Explicit Motion Research Articles

DCE-MRI of the liver with sub-second temporal resolution using GRASP-Pro with navi-stack-of-stars sampling.

Respiratory motion-induced image blurring and artifacts can compromise image quality in dynamic contrast-enhanced MRI (DCE-MRI) of the liver. Despite remarkable advances in respiratory motion detection and compensation in past years, these techniques have not yet seen widespread clinical adoption. The accuracy of image-based motion detection can be especially compromised in the presence of contrast enhancement and/or in situations involving deep and/or irregular breathing patterns. This work proposes a framework that combines GRASP-Pro (Golden-angle RAdial Sparse Parallel MRI with imProved performance) MRI with a new radial sampling scheme called navi-stack-of-stars for free-breathing DCE-MRI of the liver without the need for explicit respiratory motion compensation. A prototype 3D golden-angle radial sequence with a navi-stack-of-stars sampling scheme that intermittently acquires a 2D navigator was implemented. Free-breathing DCE-MRI of the liver was conducted in 24 subjects at 3T including 17 volunteers and 7 patients. GRASP-Pro reconstruction was performed with a temporal resolution of 0.34-0.45 s per volume, whereas standard GRASP reconstruction was performed with a temporal resolution of 15 s per volume. Motion compensation was not performed in all image reconstruction tasks. Liver images in different contrast phases from both GRASP and GRASP-Pro reconstructions were visually scored by two experienced abdominal radiologists for comparison. The nonparametric paired two-tailed Wilcoxon signed-rank test was used to compare image quality scores, and the Cohen's kappa coefficient was calculated to evaluate the inter-reader agreement. GRASP-Pro MRI with sub-second temporal resolution consistently received significantly higher image quality scores (P < 0.05) than standard GRASP MRI throughout all contrast enhancement phases and across all assessment categories. There was a substantial inter-reader agreement for all assessment categories (ranging from 0.67 to 0.89). The proposed technique using GRASP-Pro reconstruction with navi-stack-of-stars sampling holds great promise for free-breathing DCE-MRI of the liver without respiratory motion compensation.

Free-breathing time-resolved 4D MRI with improved T1-weighting contrast.

This work proposes MP-Grasp4D (magnetization-prepared golden-angle radial sparse parallel 4D) MRI, a free-breathing, inversion recovery (IR)-prepared, time-resolved 4D MRI technique with improved T1-weighted contrast. MP-Grasp4D MRI acquisition incorporates IR preparation into a radial gradient echo sequence. MP-Grasp4D employs a golden-angle navi-stack-of-stars sampling scheme, where imaging data of rotating radial stacks and navigator stacks (acquired at a consistent rotation angle) are alternately acquired. The navigator stacks are used to estimate a temporal basis for low-rank subspace-constrained reconstruction. This allows for the simultaneous capture of both IR-induced contrast changes and respiratory motion. One temporal frame of the imaging volume in MP-Grasp4D MRI is reconstructed from a single stack and an adjacent navigator stack on average, resulting in a nominal temporal resolution of 0.16 seconds per volume. Images corresponding to the optimal inversion time (TI) can be retrospectively selected for providing the best image contrast. Reader studies were conducted to assess the performance of MP-Grasp4D MRI in liver imaging across 30 subjects in comparison with standard Grasp4D MRI without IR preparation. MP-Grasp4D MRI received significantly higher scores (P < 0.05) than Grasp4D in all assessment categories. There was a moderate to almost perfect agreement (kappa coefficient from 0.42 to 0.9) between the two readers for image quality assessment. When the scan time is reduced, MP-Grasp4D MRI preserves image contrast and quality, demonstrating additional acceleration capability. MP-Grasp4D MRI improves T1-weighted contrast for free-breathing time-resolved 4D MRI and eliminates the need for explicit motion compensation. This method is expected to be valuable in different MRI applications such as MR-guided radiotherapy.

