 × 87 mm3, with a weight of 600 g. Combined with a new motion compensation strategy and the ω-k imaging algorithm, the focused image is finally obtained. Experiments have been conducted, and the results indicate that this APA mini-SAR has a resolution of 4.5 cm, the imaging swath is three times that of the traditional single-channel mini-SAR, and the operating range is increased to 800 m.

A novel motion planning algorithm for a three DoF foldable parallel compensation platform based on prediction and B-spline

Foldable parallel mechanisms are suitable to compensate for the motion of vessels due to their compact structure, multidimensional motion, and large carrying capacity. However, the vessel sways violently under harsh sea conditions, challenging the rational utilization of the mechanism workspace. This work proposes a novel motion planning algorithm based on motion prediction and the B-spline for a three-degrees-of-freedom foldable parallel compensation platform. First, for harsh sea conditions, the motion law of a guardian vessel is studied, and the encoder-decoder model is used to predict the roll, pitch, and heave motions of the vessel. Second, we propose a multidimensional motion compensation strategy under the limits of the mechanism dimensions. Then, a backward kinematics model is developed. Its workspace under the structural and actuator limitations is proposed. Finally, based on the theory of the nonuniform rational B-spline curve and target motion prediction, a dynamic motion planning algorithm is proposed, and experiments are conducted. The motion planning algorithm can provide smooth motion commands under the mechanism workspace, velocity, acceleration constraints. It can be realized using asynchronous calculation, which meets real-time motion control requirements.

SL Sensor: An open-source, real-time and robot operating system-based structured light sensor for high accuracy construction robotic applications

High accuracy 3D surface information is required for many construction robotics tasks such as automated cement polishing or robotic plaster spraying. However, consumer-grade depth cameras currently found in the market are not accurate enough for these tasks where millimeter (mm)-level accuracy is required. This paper presents SL Sensor, a structured light sensing solution capable of producing high fidelity point clouds at 5 Hz by leveraging on phase shifting profilometry (PSP) codification techniques. The SL Sensor was compared with to two commercial depth cameras - the Azure Kinect and RealSense L515. Experiments showed that the SL Sensor surpasses the two devices in both precision and accuracy for indoor surface reconstruction applications. Furthermore, to demonstrate SL Sensor's ability to be a structured light sensing research platform for robotic applications, a motion compensation strategy was developed that allows the SL Sensor to operate during linear motion when traditional PSP methods only work when the sensor is static. Field experiments show that the SL Sensor is able to produce highly detailed reconstructions of spray plastered surfaces. The robot operating system (ROS)-based software and a sample hardware build of the SL Sensor are made open-source with the objective to make structured light sensing more accessible to the construction robotics community.

Macromolecular background signal and non-Gaussian metabolite diffusion determined in human brain using ultra-high diffusion weighting.

PurposeDefinition of a macromolecular MR spectrum based on diffusion properties rather than relaxation time differences and characterization of non‐Gaussian diffusion of brain metabolites with strongly diffusion‐weighted MR spectroscopy.MethodsShort echo time MRS with strong diffusion‐weighting with b‐values up to 25 ms/μm2 at two diffusion times was implemented on a Connectom system and applied in combination with simultaneous spectral and diffusion decay modeling. Motion‐compensation was performed with a combined method based on the simultaneously acquired water and a macromolecular signal.ResultsThe motion compensation scheme prevented spurious signal decay reflected in very small apparent diffusion constants for macromolecular signal. Macromolecular background signal patterns were determined using multiple fit strategies. Signal decay corresponding to non‐Gaussian metabolite diffusion was represented by biexponential fit models yielding parameter estimates for human gray matter that are in line with published rodent data. The optimal fit strategies used constraints for the signal decay of metabolites with limited signal contributions to the overall spectrum.ConclusionThe determined macromolecular spectrum based on diffusion properties deviates from the conventional one derived from longitudinal relaxation time differences calling for further investigation before use as experimental basis spectrum when fitting clinical MR spectra. The biexponential characterization of metabolite signal decay is the basis for investigations into pathologic alterations of microstructure.

Dynamic imaging using motion-compensated smoothness regularization on manifolds (MoCo-SToRM)

Objective. We introduce an unsupervised motion-compensated reconstruction scheme for high-resolution free-breathing pulmonary magnetic resonance imaging. Approach. We model the image frames in the time series as the deformed version of the 3D template image volume. We assume the deformation maps to be points on a smooth manifold in high-dimensional space. Specifically, we model the deformation map at each time instant as the output of a CNN-based generator that has the same weight for all time-frames, driven by a low-dimensional latent vector. The time series of latent vectors account for the dynamics in the dataset, including respiratory motion and bulk motion. The template image volume, the parameters of the generator, and the latent vectors are learned directly from the k-t space data in an unsupervised fashion. Main results. Our experimental results show improved reconstructions compared to state-of-the-art methods, especially in the context of bulk motion during the scans. Significance. The proposed unsupervised motion-compensated scheme jointly estimates the latent vectors that capture the motion dynamics, the corresponding deformation maps, and the reconstructed motion-compensated images from the raw k-t space data of each subject. Unlike current motion-resolved strategies, the proposed scheme is more robust to bulk motion events during the scan.

