Robust real-time ground plane motion compensation from a moving vehicle

Cardiac x-ray CT imaging is still challenging due to heart motion, which cannot be ignored even with the current rotation speed of the equipment. In response, many algorithms have been developed to compensate remaining motion artifacts by estimating the motion using projection data or reconstructed images. In these algorithms, accurate motion estimation is critical to the compensated image quality. In addition, since the scan range is directly related to the radiation dose, it is preferable to minimize the scan range in motion estimation. In this paper, the authors propose a novel motion estimation and compensation algorithm using a sinogram with a rotation angle of less than 360°. The algorithm estimates the motion of the whole heart area using two opposite 3D partial angle reconstructed (PAR) images and compensates the motion in the reconstruction process. A CT system scans the thoracic area including the heart over an angular range of 180° + α + β, where α and β denote the detector fan angle and an additional partial angle, respectively. The obtained cone-beam projection data are converted into cone-parallel geometry via row-wise fan-to-parallel rebinning. Two conjugate 3D PAR images, whose center projection angles are separated by 180°, are then reconstructed with an angular range of β, which is considerably smaller than a short scan range of 180° + α. Although these images include limited view angle artifacts that disturb accurate motion estimation, they have considerably better temporal resolution than a short scan image. Hence, after preprocessing these artifacts, the authors estimate a motion model during a half rotation for a whole field of view via nonrigid registration between the images. Finally, motion-compensated image reconstruction is performed at a target phase by incorporating the estimated motion model. The target phase is selected as that corresponding to a view angle that is orthogonal to the center view angles of two conjugate PAR images. To evaluate the proposed algorithm, digital XCAT and physical dynamic cardiac phantom datasets are used. The XCAT phantom datasets were generated with heart rates of 70 and 100 bpm, respectively, by assuming a system rotation time of 300 ms. A physical dynamic cardiac phantom was scanned using a slowly rotating XCT system so that the effective heart rate will be 70 bpm for a system rotation speed of 300 ms. In the XCAT phantom experiment, motion-compensated 3D images obtained from the proposed algorithm show coronary arteries with fewer motion artifacts for all phases. Moreover, object boundaries contaminated by motion are well restored. Even though object positions and boundary shapes are still somewhat different from the ground truth in some cases, the authors see that visibilities of coronary arteries are improved noticeably and motion artifacts are reduced considerably. The physical phantom study also shows that the visual quality of motion-compensated images is greatly improved. The authors propose a novel PAR image-based cardiac motion estimation and compensation algorithm. The algorithm requires an angular scan range of less than 360°. The excellent performance of the proposed algorithm is illustrated by using digital XCAT and physical dynamic cardiac phantom datasets.

To develop and test a real-time motion compensation algorithm for contrast-enhanced ultrasound imaging of tumor angiogenesis on a clinical ultrasound system. The Administrative Institutional Panel on Laboratory Animal Care approved all experiments. A new motion correction algorithm measuring the sum of absolute differences in pixel displacements within a designated tracking box was implemented in a clinical ultrasound machine. In vivo angiogenesis measurements (expressed as percent contrast area) with and without motion compensated maximum intensity persistence (MIP) ultrasound imaging were analyzed in human colon cancer xenografts (n=64) in mice. Differences in MIP ultrasound imaging signal with and without motion compensation were compared and correlated with displacements in x- and y-directions. The algorithm was tested in an additional twelve colon cancer xenograft-bearing mice with (n=6) and without (n=6) anti-vascular therapy (ASA-404). In vivo MIP percent contrast area measurements were quantitatively correlated with ex vivo microvessel density (MVD) analysis. MIP percent contrast area was significantly different (P<0.001) with and without motion compensation. Differences in percent contrast area correlated significantly (P<0.001) with x- and y-displacements. MIP percent contrast area measurements were more reproducible with motion compensation (ICC=0.69) than without (ICC=0.51) on two consecutive ultrasound scans. Following anti-vascular therapy, motion-compensated MIP percent contrast area significantly (P=0.03) decreased by 39.4±14.6% compared to non-treated mice and correlated well with ex vivo MVD analysis (Rho=0.70; P=0.05). Real-time motion-compensated MIP ultrasound imaging allows reliable and accurate quantification and monitoring of angiogenesis in tumors exposed to breathing-induced motion artifacts.