Cascaded UNet for progressive noise residual prediction for structure-preserving video denoising

The prominence of high-quality video services has become so substantial that by 2030, it is estimated that approximately 80% of internet traffic will consist of videos. On the contrary, video denoising remains a relatively unexplored and intricate field, presenting more substantial challenges compared to image denoising. Many published deep learning video denoising algorithms typically rely on simple, efficient single encoder–decoder networks, but they have inherent limitations in preserving intricate image details and effectively managing noise information propagation for noise residue modelling. In response to these challenges, the proposed work introduces an innovative approach; in terms of utilization of cascaded UNets for progressive noise residual prediction in video denoising. This multi-stage encoder–decoder architecture is meticulously designed to accurately predict noise residual maps, thereby preserving the locally fine details within video content as represented by SSIM. The proposed network has undergone extensive end-to-end training from scratch without explicit motion compensation to reduce complexity. In terms of the more rigorous SSIM metric, the proposed network outperformed all video denoising methods while maintaining a comparable PSNR.

Collaborative spatial-temporal video salient object detection with cross attention transformer

Noticing the effectiveness of explicit motion encoding in optical flow, a variety of recent works employ flow-guided two-branch structures to handle the video salient object detection (VSOD) task. However, most of them ignore the semantic gap between the moving objects and the salient objects. Besides, the long-range dependency is not sufficiently investigated due to the local convolution operation. To tackle these problems, in this paper, we propose a novel collaborative spatio-temporal feature fusion method based on the cross-attention transformer for VSOD. A Siamese feature extractor is introduced to jointly optimize the motion and static feature extraction with reduced network parameters. The deep level set method is employed to segment the moving objects from optical flow, and thus the dependence on saliency groundtruth is greatly reduced. Moreover, a cross-attention transformer is proposed to jointly optimize and fuse the static and motion features, as well as investigate long-range dependency. Experimental results on six commonly used video salient object datasets demonstrate that our method achieves state-of-the-art performance among all VSOD algorithms.

Learn the Force We Can: Enabling Sparse Motion Control in Multi-Object Video Generation

We propose a novel unsupervised method to autoregressively generate videos from a single frame and a sparse motion input. Our trained model can generate unseen realistic object-to-object interactions. Although our model has never been given the explicit segmentation and motion of each object in the scene during training, it is able to implicitly separate their dynamics and extents. Key components in our method are the randomized conditioning scheme, the encoding of the input motion control, and the randomized and sparse sampling to enable generalization to out of distribution but realistic correlations. Our model, which we call YODA, has therefore the ability to move objects without physically touching them. Through extensive qualitative and quantitative evaluations on several datasets, we show that YODA is on par with or better than state of the art video generation prior work in terms of both controllability and video quality.

EulerMormer: Robust Eulerian Motion Magnification via Dynamic Filtering within Transformer

Video Motion Magnification (VMM) aims to break the resolution limit of human visual perception capability and reveal the imperceptible minor motion that contains valuable information in the macroscopic domain. However, challenges arise in this task due to photon noise inevitably introduced by photographic devices and spatial inconsistency in amplification, leading to flickering artifacts in static fields and motion blur and distortion in dynamic fields in the video. Existing methods focus on explicit motion modeling without emphasizing prioritized denoising during the motion magnification process. This paper proposes a novel dynamic filtering strategy to achieve static-dynamic field adaptive denoising. Specifically, based on Eulerian theory, we separate texture and shape to extract motion representation through inter-frame shape differences, expecting to leverage these subdivided features to solve this task finely. Then, we introduce a novel dynamic filter that eliminates noise cues and preserves critical features in the motion magnification and amplification generation phases. Overall, our unified framework, EulerMormer, is a pioneering effort to first equip with Transformer in learning-based VMM. The core of the dynamic filter lies in a global dynamic sparse cross-covariance attention mechanism that explicitly removes noise while preserving vital information, coupled with a multi-scale dual-path gating mechanism that selectively regulates the dependence on different frequency features to reduce spatial attenuation and complement motion boundaries. We demonstrate extensive experiments that EulerMormer achieves more robust video motion magnification from the Eulerian perspective, significantly outperforming state-of-the-art methods. The source code is available at https://github.com/VUT-HFUT/EulerMormer.

