High Frame Research Articles

Vision-based tactile intelligence with soft robotic metamaterial

Robotic metamaterials represent an innovative approach to creating synthetic structures that combine desired material characteristics with embodied intelligence, blurring the boundaries between materials and machinery. Inspired by the functional qualities of biological skin, integrating tactile intelligence into these materials has gained significant interest for research and practical applications. This study introduces a Soft Robotic Metamaterial (SRM) design featuring omnidirectional adaptability and superior tactile sensing, combining vision-based motion tracking and machine learning. The study compares two sensory integration methods to a state-of-the-art motion tracking system and force/torque sensor baseline: an internal-vision design with high frame rates and an external-vision design offering cost-effectiveness. The results demonstrate the internal-vision SRM design achieving an impressive tactile accuracy of 98.96%, enabling soft and adaptive tactile interactions, especially beneficial for dexterous robotic grasping. The external-vision design offers similar performance at a reduced cost and can be adapted for portability, enhancing material science education and robotic learning. This research significantly advances tactile sensing using vision-based motion tracking in soft robotic metamaterials, and the open-source availability on GitHub fosters collaboration and further exploration of this innovative technology (https://github.com/bionicdl-sustech/SoftRoboticTongs).

FAVER: Blind quality prediction of variable frame rate videos

Video quality assessment (VQA) remains an important and challenging problem that affects many applications at the widest scales. Recent advances in mobile devices and cloud computing techniques have made it possible to capture, process, and share high resolution, high frame rate (HFR) videos across the Internet nearly instantaneously. Being able to monitor and control the quality of these streamed videos can enable the delivery of more enjoyable content and perceptually optimized rate control. Accordingly, there is a pressing need to develop VQA models that can be deployed at enormous scales. While some recent effects have been applied to full-reference (FR) analysis of variable frame rate and HFR video quality, the development of no-reference (NR) VQA algorithms targeting frame rate variations has been little studied. Here, we propose a first-of-a-kind blind VQA model for evaluating HFR videos, which we dub the Framerate-Aware Video Evaluator w/o Reference (FAVER). FAVER uses extended models of spatial natural scene statistics that encompass space–time wavelet-decomposed video signals, and leverages the advantages of the deep neural network to provide motion perception, to conduct efficient frame rate sensitive quality prediction. Our extensive experiments on several HFR video quality datasets show that FAVER outperforms other blind VQA algorithms at a reasonable computational cost. To facilitate reproducible research and public evaluation, an implementation of FAVER is being made freely available online: https://github.com/uniqzheng/HFR-BVQA.

Value of high frame rate contrast enhanced ultrasound in gallbladder wall thickening in non-acute setting

BackgroundUltrasound (US) has been widely used in screening and differential diagnosis of gallbladder wall thickening (GWT). However, the sensitivity and specificity for diagnosing wall-thickening type gallbladder cancer are limited, leading to delayed treatment or overtreatment. We aim to explore the value of high frame rate contrast enhanced ultrasound (H-CEUS) in distinguishing wall-thickening type gallbladder cancer (malignant) from GWT mimicking malignancy (benign).MethodsThis retrospective study enrolled consecutive patients with non-acute GWT who underwent US and H-CEUS examination before cholecystectomy. Clinical information, US image and H-CEUS image characteristics between malignant and benign GWT were compared. The independent risk factors for malignant GWT on H-CEUS images were selected by multivariate logistic regression analysis. The diagnostic performance of H-CEUS in determining malignant GWT was compared with that of the gallbladder reporting and data system (GB-RADS) score.ResultsForty-six patients included 30 benign GWTs and 16 malignant GWTs. Only mural layering and interface with liver on US images were significantly different between malignant and benign GWT (P < 0.05). Differences in enhancement direction, vascular morphology, serous layer continuity, wash-out time and mural layering in the venous phase of GWT on H-CEUS images were significant between malignant and benign GWT (P < 0.05). The sensitivity, specificity and accuracy of H-CEUS based on enhancement direction, vascular morphology and wash-out time in the diagnosis of malignant GWT were 93.75%, 90.00%, and 91.30%, respectively. However, the sensitivity, specificity and accuracy of the GB-RADS score were only 68.75%, 73.33% and 71.74%, respectively. The area under ROC curve (AUC) of H-CEUS was significantly higher than that of the GB-RADS score (AUC = 0.965 vs. 0.756).ConclusionsH-CEUS can accurately detect enhancement direction, vascular morphology and wash-out time of GWT, with a higher diagnostic performance than the GB-RADS score in determining wall-thickening type gallbladder cancer. This study provides a novel imaging means with high accuracy for the diagnosis of wall-thickening type gallbladder cancer, thus may be better avoiding delayed treatment or overtreatment.

