Presence Of Large Motion Research Articles

Video-based subtle vibration measurement in the presence of large motions

Recently, phase-based methods offer precise measurement of subtle vibrations from videos but face challenges when dealing with moving objects. This paper introduces a novel video-based method for measuring subtle vibrations in the presence of large motions. It employs complex steerable filters to capture phase variations at each pixel, extracting a composite motion signal. Based on the differences in acceleration characteristics between subtle vibrations and large motions, a band-passed acceleration filter is designed to separate them, relying only on limited prior knowledge of the frequency band. The filter design minimizes passband attenuation using a genetic algorithm for iterative parameter optimization. By further incorporating a Gaussian amplitude kernel, we suppress large-amplitude nonlinear motion interference and extract subtle vibrations with acceleration characteristics. Both simulation experiments and real-world tests demonstrate that this method outperforms state-of-the-art techniques in terms of accuracy, providing a more effective approach for measuring subtle vibrations in moving objects.

Unsupervised Landmark Detection-Based Spatiotemporal Motion Estimation for 4-D Dynamic Medical Images.

Motion estimation is a fundamental step in dynamic medical image processing for the assessment of target organ anatomy and function. However, existing image-based motion estimation methods, which optimize the motion field by evaluating the local image similarity, are prone to produce implausible estimation, especially in the presence of large motion. In addition, the correct anatomical topology is difficult to be preserved as the image global context is not well incorporated into motion estimation. In this study, we provide a novel motion estimation framework of dense-sparse-dense (DSD), which comprises two stages. In the first stage, we process the raw dense image to extract sparse landmarks to represent the target organ's anatomical topology, and discard the redundant information that is unnecessary for motion estimation. For this purpose, we introduce an unsupervised 3-D landmark detection network to extract spatially sparse but representative landmarks for the target organ's motion estimation. In the second stage, we derive the sparse motion displacement from the extracted sparse landmarks of two images of different time points. Then, we present a motion reconstruction network to construct the motion field by projecting the sparse landmarks' displacement back into the dense image domain. Furthermore, we employ the estimated motion field from our two-stage DSD framework as initialization and boost the motion estimation quality in light-weight yet effective iterative optimization. We evaluate our method on two dynamic medical imaging tasks to model cardiac motion and lung respiratory motion, respectively. Our method has produced superior motion estimation accuracy compared to the existing comparative methods. Besides, the extensive experimental results demonstrate that our solution can extract well-representative anatomical landmarks without any requirement of manual annotation. Our code is publicly available online: https://github.com/yyguo-sjtu/DSD-3D-Unsupervised-Landmark-Detection-Based-Motion-Estimation.

Steerable pyramid with subpixel compensation for remotely measuring and magnifying dynamic vibration of moving objects

Measuring vibration generated by a moving object is difficult because the accelerometers must be mounted on the moving object. Moreover, the weight and damping ratio of the accelerometers could cause extra mass and damping effect on small moving objects, casting doubt on the accuracy of such measurements. This paper reports a novel method that can remotely measure vibration from the motion of an object captured by a high-speed camera. Aimed at magnifying subtle changes in the presence of large motions, several phase-based video process methods have been proposed previously. Although these methods can capture such subtle changes, they all have deficiencies. For instance, the pure Euler method needs to process every position of movement, making the process very slow if the video file is large. Furthermore, the methods that combine Eulerian and Lagrangian algorithms ignore the subpixel compensation of stabilization. This paper reports a novel dynamic video processing technology by combining phase-based video process technology with visual tracking. The reason for using visual tracking is to extract the target and detect its dynamic position. To compensate for the error induced during the process of video stabilization, the steerable pyramid method was applied to enhance the magnification of subtle vibration with small error. The proposed method was verified by experiments to show that it can effectively improve the signal-to-noise ratio (SNR). The results demonstrated that the vibration and motion generated from the moving object can be detected and measured with satisfactory SNR. With the help of such an innovative method, in the future, both the motion and vibration signals of fast-moving objects, like airplanes, cars, or even small structures, can be measured simultaneously and remotely using high-speed cameras that can also move.

DAViS Camera Optical Flow

Frame-based optical flow (OF) methods struggle in the presence of large motion and occlusion due to slow frame rates. Optical flow of dynamic vision sensor (DVS) has gained attention recently as a way to overcome these shortcomings. DVS reports “events” corresponding log intensity changes exceeding a specific threshold, with an accuracy of microsecond order. The increased temporal sampling rate are indeed helpful, but the poor spatial fidelity of DVS outputs make events-based OF less reliable overall. In this work, we consider a new sensor called DAViS that combines the conventional active pixel sensor (APS) and DVS circuitries, yielding a conventional intensity image frames as well as the events. We propose a novel optical flow method designed specifically for a DAViS camera that leverages the high spatial fidelity of intensity image frames and the high temporal resolution of events generated by DVS. Hence, the proposed DAViS-OF method yields reliable motion vector estimates while overcoming the fast motion and occlusion problems. The proposed DAViS-OF method is computationally efficient and is suitable for real-time implementation.