Prospectively accelerated dynamic speech magnetic resonance imaging at 3 T using a self-navigated spiral-based manifold regularized scheme.

This work develops and evaluates a self-navigated variable density spiral (VDS)-based manifold regularization scheme to prospectively improve dynamic speech magnetic resonance imaging (MRI) at 3 T. Short readout duration spirals (1.3-ms long) were used to minimize sensitivity to off-resonance. A custom 16-channel speech coil was used for improved parallel imaging of vocal tract structures. The manifold model leveraged similarities between frames sharing similar vocal tract postures without explicit motion binning. The self-navigating capability of VDS was leveraged to learn the Laplacian structure of the manifold. Reconstruction was posed as a sensitivity-encoding-based nonlocal soft-weighted temporal regularization scheme. Our approach was compared with view-sharing, low-rank, temporal finite difference, extra dimension-based sparsity reconstruction constraints. Undersampling experiments were conducted on five volunteers performing repetitive and arbitrary speaking tasks at different speaking rates. Quantitative evaluation in terms of mean square error over moving edges was performed in a retrospective undersampling experiment on one volunteer. For prospective undersampling, blinded image quality evaluation in the categories of alias artifacts, spatial blurring, and temporal blurring was performed by three experts in voice research. Region of interest analysis at articulator boundaries was performed in both experiments to assess articulatory motion. Improved performance with manifold reconstruction constraints was observed over existing constraints. With prospective undersampling, a spatial resolution of 2.4 × 2.4 mm2/pixel and a temporal resolution of 17.4 ms/frame for single-slice imaging, and 52.2 ms/frame for concurrent three-slice imaging, were achieved. We demonstrated implicit motion binning by analyzing the mechanics of the Laplacian matrix. Manifold regularization demonstrated superior image quality scores in reducing spatial and temporal blurring compared with all other reconstruction constraints. While it exhibited faint (nonsignificant) alias artifacts that were similar to temporal finite difference, it provided statistically significant improvements compared with the other constraints. In conclusion, the self-navigated manifold regularized scheme enabled robust high spatiotemporal resolution dynamic speech MRI at 3 T.

Omniscient Video Super-Resolution with Explicit-Implicit Alignment

When considering the temporal relationships, most previous video super-resolution (VSR) methods follow the iterative or recurrent framework. The iterative framework adopts neighboring low-resolution (LR) frames from a sliding window, while the recurrent framework utilizes the output generated in the previous SR procedure. The hybrid framework combines them but still cannot fully leverage the temporal relationships. Meanwhile, the existing methods are limited in the receptive field of the optical flow or lack semantic constrains on motion information. In this work, we propose an omniscient framework to fully explore the temporal relationships in the video, which encompasses both LR frames and SR outputs from the past, present, and future. The omniscient framework is more generic because the iterative, recurrent, and hybrid frameworks can be regarded as its special cases. Besides, when addressing the motion information, most previous VSR methods adopt the explicit motion estimation and compensation, while many recent methods turn to implicit alignment. In implicit alignment methods, because basic non-local means suffers from heavy computational costs, we improve it by capturing the non-local correlations in a relatively local manner to reduce the complexity. Moreover, we integrate the explicit and implicit methods into an explicit-implicit alignment module to better utilize motion information. We have conducted extensive experiments on public datasets, which show that our method is superior over the state-of-the-art methods in objective metrics, subjective visual quality, and complexity. In particular, on datasets of Vid4 and UDM10, our method improves PSNR by 0.19 dB, 0.49 dB against the most advanced method BasicVSR++, respectively.