Dynamic light scattering and laser speckle contrast imaging of the brain: theory of the spatial and temporal statistics of speckle pattern evolution.

Dynamic light scattering (DLS) and laser speckle contrast imaging (LSCI) are closely related techniques that exploit the statistics of speckle patterns, which can be utilized to measure cerebral blood flow (CBF). Conventionally, the temporal speckle intensity auto-correlation function is calculated in DLS, while the spatial speckle contrast Ks is calculated in LSCI measurements. Due to the rapid development of CMOS detection technology with increased camera frame rates while still maintaining a large number of pixels, the ensemble or spatial average of as well as the temporal contrast Kt can be easily calculated and utilized to quantify CBF. Although many models have been established, a proper summary is still lacking to fully characterize DLS and LSCI measurements for spatial and temporal statistics, laser coherence properties, various motion types, etc. As a result, there are many instances where theoretical models are misused. For instance, mathematical formulas derived in the diffusive regime or for ergodic systems are sometimes applied to small animal brain measurements, e.g., mice brains, where the assumptions are not valid. Therefore, we aim to provide a review of the speckle theory for both DLS and LSCI measurements with detailed derivations from first principles, taking into account non-ergodicity, spatial and temporal statistics of speckles, scatterer motion types, and laser coherence properties. From these calculations, we elaborate on the differences between spatial and temporal averaging for DLS and LSCI measurements that are typically ignored but can result in inaccurate measurements of blood flow, particularly the spatially varying nature of the static component in and Kt. We also obtained maps in in vivo mouse brain measurements using high frame rate CMOS cameras which have not been demonstrated before, and compared with and Ks,t. This work provides a useful guide for choosing the correct model to analyze spatial and temporal speckle statistics in in-vivo DLS and LSCI measurements.

An Image Histogram Equalization Acceleration Method for Field-Programmable Gate Arrays Based on a Two-Dimensional Configurable Pipeline.

New artificial intelligence scenarios, such as high-precision online industrial detection, unmanned driving, etc., are constantly emerging and have resulted in an increasing demand for real-time image processing with high frame rates and low power consumption. Histogram equalization (HE) is a very effective and commonly used image preprocessing algorithm designed to improve the quality of image processing results. However, most existing HE acceleration methods, whether run on general-purpose CPUs or dedicated embedded systems, require further improvement in their frame rate to meet the needs of more complex scenarios. In this paper, we propose an HE acceleration method for FPGAs based on a two-dimensional configurable pipeline architecture. We first optimize the parallelizability of HE with a fully configurable two-dimensional pipeline architecture according to the principle of adapting the algorithm to the hardware, where one dimension can compute the cumulative histogram in parallel and the other dimension can process multiple inputs simultaneously. This optimization also helps in the construction of a simple architecture that achieves a higher frequency when implementing HE on FPGAs, which consist of configurable input units, calculation units, and output units. Finally, we optimize the pipeline and critical path of the calculation units. In the experiments, we deploy the optimized HE on a VCU118 test board and achieve a maximum frequency of 891 MHz (which is up to 22.6 times more acceleration than CPU implementations), as well as a frame rate of 1899 frames per second for 1080p images.

An Embedded-GPU-Based Scheme for Real-Time Imaging Processing of Unmanned Aerial Vehicle Borne Video Synthetic Aperture Radar

The UAV-borne video SAR (ViSAR) imaging system requires miniaturization, low power consumption, high frame rates, and high-resolution real-time imaging. In order to satisfy the requirements of real-time imaging processing for the UAV-borne ViSAR under limited memory and parallel computing resources, this paper proposes a method of embedded GPU-based real-time imaging processing for the UAV-borne ViSAR. Based on a parallel programming model of the compute unified device architecture (CUDA), this paper designed a parallel computing method for range-Doppler (RD) and map drift (MD) algorithms. By utilizing the advantages of the embedded GPU characterized with parallel computing, we improved the processing speed of real-time ViSAR imaging. This paper also adopted a unified memory management method, which greatly reduces data replication and communication latency between the CPU and the GPU. The data processing of 2048 × 2048 points took only 1.215 s on the Jetson AGX Orin platform to form a nine-consecutive-frame image with a resolution of 0.15 m, with each frame taking only 0.135 s, enabling real-time imaging at a high frame rate of 5 Hz. In actual testing, continuous mapping can be achieved without losing the scenes, intuitively obtaining the dynamic observation effects of the area. The processing results of the measured data have verified the reliability and effectiveness of the proposed scheme, satisfying the processing requirements for real-time ViSAR imaging.