Design, structural modeling, control, and performance of 20 MW spar floating wind turbines

As floating wind turbines (FWTs) increase in size and power, the relative contribution of wave and wind loads to their global responses differs from what has been observed for 5–10 MW units. In addition, the larger deflections at the platform, increased natural period range for some degrees of freedom, and larger RNA weight at higher heights invite a review on structural modeling methods, design constraints, dynamic analysis, and control systems. This paper explores these topics through the design and structural analysis of three spar-type 20 MW FWTs, with different constraints on the static pitch angle at rated wind speed. Time-domain simulations are performed with a non-linear aero-hydro-servo-elastic software, and sectional fatigue damage and extreme motions and axial stresses for the three designs are compared. The platform is modeled as a flexible body, with hydrodynamic loads evaluated with potential theory and distributed over the hull. A control system with a motion compensation strategy is adopted, ensuring the same controller bandwidth for the three FWTs and showing significant performance improvements compared to detuning the controller gains. In addition to impacting steel and ballast mass, the static pitch angle at rated thrust affects the platform dynamics and fatigue damage/extreme loads significantly. The platforms with larger restoring in pitch present less fatigue damage at the platform, but more at the tower. Extreme stresses are largely affected by gravitational loads, such that the designs with larger pitch at rated thrust have the highest extreme stresses at the platform and most of the tower sections. Load cases associated with the rated wind speed often govern the extreme loads, unlike previous studies with 5 MW and 10 MW FWTs.

LiDAR Point Cloud Compression by Vertically Placed Objects Based on Global Motion Prediction

A point cloud acquired through a Light Detection And Ranging (LiDAR) sensor can be illustrated as a continuous frame with a time axis. Since the frame-by-frame point cloud has a high correlation between frames, a higher compression efficiency can be obtained by using an inter-prediction scheme, and for this purpose, Geometry-based Point Cloud Compression (G-PCC) in the Moving Picture Expert Group (MPEG) opened Inter-Exploratory Model (Inter-EM) which experiments on continuous LiDAR based point cloud frames compression through inter-prediction. The points of the LiDAR based point cloud have two different types of motion: global motion brought about by a vehicle with a LiDAR sensor and local motion generated by an object e.g., a walking person. Thus, Inter-EM consists of a compression structure in terms of both global and local motion, and the Inter-EM’s global motion compensation technology increases the compression efficiency via a single matrix describing the global motion of points. However, this is difficult to predict with a single matrix, which causes imprecise global motion estimation since the objects in a LiDAR-based point cloud show different global motion estimates according to object characteristics such as shape and position. Therefore, this paper proposes a global motion prediction and compensation scheme that considers the characteristics of objects for efficient compression of LiDAR-based point cloud frames. The proposed global motion prediction and compensation scheme achieved maximum gain of −22.0% and average of −9.4% in terms of the Bjontegaard-Delta-rate (BD-rate), and effectively compressed the LiDAR-based sparse point cloud.

Measuring radiofrequency field-induced temperature variations in brain MRI exams with motion compensated MR thermometry and field monitoring.

An MR thermometry (MRT) method with motion and field fluctuation compensation is proposed to measure non-invasively sub-degree brain temperature variations occurring through radiofrequency (RF) power deposition during MR exams. MRT at 7T with a multi-slice echo planar imaging (EPI) sequence and concurrent field monitoring was first tested in vitro to assess accuracy in the presence of external field perturbations, an optical probe being used for ground truth. In vivo, this strategy was complemented by a motion compensation scheme based on a dictionary pre-scan, as reported in some previous work, and was adapted to the human brain. Precision reached with this scheme was assessed on eight volunteers with a 5 minute-long low specific absorption rate (SAR) scan. Finally, temperature rise in the brain was measured twice on the same volunteers and with the same strategy, this time by employing a 20-minutes scan at the maximum SAR delivered with a commercial volume head coil. In vitro, the root mean square (RMS) error between optical probe and MRT measurements was 0.02°C with field sensor correction. In vivo, the low SAR scan returned a precision in temperature change measurement with field monitoring and motion compensation of 0.05°C. The 20-minutes maximum SAR scan returned a temperature rise throughout the inner-brain in the range of 0-0.2°C. Brain periphery remained too sensitive with respect to motion to lead to equally conclusive results. Sub-degree temperature rise in the inner human brain was characterized experimentally throughout RF exposure. Potential applications include improvement of human thermal models and revision of safety margins.