Energy-Motion Features Aggregation Network for Players’ Fine-Grained Action Analysis in Soccer Videos

Rich and complex events in sports have led to the development of a wide-variety of techniques for interpreting content of sports videos in terms of players’ actions, poses, gait, performance, etc. This is due to the requirements from coaches, trainers and players who expect to analyze actions in top sports events, as well as sports fans who practice to imitate professional playing skills, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., dribbling, shooting, etc. However, this poses two key challenges for automated sports analysis community. Firstly, there are extremely limited public sports datasets. Secondly, recent advances in interpretations of sports activities, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., soccer, are predominantly made through analyzing coarse-grained contents. Players’ fine-grained skills analysis still remains under-explored. To alleviate these problems, this paper (a) collects the dataset of highlight videos of soccer players, including two coarse-grained action types of soccer players and six fine-grained actions of players. Detailed annotations are provided for the collected dataset, in terms of action classes, bounding boxes, segmentation maps, and body keypoints of soccer players, and positions of a soccer ball in a game. (b) leverages the understanding of complex highlight videos by proposing an energy-motion features aggregation network- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EMA-Net</i> to fully exploit energy-based representation of soccer players movements in video sequences and explicit motion dynamics of soccer players in videos for soccer players’ fine-grained action analysis. Experimental results and ablation studies validate the proposed approach in recognizing soccer players actions using the collected soccer highlight video datasets.

Motion state-dependent motor learning based on explicit visual feedback has limited spatiotemporal properties compared with adaptation to physical perturbations.

We recently showed that subjects can learn motion state-dependent changes to motor output (temporal force patterns) based on explicit visual feedback of the equivalent force field (i.e., without the physical perturbation). Here, we examined the spatiotemporal properties of this learning compared with learning based on physical perturbations. There were two human subject groups and two experimental paradigms. One group (n = 40) experienced physical perturbations (i.e., a velocity-dependent force field, vFF), whereas the second (n = 40) was given explicit visual feedback (EVF) of the force-velocity relationship. In the latter, subjects moved in force channels and we provided visual feedback of the lateral force exerted during the movement, as well as the required force pattern based on movement velocity. In the first paradigm (spatial generalization), following vFF or EVF training, generalization of learning was tested by requiring subjects to move to 14 untrained target locations (0° to ±135° around the trained location). In the second paradigm (temporal stability), following training, we examined the decay of learning over eight delay periods (0 to 90 s). Results showed that learning based on EVF did not generalize to untrained directions, whereas the generalization for the vFF was significant for targets ≤ 45° away. In addition, the decay of learning for the EVF group was significantly faster than the FF group (a time constant of 2.72 ± 1.74 s vs. 12.53 ± 11.83 s). Collectively, our results suggest that recalibrating motor output based on explicit motion state information, in contrast to physical disturbances, uses learning mechanisms with limited spatiotemporal properties.NEW & NOTEWORTHY Adjustment of motor output based on limb motion state information can be achieved based on explicit information or from physical perturbations. Here, we investigated the spatiotemporal characteristics of short-term motor learning to determine the properties of the respective learning mechanisms. Our results suggest that adjustments based on physical perturbations are more temporally stable and applied over a greater spatial range than the learning based on explicit visual feedback, suggesting largely separate learning mechanisms.

Scale‐Awareness in an Eddy Energy Constrained Mesoscale Eddy Parameterization

AbstractThere is an increasing interest in mesoscale eddy parameterizations that are scale‐aware, normally interpreted to mean that a parameterization does not require parameter recalibration as the model resolution changes. Here we examine whether Gent–McWilliams (GM) based version of GEOMETRIC, a mesoscale eddy parameterization that is constrained by a parameterized eddy energy budget, is scale‐aware in its energetics. It is generally known that GM‐based schemes severely damp out explicit eddies, so the parameterized component would be expected to dominate across resolutions, and we might expect a negative answer to the question of energetic scale‐awareness. A consideration of why GM‐based schemes damp out explicit eddies leads a suggestion for what we term a splitting procedure: a definition of a “large‐scale” field is sought, and the eddy‐induced velocity from the GM‐scheme is computed from and acts only on the large‐scale field, allowing explicit and parameterized components to co‐exist. Within the context of an idealized re‐entrant channel model of the Southern Ocean, evidence is provided that the GM‐based version of GEOMETRIC is scale‐aware in the energetics as long as we employ a splitting procedure. The splitting procedure also leads to an improved representation of mean states without detrimental effects on the explicit eddy motions.