Deciphering cancer genomes with GenomeSpy: a grammar-based visualization toolkit.

Visualization is an indispensable facet of genomic data analysis. Despite the abundance of specialized visualization tools, there remains a distinct need for tailored solutions. However, their implementation typically requires extensive programming expertise from bioinformaticians and software developers, especially when building interactive applications. Toolkits based on visualization grammars offer a more accessible, declarative way to author new visualizations. Yet, current grammar-based solutions fall short in adequately supporting the interactive analysis of large datasets with extensive sample collections, a pivotal task often encountered in cancer research. We present GenomeSpy, a grammar-based toolkit for authoring tailored, interactive visualizations for genomic data analysis. By using combinatorial building blocks and a declarative language, users can implement new visualization designs easily and embed them in web pages or end-user-oriented applications. A distinctive element of GenomeSpy's architecture is its effective use of the graphics processing unit in all rendering, enabling a high frame rate and smoothly animated interactions, such as navigation within a genome. We demonstrate the utility of GenomeSpy by characterizing the genomic landscape of 753 ovarian cancer samples from patients in the DECIDER clinical trial. Our results expand the understanding of the genomic architecture in ovarian cancer, particularly the diversity of chromosomal instability. GenomeSpy is a visualization toolkit applicable to a wide range of tasks pertinent to genome analysis. It offers high flexibility and exceptional performance in interactive analysis. The toolkit is open source with an MIT license, implemented in JavaScript, and available at https://genomespy.app/.

The Onset and Early Stages of Dynamic Wetting of Superspreading and Non-Superspreading Trisiloxane Surfactant Solutions on Hydrophobic Surfaces

The onset and early stages of dynamic wetting on different hydrophobic surfaces is investigated experimentally for aqueous solutions of two commercial trisiloxane surfacants of similar chemical structure, one of which exhibits superspreading behaviour, in order to investigate the spreading dynamics independently of the surface activity. Superspreading, or the ability of a surfactant solution to spread on a surface beyond the state determined by thermodynamic equilibrium, has been investigated for more than 30 years however its physical mechanism remains poorly understood to date despite its important applications in the formulation of agrochemicals. Surfactant solutions were prepared by dissolving S233 and S240 surfactants (Evonik Industries AG, Essen, Germany) into de-ionised water at a weight concentration of 0.1%. Drops of surfactant solutions and pure water were deposited on three horizontal substrates with different wettability (equilibrium contact angle of water ranging between 55∘ and 100∘), and observed from below with a high-frame rate camera to visualise the advancing contact line. The spreading ratio of drops as a function of time was extracted from high-speed videos by digital image processing. Results reveal that the superspreading solution exhibits an intermittent spreading rate, as well as peculiar features of the contact line, which are not observed for the non-superspreading solution, and confirm the superspreading effect becomes less significant when the surface energy of the substrate is decreased.

Volumetric ultrasound localization microscopy with diverging cylindrical waves.

Transcranial ultrasound plays a limited role in neuroradiology due to its lack of resolution, planar imaging, and user-dependency. By breaching the diffraction limit using injected microbubbles, volumetric ultrasound localization microscopy (ULM) could help alleviate those issues. However, performing 3D ultrasound imaging at a high frame rate with sufficient signal-to-noise ratio to track individual microbubbles through the skull remains a challenge, especially with a portable scanner. In this study, we describe a ULM sequence suitable for volumetric transcranial imaging exploiting cylindrical emissions on multiplexed matrix probes, through simulations, hydrophone measurements, and flow phantoms. This geometry leads to a doubling of the peak acoustic pressure, up to 400 kPa, with respect to spherical emission and improved volume rate, up to 180 Hz. Cylindrical emissions also improve ULM saturation rate by 60% through a skull phantom. The assessment of microbubble velocity was also improved from 33% error in the average flow measured with spherical waves to a 5% error with cylindrical waves. Conversely, we demonstrate the detrimental impacts of cylindrical waves toward the field of view and isotropic sensitivity. Nevertheless, due to its enhanced signal-to-noise ratio and 3D nature, such a cylindrical volumetric sequence could be beneficial for ULM as a diagnostic tool in humans, especially when portability is a necessity.

Windowed Radon Transform and Tensor Rank-1 Decomposition for Adaptive Beamforming in Ultrafast Ultrasound.