Gait monitoring system for patients with Parkinson’s disease

BackgroundWearable monitoring devices based on inertial sensors have the potential to be used as a quantitative method in clinical practice for continuous assessment of gait disabilities in Parkinson’s disease (PD). MethodsThis manuscript introduces a new gait monitoring system adapted to patients with PD, based on a wearable monitoring device. To eliminate inter- and intra-subject variability, the computational method was based on heuristic rules with adaptive thresholds and ranges and a motion compensation strategy. The experimental trials involved repeated measurements of walking trials from two cross-sectional studies: the first study was performed in order to validate the effectiveness of the system against a robust 3D motion analysis with 10 healthy subjects; and the second-one aimed to validate our approach against a well-studied wearable IMU-based system on a hospital environment with 20 patients with PD. ResultsThe proposed system proved to be efficient (Experiment I: sensitivity = 95,09% and accuracy = 93,64%; Experiment II: sensitivity = 99,53% and accuracy = 97,42%), time-effective (Experiment I: earlies = 13,71 ms and delays = 12,91 ms; Experiment II: earlies = 12,94 ms and delays = 12,71 ms), user and user-motion adaptable and a low computational-load strategy for real-time gait events detection. Further, it was measured the percentage of absolute error classified with a good acceptability (Experiment I: 3,02 ≤ ε%≤12,94; Experiment II: 2,81 ≤ ε%≤13,45). Lastly, regarding the measured gait parameters, it was observed a reflection of the typical levels of motor impairment for the different disease stages. ConclusionThe achieved outcomes enabled to verify that the proposed system can be suitable for gait analysis in the assistance and rehabilitation fields.

Vessel Target Echo Characteristics and Motion Compensation for Shipborne HFSWR under Non-Uniform Linear Motion

For shipborne high-frequency surface wave radar (HFSWR), the movement of the ship has a great impact on the radar echo, thus affecting target detection performance. In this paper, the characteristics of the target echo spectrum and the motion compensation methods for shipborne HFSWR are investigated. Firstly, simulation analysis of echo from a moving target under different ship motion conditions was conducted with a focus on the frequency shift and broadening characteristics of the target echo spectrum. The simulation results show that the non-uniform linear motion and yaw of the ship will shift and broaden the target echoes, resulting in signal-to-noise ratio (SNR) reduction. When the ship velocity and yaw angle change periodically, false target echo peaks will appear in the echo spectrum, which will reduce the accuracy of target detection. To tackle this problem, a motion compensation scheme for the target echo is proposed, including the heading compensation for the effect of yaw and the velocity compensation for non-uniform movement. The influence of the velocity and yaw angle measurement accuracy on the compensation results is also analyzed. Finally, the target echo characteristics and motion compensation method of shipborne HFSWR are verified with experimental data.

A Novel Motion Compensation Scheme for Airborne Very High Resolution SAR

Due to the atmospheric turbulence, the motion trajectory of airborne very high resolution (VHR) synthetic aperture radars (SARs) is inevitably affected, which introduces range-variant range cell migration (RCM) and aperture-dependent azimuth phase error (APE). Both types of errors consequently result in defocused images, as residual range- and aperture-dependent motion errors are significant in VHR-SAR images. Nevertheless, little work has been devoted to the range-variant RCM auto-correction and aperture-dependent APE auto-correction. In this paper, a precise motion compensation (MoCo) scheme for airborne VHR-SAR is studied. In the proposed scheme, the motion error is obtained from inertial measurement unit and SAR data, and compensated for with respect to both range and aperture. The proposed MoCo scheme compensates for the motion error without space-invariant approximation. Simulations and experimental data from an airborne 3.6 GHz bandwidth SAR are employed to demonstrate the validity and effectiveness of the proposed MoCo scheme.

Image Motion of Remote Sensing Camera With Wide Field of View Over the Antarctic and Arctic

Imaging by the remote sensing camera with a wide field of view (WFV) is of great significance in the issue of polar environment. However, the instability of the image motion velocity poses a huge challenge to the observation task since the direction of the earth's rotation velocity keeps changing in the polar regions. The edge blur of polar image by the camera with the WFV is usually ignored. Therefore, a specific theoretical model that the camera images over the polar region is supposed to describe the instability distinctly. Mathematical expressions of the image motion velocity field are obtained with a novel method of velocity projection and coordinate transformation. The quantitative analysis of simulation reveals that the increasing anisotropy of the instantaneous image motion velocity field gets most significant near the poles. The value of modulation transfer function at the edge of the field of view decreased by 0.33%, which results in a discrepancy of sharpness in an image. The model is capable of explaining the edge blur, of the image from BNU-1, which provides a theoretical basis for the image motion compensation strategy of cameras with the WFV.

Tensor Velocity Imaging With Motion Correction.

This article presents a motion compensation procedure that significantly improves the accuracy of synthetic aperture tensor velocity estimates for row-column arrays. The proposed motion compensation scheme reduces motion effects by moving the image coordinates with the velocity field during summation of low-resolution volumes. The velocity field is estimated using a transverse oscillation cross-correlation estimator, and each image coordinate's local tensor velocity is determined by upsampling the field using spline interpolation. The motion compensation procedure is validated using Field II simulations and flow measurements acquired using a 3-MHz row-column addressed probe and the research scanner SARUS. For a peak velocity of 25 cm/s, a pulse repetition frequency of 2 kHz, and a beam-to-flow angle of 60°, the proposed motion compensation procedure was able to reduce the relative bias from -27.0% to -9.4% and the standard deviation from 8.6% to 8.1%. In simulations performed with a pulse repetition frequency of 10 kHz, the proposed method reduces the bias in all cases with beam-to-flow angles of 60° and 75° and peak velocities between 10 and 150 cm/s.