Effective Molecular Dynamics from Neural Network-Based Structure Prediction Models.

Recent breakthroughs in neural network-based structure prediction methods, such as AlphaFold2 and RoseTTAFold, have dramatically improved the quality of computational protein structure prediction. These models also provide statistical confidence scores that can estimate uncertainties in the predicted structures, but it remains unclear to what extent these scores are related to the intrinsic conformational dynamics of proteins. Here, we compare AlphaFold2 prediction scores with explicit large-scale molecular dynamics simulations of 28 one- and two-domain proteins with varying degrees of flexibility. We demonstrate a strong correlation between the statistical prediction scores and the explicit motion derived from extensive atomistic molecular dynamics simulations and further derive an elastic network model based on the statistical scores of AlphFold2 (AF-ENM), which we benchmark in combination with coarse-grained molecular dynamics simulations. We show that our AF-ENM method reproduces the global protein dynamics with improved accuracy, providing a powerful way to derive effective molecular dynamics using neural network-based structure prediction models.

Sequential parametrized motion planning and its complexity, II

This is a continuation of our recent paper [6] in which we developed the theory of sequential parametrized motion planning. A sequential parametrized motion planning algorithm produced a motion of the system which is required to visit a prescribed sequence of states, in a certain order, at specified moments of time. In [6] we analysed the sequential parametrized topological complexity of the Fadell - Neuwirth fibration which is relevant to the problem of moving multiple robots avoiding collisions with other robots and with obstacles in the Euclidean space. In [6] we found the sequential parametrised topological complexity of the Fadell - Neuwirth bundle for the case of the Euclidean space Rd of odd dimension as well as the case d=2. In the present paper we give the complete answer for an arbitrary d≥2 even. Moreover, we present an explicit motion planning algorithm for controlling multiple robots in Rd having the minimal possible topological complexity; this algorithm is applicable to any number n of robots and any number m≥2 of obstacles.

Quality Assessment of UGC Videos Based on Decomposition and Recomposition

The prevalence of short-video applications imposes more requirements for video quality assessment (VQA). User-generated content (UGC) videos are captured under an unprofessional environment, thus suffering from various dynamic degradations, such as camera shaking. To cover the dynamic degradations, existing recurrent neural network-based UGC-VQA methods can only provide implicit modeling, which is unclear and difficult to analyze. In this work, we consider explicit motion representation for dynamic degradations, and propose a motion-enhanced UGC-VQA method based on decomposition and recomposition. In the decomposition stage, a dual-stream decomposition module is built, and VQA task is decomposed into single frame-based quality assessment problem and cross frames-based motion understanding. The dual streams are well grounded on the two-pathway visual system during perception, and require no extra UGC data due to knowledge transfer. Hierarchical features from shallow to deep layers are gathered to narrow the gaps from tasks and domains. In the recomposition stage, a progressively residual aggregation module is built to recompose features from the dual streams. Representations with different layers and pathways are interacted and aggregated in a progressive and residual manner, which keeps a good trade-off between representation deficiency and redundancy. Extensive experiments on UGC-VQA databases verify that our method achieves the state-of-the-art performance and keeps a good capability of generalization. The source code will be available in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Sissuire/DSD-PRO</uri> .

DiFiR-CT: Distance field representation to resolve motion artifacts in computed tomography.