Ultrafast ultrasound has recently emerged as an alternative to traditional focused ultrasound. By virtue of the low number of insonifications it requires, ultrafast ultrasound enables the imaging of the human body at potentially very high frame rates. However, unaccounted for speed-of-sound variations in the insonified medium often result in phase aberrations in the reconstructed images. The diagnosis capability of ultrafast ultrasound is thus ultimately impeded. Therefore, there is a strong need for adaptive beamforming methods that are resilient to speed-of-sound aberrations. Several of such techniques have been proposed recently but they often lack parallelizability or the ability to directly correct both transmit and receive phase aberrations. In this article, we introduce an adaptive beamforming method designed to address these shortcomings. To do so, we compute the windowed Radon transform of several complex radio-frequency images reconstructed using delay-and-sum. Then, we apply to the obtained local sinograms weighted tensor rank-1 decompositions and their results are eventually used to reconstruct a corrected image. We demonstrate using simulated and in-vitro data that our method is able to successfully recover aberration-free images and that it outperforms both coherent compounding and the recently introduced SVD beamformer. Finally, we validate the proposed beamforming technique on in-vivo data, resulting in a significant improvement of image quality compared to the two reference methods.

Tackling Event-Based Lip-Reading by Exploring Multigrained Spatiotemporal Clues.

Automatic lip-reading (ALR) is the task of recognizing words based on visual information obtained from the speaker's lip movements. In this study, we introduce event cameras, a novel type of sensing device, for ALR. Event cameras offer both technical and application advantages over conventional cameras for ALR due to their higher temporal resolution, less redundant visual information, and lower power consumption. To recognize words from the event data, we propose a novel multigrained spatiotemporal features learning framework, which is capable of perceiving fine-grained spatiotemporal features from microsecond time-resolved event data. Specifically, we first convert the event data into event frames of multiple temporal resolutions to avoid losing too much visual information at the event representation stage. Then, they are fed into a multibranch subnetwork where the branch operating on low-rate frames can perceive spatially complete but temporally coarse features, while the branch operating on high frame rate can perceive spatially coarse but temporally fine features. Thus, fine-grained spatial and temporal features can be simultaneously learned by integrating the features perceived by different branches. Furthermore, to model the temporal relationships in the event stream, we design a temporal aggregation subnetwork to aggregate the features perceived by the multibranch subnetwork. In addition, we collect two event-based lip-reading datasets (DVS-Lip and DVS-LRW100) for the study of the event-based lip-reading task. Experimental results demonstrate the superiority of the proposed model over the state-of-the-art event-based action recognition models and video-based lip-reading models.

Deep Power-aware Tunable Weighting for Ultrasound Microvascular Imaging.

Ultrasound microvascular imaging (UMI), including ultrafast power Doppler imaging (uPDI) and ultrasound localization microscopy (ULM), obtains blood flow information through plane wave transmissions at high frame rates. However, low signal-to-noise ratio of plane waves causes low image quality. Adaptive beamformers have been proposed to suppress noise energy to achieve higher image quality accompanied by increasing computational complexity. Deep learning (DL) leverages powerful hardware capabilities to enable rapid implementation of noise suppression at the cost of flexibility. To enhance the applicability of DL-based methods, in this work, we propose a deep power-aware tunable (DPT) weighting (i.e., postfilter) for delay-and-sum (DAS) beamforming to improve UMI by enhancing plane wave images. The model, called Yformer is a hybrid structure combining convolution and Transformer. With the DAS beamformed and compounded envelope image as input, Yformer can estimate both noise power and signal power. Furthermore, we utilize the obtained powers to compute pixel-wise weights by introducing a tunable noise control factor, which is tailored for improving the quality of different UMI applications. In vivo experiments on the rat brain demonstrate that Yformer can accurately estimate the powers of noise and signal with the structural similarity index (SSIM) higher than 0.95. The performance of the DPT weighting is comparable to that of superior adaptive beamformer in uPDI with low computational cost. The DPT weighting was then applied to four different datasets of ULM, including public simulation, public rat brain, private rat brain, and private rat liver datasets, showing excellent generalizability using the model trained by the private rat brain dataset only. In particular, our method indirectly improves the resolution of liver ULM from 25.24 μm to 18.77 μm by highlighting small vessels. In addition, the DPT weighting exhibits more details of blood vessels with faster processing, which has the potential to facilitate the clinical applications of high-quality UMI.

Curved toroidal row column addressed transducer for 3D ultrafast ultrasound imaging.