Amplitude-Based Filtering for Video Magnification in Presence of Large Motion.

Video magnification reveals important and informative subtle variations in the world. These signals are often combined with large motions which result in significant blurring artifacts and haloes when conventional video magnification approaches are used. To counter these issues, this paper presents an amplitude-based filtering algorithm that can magnify small changes in video in presence of large motions. We seek to understand the amplitude characteristic of small changes and large motions with the goal of extracting accurate signals for visualization. Based on spectrum amplitude filtering, the large motions can be removed while small changes can still be magnified by Eulerian approach. An advantage of this algorithm is that it can handle large motions, whether they are linear or nonlinear. Our experimental results show that the proposed method can amplify subtle variations in the presence of large motion, as well as significantly reduce artifacts. We demonstrate the presented algorithm by comparing to the state-of-the-art and provide subjective and objective evidence for the proposed method.

Handling images of patient postures in arms up and arms down position using a biomechanical skeleton model

Abstract Deformable image registration is gradually becoming the tool of choice for motion extraction during adaptive radiotherapy. Achieving a motion vector field that accurately represents the anatomical changes requires a tissue specific transformation model. Therefore, widely used spline based models most likely fail in appropriately reproducing large anatomical changes such as the arms of the patient being positioned up and down. We present the application of a tissue specific biomechanical model with the goal to mimic patient motion even in presence of large motion. Based on the planning CT, delineated bones are used to represent the rigid anatomy of the patient. We implement ball-and-socket joints between corresponding bones in order to achieve mobility of the skeleton. An inverse kinematics approach enables the propagation of motion between individual bones across their joints, leading to an articulated skeleton that can be controlled by feature points on one or more bones. The transformation of each bone initializes a chainmail based soft tissue model to also propagate the motion into the surrounding heterogeneous soft tissue. Representation of different postures like arms up and down can be achieved within less than 1 s for the skeleton and ∼10 s for the soft tissue. Especially for large anatomical changes, the kinematics approach benefits from the direct articulation at specific joints, considerably lowering the degrees of freedom for motion description. Being the input for the chainmail based soft tissue model, the transformed bones guarantee for its meaningful initialization. The proposed biomechanical skeleton model is promising to facilitate the registration of patients’ anatomy, being positioned with arms up and arms down. The results encourage further refinement of the joints and the soft tissue model.

Robust optimization of VMAT for lung cancer: Dosimetric implications of motion compensation techniques.

In inverse planning of lung radiotherapy, techniques are required to ensure dose coverage of target disease in the presence of tumor motion as a result of respiration. A range of published techniques for mitigating motion effects were compared for dose stability across 5 breath cycles of ±2 cm. Techniques included planning target volume (PTV) expansions, internal target volumes with (OITV) and without tissue override (ITV), average dataset scans (ADS), and mini‐max robust optimization. Volumetric arc therapy plans were created on a thorax phantom and verified with chamber and film measurements. Dose stability was compared by DVH analysis in calculations across all geometries. The lung override technique resulted in a substantial lack of dose coverage (−10%) to the tumor in the presence of large motion. PTV, ITV and ADS techniques resulted in substantial (up to 25%) maximum dose increases where solid tissue travelled into low density optimized regions. The results highlight the need for care in optimization of highly heterogeneous where density variations may occur with motion. Robust optimization was shown to provide greater stability in both maximum (<3%) and minimum dose variations (<2%) over all other techniques.

A method for the dynamic correction of B0-related distortions in single-echo EPI at 7 T

We propose a method to calculate field maps from the phase of each EPI in an fMRI time series. These field maps can be used to correct the corresponding magnitude images for distortion caused by inhomogeneity in the static magnetic field. In contrast to conventional static distortion correction, in which one ‘snapshot’ field map is applied to all subsequent fMRI time points, our method also captures dynamic changes to B0 which arise due to motion and respiration. The approach is based on the assumption that the non-B0-related contribution to the phase measured by each radio-frequency coil, which is dominated by the coil sensitivity, is stable over time and can therefore be removed to yield a field map from EPI.Our solution addresses imaging with multi-channel coils at ultra-high field (7T), where phase offsets vary rapidly in space, phase processing is non-trivial and distortions are comparatively large. We propose using dual-echo gradient echo reference scan for the phase offset calculation, which yields estimates with high signal-to-noise ratio. An extrapolation method is proposed which yields reliable estimates for phase offsets even where motion is large and a tailored phase unwrapping procedure for EPI is suggested which gives robust results in regions with disconnected tissue or strong signal decay.Phase offsets are shown to be stable during long measurements (40min) and for large head motions. The dynamic distortion correction proposed here is found to work accurately in the presence of large motion (up to 8.1°), whereas a conventional method based on single field map fails to correct or even introduces distortions (up to 11.2mm). Finally, we show that dynamic unwarping increases the temporal stability of EPI in the presence of motion.Our approach can be applied to any EPI measurements without the need for sequence modification.