Motion during data acquisition leads to artifacts in computed tomography (CT) reconstructions. In cases such as cardiac imaging, not only is motion unavoidable, but evaluating the motion of the object is of clinical interest. Reducing motion artifacts has typically been achieved by developing systems with faster gantry rotation or via algorithms which measure and/or estimate the displacement. However, these approaches have had limited success due to both physical constraints as well as the challenge of estimating non-rigid, temporally varying, and patient-specific motionfields. To develop a novel reconstruction method which generates time-resolved, artifact-free images without estimation or explicit modeling of themotion. We describe an analysis-by-synthesis approach which progressively regresses a solution consistent with the acquired sinogram. In our method, we focus on the movement of object boundaries. Not only are the boundaries the source of image artifacts, but object boundaries can simultaneously be used to represent both the object as well as its motion over time without the need for an explicit motion model. We represent the object boundaries via a signed distance function (SDF) which can be efficiently modeled using neural networks. As a result, optimization can be performed under spatial and temporal smoothness constraints without the need for explicit motion estimation. We illustrate the utility of DiFiR-CT in three imaging scenarios with increasing motion complexity: translation of a small circle, heart-like change in an ellipse's diameter, and a complex topological deformation. Compared to filtered backprojection, DiFiR-CT provides high quality image reconstruction for all three motions without hyperparameter tuning or change to the architecture. We also evaluate DiFiR-CT's robustness to noise in the acquired sinogram and found its reconstruction to be accurate across a wide range of noise levels. Lastly, we demonstrate how the approach could be used for multi-intensity scenes and illustrate the importance of the initial segmentation providing a realistic initialization. Code and supplemental movies are available at https://kunalmgupta.github.io/projects/DiFiR-CT.html. Projection data can be used to accurately estimate a temporally-evolving scene without the need for explicit motion estimation using a neural implicit representation and analysis-by-synthesisapproach.

A Comparative Analysis of End-To-End Trainable Motion Compensation based Video Super-Resolution Techniques

Video Super-Resolution (VSR) is gaining massive attention in the digital age across various application domains such as video surveillance and ultra-high-definition displays. Deep learning (DL) has recently made significant progress in research and industry domains. Existing researchers used numerous VSR methods based on deep learning to improve the resolution. VSR is based on inter-frame information utilization. Video frames provide the leverage information of data that modalities record sequentially, i.e.; the object's temporal changes cause inconsistent measurements. Consequently, suitable models and algorithms must be developed to provide artifact-free images. This paper goes through two reconstruction methods incorporating various motion data sources. Initially, it describes an iterative approach that implicitly treats dynamic behavior as uncertainty in the forward model. Further direct reconstruction techniques can be derived for an explicit motion map. This paper compares both explicit and implicit motion compensation-based methods. Convolutional neural networks (CNN) and recurrent, residual network (RRN) based methods from a deep learning environment are analyzed to reconstruct the High-Resolution (HR) frames. This paper shows numerical findings for both methods using simulated data. Observations show that RRN can boost the super-resolution (SR) performance and achieve considerable visual quality after reconstruction; however, performance is limited to small datasets due to shallow network parameters. Extensive experiments show that RRN significantly impacts SR results due to its substantial computational efficiency.

Fluid Micelle Network for Image Super-Resolution Reconstruction.

Most existing convolutional neural-network-based super-resolution (SR) methods focus on designing effective neural blocks but rarely describe the image SR mechanism from the perspective of image evolution in the SR process. In this study, we explore a new research routine by abstracting the movement of pixels in the reconstruction process as the flow of fluid in the field of fluid dynamics (FD), where explicit motion laws of particles have been discovered. Specifically, a novel fluid micelle network is devised for image SR based on the theory of FD that follows the residual learning scheme but learns the residual structure by solving the finite difference equation in FD. The pixel motion equation in the SR process is derived from the Navier-Stokes (N-S) FD equation, establishing a guided branch that is aware of edge information. Thus, the second-order residual drives the network for feature extraction, and the guided branch corrects the direction of the pixel stream to supplement the details. Experiments on popular benchmarks and a real-world microscope chip image dataset demonstrate that the proposed method outperforms other modern methods in terms of both objective metrics and visual quality. The proposed method can also reconstruct clear geometric structures, offering the potential for real-world applications.