For spotlight synthetic aperture radar (SAR) on circular trajectories, the phase error caused by motion error is 2-D space-variant. For the conventional motion compensation (MOCO) algorithm, the imaging scene is generally segmented into many sub-regions to compensate for the space-variant phase error. However, the complex phase error results in that fact that it is difficult to obtain the quantitative criterion of scene segmentation. To address this issue, we propose a novel MOCO algorithm. This method contains three steps; the first step is non-space-variant phase error compensation; the second step is to remove the height term; the third step is space-variant phase error compensation. Among these steps, the most critical operation is removing the height term. In the distance equation, the height term is independent of the ground target coordinate. Hence, it can be removed by a serial of operations containing resampling and phase compensation. Through removing height term, it doesn't only compensate the height error but also makes the residual phase error into 1-D space-variant. Then, the phase error is exclusively related to the radius error and the target abscissa. Hence, after compensating for the space-variant error, it only needs to segment scene along the abscissa direction. Meanwhile, it can gain the quantitative criterion of scene segmentation to guarantee the phase error variation range is less than π/4 in each sub-scene. Eventually, some simulations are conducted to illustrate that the proposed method is valid.

Aiming at the higher bit-rate occupation of motion vector encoding and more time load of full-searching strategies, a multi-resolution motion estimation and compensation algorithm based on adjacent prediction of frame difference was proposed. Differential motion detection was employed to image sequences and proper threshold was adopted to identify the connected region. Then the motion region was extracted to carry out motion estimation and motion compensation on it. The experiment results show that the encoding efficiency of motion vector is promoted, the complexity of motion estimation is reduced and the quality of the reconstruction image at the same bit-rate as Multi-Resolution Motion Estimation (MRME) is improved.

Until today, several software-based approaches to increase the temporal resolution in cardiac computed tomography by estimating motion vector fields (MVFs) have been developed. Thereunder, the majority are motion compensation algorithms, which estimate the MVFs employing a three-dimensional registration routine working on reconstructions of multiple cardiac phases.^{2, 6, 7, 12}We present an algorithm that requires nothing more than the data needed for a short scan reconstruction for motion estimation and motion-compensated reconstruction, which both are based on the reconstruction of volumes from a limited angular range.^{2, 3, 7, 8} Those partial angle reconstructions are centered at different time points during the short scan and have a temporal resolution of about 10ms each. The MVFs are estimated by a constrained cost function optimization routine employing a motion artifact measuring cost function. During optimization, the MVFs are applied directly by warping the partial angle reconstructions, and the motion compensation is established by simply adding the shifted images. In order to enforce smooth vector fields and keep the number of parameters low, the motion is modeled by a low degree polynomial. Furthermore, to find a good estimation of the MVFs even in phases with rapid cardiac motion, the constrained optimization is re-initialized multiple times. The algorithm is validated with the help of a simulation study and applied to patient data, where motion- compensated reconstructions are performed in various cardiac phases. We show that the image quality can be improved, also in more rapid cardiac phases due to re-initialization of the optimization routine.

Synthetic transmit aperture (STA) has been widely investigated in ultrasound system recently with characteristics of high frame rate and low hardware cost. Since the high-resolution image (HRI) of STA is formed by summation of low-resolution images (LRIs), it is susceptible to inter-firing motions. In this paper, we propose a low-complexity global motion compensation algorithm. We use the common region of interest (ROIcom) between different transmissions of STA imaging to beamform backward and forward beam vectors. Then, the magnitude and direction of motion can be evaluated by cross-correlations between specific beam vectors in STA imaging. Compared with the uncompensated image in two-dimentional (2D) motion environment, the proposed motion compensation algorithm can improve the contrast ratio (CR) and contrast-to-noise ratio (CNR) by 13.73 and 2.04 dB, respectively. Also, the proposed algorithm improves the CR and CNR about 7.84 and 1.36 dB comparing with the reference work, respectively. In the Field II breath model, the proposed method also improves the CR and CNR about 6.65 and 1.04 dB than the reference method, respectively. Moreover, we propose a low-complexity delay generator in the architecture design to further reduce the computational complexity of the whole beamforming system. Finally, we verify the proposed low-complexity motion compensation beamforming engine by using the VLSI implementation with CMOS 90 nm technology. In the post-layout result, the core size is 2.39 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> at 125 MHz operating frequency and the frame rate of the beamforming system is 42.23 frames per second.