Video Denoising for Scenes With Challenging Motion: A Comprehensive Analysis and a New Framework

Challenging motion, which tends to cause artifacts, is a key problem in the video denoising task. Recent video denoising methods have attempted to address this problem. However, they usually provide general performance evaluation on the overall dataset and cannot provide a comprehensive analysis for the influence of different motion levels. Thus, we questioned whether these methods can effectively deal with different scene motions. To this end, we synthesize a dataset containing videos with different motion levels and capture a new dataset that consists of videos involving large-scale motion. Then, we provide a comprehensive analysis on the elaborately collected datasets and find that, as the motion level increases, the performance of the denoising models based on implicit motion estimation (IME) declines sharply, while explicit motion estimation (EME) contributes to a more robust denoising quality. Therefore, in this work, we present an EME-embedded progressive denoising framework that fully considers the relationship between the noise removal and motion estimation. Specifically, we decouple video denoising into spatial denoising, EME-based frame reconstruction, and temporal refining processes. Spatial denoising improves the accuracy of EME process in the case of videos suffering from heavy noise, while the temporal refining process refines the denoised frame by utilizing temporal redundancy of the reconstructed motion-free frames. Extensive experiments demonstrate that the proposed method outperforms existing state-of-the-art methods, especially for videos containing large-scale motion.

Video Chatting Platform with Video Enhancing Feature Using Super Resolution

Abstract: This paper describes the process of creating a Multi-User Video Chatting Platform with Agora Video SDK and a Django Backend. The Video Super Resolution technique is being used to improve the quality of video calls. VSR is the process of reconstructing high-resolution videos from low-resolution ones. We employ the Dynamic Up sampling Filters Without Explicit Motion Compensation technique for VSR. We reconstruct an HR image directly from the input image using dynamic Up sampling filters, and the fine details are added using the computed residual.

4DGolden-Angle Radial MRI at Subsecond Temporal Resolution.

Intraframe motion blurring, as a major challenge in free-breathing dynamic MRI, can be reduced if high temporal resolution can be achieved. To address this challenge, this work proposes a highly accelerated 4D (3D + time) dynamic MRI framework with subsecond temporal resolution that does not require explicit motion compensation. The method combines standard stack-of-stars golden-angle radial sampling and tailored GRASP-Pro (Golden-angle RAdial Sparse Parallel imaging with imProved performance) reconstruction. Specifically, 4D dynamic MRI acquisition is performed continuously without motion gating or sorting. The k-space centers in stack-of-stars radial data are organized to guide estimation of a temporal basis, with which GRASP-Pro reconstruction is employed to enforce joint low-rank subspace and sparsity constraints. This new basis estimation strategy is the new feature proposed for subspace-based reconstruction in this work to achieve high temporal resolution (e.g., subsecond/3D volume). It does not require sequence modification to acquire additional navigation data, it is compatible with commercially available stack-of-stars sequences, and it does not need an intermediate reconstruction step. The proposed 4D dynamic MRI approach was tested in abdominal motion phantom, free-breathing abdominal MRI, and dynamic contrast-enhanced MRI (DCE-MRI). Our results have shown that GRASP-Pro reconstruction with the new basis estimation strategy enables highly-accelerated 4D dynamic imaging at subsecond temporal resolution (with five spokes or less for each dynamic frame per image slice) for both free-breathing non-DCE-MRI and DCE-MRI. In the abdominal phantom, better image quality with lower root mean square error and higher structural similarity index was achieved using GRASP-Pro compared with standard GRASP. With the ability to acquire each 3D image in less than 1 s, intraframe respiratory blurring can be intrinsically reduced for body applications with our approach, which eliminates the need for explicit motion detection and motion compensation.