Real-time video image processing requires video compression techniques. Efficient Motion Estimation (ME) and Motion Compensation (MC) algorithms and their successful hardware implementation are the key to video compression. Work on developing and implementing efficient ME and MC algorithms for multi-camera systems is ongoing. The low power consumption yet high speed of Heterogeneous Reconfigurable Devices such as Field Programmable Gate Arrays (FPGA) can be suitable for implementation of real-time optic flow computation using multi-camera systems. This paper focuses on a search into the current state-of-the-art for multi-camera motion tracking. It has been found the tracking systems mainly focused on people and vehicle tracking in both indoor and outdoor conditions, tracking under occlusion, tracking for surveillance, single and multi-view tracking, etc. The review search also found use of multi-camera tracking in the medical sector, such as multi-camera tracking systems for respiratory motion tracking. Research on this field has not been explored much and this holds possibilities for further work.

This paper proposes a novel motion compensation algorithm for wide beam Synthetic Aperture Radar (SAR) based on frequency division. It applies motion compensation in time domain and corrects both low- and high- frequency motion errors. The rationale and procedure of this algorithm are introduced in detail. Point scatterers of a P-band airborne SAR with different motion errors are tested to validate this algorithm. Compared with traditional narrow beam motion compensation and available wide beam compensation, the proposed algorithm has better performance.

CyberKnife, a stereotactic radiosurgery (SRS) technique, delivers highly precise radiation beams to tumors. However, the patient motion, such as respiration and positional changes, can lead to tumor displacement, affecting the treatment efficacy. Fiducial marker tracking plays a crucial role in CyberKnife therapy, enabling real-time tumor position monitoring and ensuring accurate radiation delivery. This paper reviews motion compensation methods based on fiducial marker tracking, explores how fiducial markers facilitate precise tumor localization (tracking precision: 0.2–0.5 mm), and analyzes various motion compensation techniques, including respiratory motion compensation and positional adjustment. By dynamically adjusting the treatment device and radiation dose, fiducial marker tracking and motion compensation significantly enhance the precision of CyberKnife therapy, reduce errors caused by patient motion, and improve treatment outcomes.

A modified block matching algorithm (BMA) with motion correlation constraint (MCCBMA) is proposed at first. Then a novel motion compensation algorithm (INTPMC) which computes motion vector for each pixel by interpolating motion vectors is presented. In order to increase interframe prediction performance and decrease the computational complexity, an optimizing algorithm for partial motion vectors is described at last. Experimental results show that the proposed algorithm can improve the prediction performance obviously with a moderately increased complexity compared with the conventional full search block matching algorithm (FSBMA).

This letter presents a comparison between three Fourier-based motion compensation (MoCo) algorithms for airborne synthetic aperture radar (SAR) systems. These algorithms circumvent the limitations of conventional MoCo, namely the assumption of a reference height and the beam-center approximation. All these approaches rely on the inherent time-frequency relation in SAR systems but exploit it differently, with the consequent differences in accuracy and computational burden. After a brief overview of the three approaches, the performance of each algorithm is analyzed with respect to azimuthal topography accommodation, angle accommodation, and maximum frequency of track deviations with which the algorithm can cope. Also, an analysis on the computational complexity is presented. Quantitative results are shown using real data acquired by the Experimental SAR system of the German Aerospace Center (DLR).

The non-contact vital sign detection of an individual in indoor environments using a radar sensor has been widely researched by recent advances in the IoT industry. Most of these studies have been conducted by analyzing the spectrum of extracted phase variations in the echo signal of a motionless individual that has only micro-motion (that is, vital signs) caused by both the lung and the heart. However, in the case of a walking individual, the performance of the vital sign detection using conventional methods may be rapidly degraded owing to extreme phase fluctuations caused by the relatively large movement of the individual compared to the vital sign motions. To address this problem, we propose a new motion compensation method for the vital sign detection of a walking individual. First, we conduct a modeling of the echo signal for a walking individual using a radar and discuss problems in vital sign detection. Next, a new algorithm for real-time motion compensation is suggested based on a moving averaging filter and mode in statistics. In the experiments using an IR-UWB (Impulse-radio ultra-wideband) radar, we observed that the proposed method performed a successful real-time vital sign detection of a walking individual.

Motion correction in PET has become more important as system resolution has improved. The purpose of this study was to evaluate the accuracy of three motion compensation methods, event-by-event motion compensation with list-mode reconstruction (MOLAR), frame-based motion correction, and post-reconstruction image registration. Motion compensated image reconstructions were carried out with simulated HRRT data, using a range of motion information based on human motion data. ROI analyses in high contrast regions were performed to evaluate the accuracy of all the motion compensation methods, with particular attention to within-frame motion.Our study showed that MOLAR with list-mode based motion correction using accurate motion data can reliably correct for all reasonable head motions. Over all motions, the average ROI count was within 0.1±4.2% and 0.7±0.9% of the reference, no-motion value for two different ROIs. The location of the ROI centroid was found to be within 0.7±0.3mm of that of the reference image for the raphe nucleus. Frame-based motion compensation and post-reconstruction image registration were able to correct for small (<5mm), but the ROI intensity begins to deteriorate for medium motions (5-10mm), especially for small brain structures such as the raphe nucleus. For large (>10mm) motions, the average centroid locations of the raphe nucleus ROI had an offset error of 1.5±1.8mm and 1.8±1.8mm for each of the frame-based methods. For each frame-based method, the decrease in the average ROI intensity was 16.9±4.3% and 20.2±9.9% respectively for the raphe nucleus, and was 5.5±2.2% and 7.4±0.2% for putamen. Based on these data, we conclude that event-by-event based motion correction works accurately for all reasonable motions, whereas frame-based motion correction is accurate only when the within-frame motion is less than 10mm.

We retrospectively evaluated the efficacy and toxicity of gross tumor volume (GTV) mean dose optimized stereotactic body radiation therapy (SBRT) for primary and secondary lung tumors with and without robotic real-time motion compensation. Between 2011 and 2017, 208 patients were treated with SBRT for 111 primary lung tumors and 163 lung metastases with a median GTV of 8.2 cc (0.3-174.0 cc). Monte Carlo dose optimization was performed prioritizing GTV mean dose at the potential cost of planning target volume (PTV) coverage reduction while adhering to safe normal tissue constraints. The median GTV mean biological effective dose (BED)10 was 162.0 Gy10 (34.2-253.6 Gy10) and the prescribed PTV BED10 ranged 23.6-151.2 Gy10 (median, 100.8 Gy10). Motion compensation was realized through direct tracking (44.9%), fiducial tracking (4.4%), and internal target volume (ITV) concepts with small (≤5 mm, 33.2%) or large (>5 mm, 17.5%) motion. The local control (LC), progression-free survival (PFS), overall survival (OS), and toxicity were analyzed. Median follow-up was 14.5 months (1-72 months). The 2-year actuarial LC, PFS, and OS rates were 93.1, 43.2, and 62.4%, and the median PFS and OS were 18.0 and 39.8 months, respectively. In univariate analysis, prior local irradiation (hazard ratio (HR) 0.18, confidence interval (CI) 0.05-0.63, p = 0.01), GTV/PTV (HR 1.01-1.02, CI 1.01-1.04, p < 0.02), and PTV prescription, mean GTV, and maximum plan BED10 (HR 0.97-0.99, CI 0.96-0.99, p < 0.01) were predictive for LC while the tracking method was not (p = 0.97). For PFS and OS, multivariate analysis showed Karnofsky Index (p < 0.01) and tumor stage (p ≤ 0.02) to be significant factors for outcome prediction. Late radiation pneumonitis or chronic rip fractures grade 1-2 were observed in 5.3% of the patients. Grade ≥3 side effects did not occur. Robotic SBRT is a safe and effective treatment for lung tumors. Reducing the PTV prescription and keeping high GTV mean doses allowed the reduction of toxicity while maintaining high local tumor control. The use of real-time motion compensation is strongly advised, however, well-performed ITV motion compensation may be used alternatively when direct tracking is not feasible.

ABSTRACTInverse synthetic aperture radar (ISAR) imaging is an efficient technique to generate two‐dimensional spatial distributions of radar cross‐section of a target. The received signal of ISAR contains interphase errors resulting in blurring problem in the images due to motion effects of the target. In this study, a simple, stable, and robust method based on artificial bee colony (ABC) algorithm has been proposed for motion compensation (MoComp) to overcome the blurring problem in ISAR images. The minimum entropy was used as objective function in the ABC algorithm for optimally determining the parameters of velocity and acceleration. The proposed method was implemented to four different scenarios based on the two air target models to show performance of the ABC algorithm. The radar signal of the target without MoComp was given as inputs to the ABC algorithm to determine velocity and acceleration values so as to provide the clearest ISAR image. The proposed method was compared with a suggestion reported elsewhere using particle swarm optimization (PSO), PSO with island model, genetic algorithm. It is pointed out that the proposed method is more successful, stable and efficient in terms of the image quality, entropy values, and standard deviation. © 2014 Wiley Periodicals, Inc. Microwave Opt Technol Lett 56:2691–2698, 2014