3D Imaging of the human heart at high frame rate is of major interest for various clinical applications. Electronic complexity and cost has prevented the dissemination of 3D ultrafast imaging into the clinic. Row column addressed (RCA) transducers provide volumetric imaging at ultrafast frame rate by using a low electronic channel count, but current models are ill-suited for transthoracic cardiac imaging due to field-of-view limitations. In this study, we proposed a mechanically curved RCA with an aperture adapted for transthoracic cardiac imaging (24 × 16 mm²). The RCA has a toroidal curved surface of 96 elements along columns (curvature radius rC = 4.47 cm) and 64 elements along rows (curvature radius rR = 3 cm). We implemented delay and sum beamforming with an analytical calculation of the propagation of a toroidal wave which was validated using simulations (Field II). The imaging performance was evaluated on a calibrated phantom. Experimental 3D imaging was achieved up to 12 cm deep with a total angular aperture of 30° for both lateral dimensions. The Contrast-to-Noise ratio increased by 12 dB from 2 to 128 virtual sources. Then, 3D Ultrasound Localization Microscopy (ULM) was characterized in a sub-wavelength tube diameter. Finally, 3D ULM was demonstrated on a perfused ex-vivo swine heart to image the coronary microcirculation.

Scene-aware Foveated Neural Radiance Fields.

Foveated rendering provides an idea for improving the image synthesis performance of neural radiance fields (NeRF) methods. In this paper, we propose a scene-aware foveated neural radiance fields method to synthesize high-quality foveated images in complex VR scenes at high frame rates. Firstly, we construct a multi-ellipsoidal neural representation to enhance the neural radiance field's representation capability in salient regions of complex VR scenes based on the scene content. Then, we introduce a uniform sampling based foveated neural radiance field framework to improve the foveated image synthesis performance with one-pass color inference, and improve the synthesis quality by leveraging the foveated scene-aware objective function. Our method synthesizes high-quality binocular foveated images at the average frame rate of 66 frames per second (FPS) in complex scenes with high occlusion, intricate textures, and sophisticated geometries. Compared with the state-of-the-art foveated NeRF method, our method achieves significantly higher synthesis quality in both the foveal and peripheral regions with 1.41-1.46× speedup. We also conduct a user study to prove that the perceived quality of our method has a high visual similarity with the ground truth.

Enhancing spatial resolution in MÖNCH for electron microscopy via deep learning

Hybrid Pixel Detectors (HPDs) are highly suitable in diffraction-based electron microscopy due to their high frame rates (> 1 kHz), high dynamic range, and good radiation hardness. However, their use in imaging applications has been limited by their relatively large pixel size (≥ 55 μm) and high-energy (>80 keV) electrons scattering over multiple pixels in the sensor layer. To realize the full potential of fast, radiation-hard HPDs across electron microscopy modalities, we developed deep learning techniques to precisely localize the impact point of incident electrons in MÖNCH, a charge integrating HPD with 25 μm pixel size. With neural network models trained using labeled data via simulations and experimental measurements, the best spatial resolution obtained, defined in terms of the root mean squared error, was 0.60 pixels for 200 keV electrons, a three-fold improvement over a simple charge centroid method. This article presents the training sample generation, deep learning model design, training results, and imaging outcomes for a sample containing gold nanoparticles.

Affordable Phenotyping at the Edge for High-Throughput Detection of Hypersensitive Reaction Involving Cotyledon Loss.

The use of low-cost depth imaging sensors is investigated to automate plant pathology tests. Spatial evolution is explored to discriminate plant resistance through the hypersensitive reaction involving cotyledon loss. A high temporal frame rate and a protocol operating with batches of plants enable to compensate for the low spatial resolution of depth cameras. Despite the high density of plants, a spatial drop of the depth is observed when the cotyledon loss occurs. We introduce a small and simple spatiotemporal feature space which is shown to carry enough information to automate the discrimination between batches of resistant (loss of cotyledons) and susceptible plants (no loss of cotyledons) with 97% accuracy and with a timing 30 times faster than for human annotation. The robustness of the method-in terms of density of plants in the batch and possible internal batch desynchronization-is assessed successfully with hundreds of varieties of Pepper in various environments. A study on the generalizability of the method suggests that it can be extended to other pathosystems and also to segregating plants, i.e., intermediate state with batches composed of resistant and susceptible plants. The imaging system developed, combined with the feature extraction method and classification model, provides a full pipeline with unequaled throughput and cost efficiency by comparison with the state-of-the-art one. This system can be deployed as a decision-support tool but is also compatible with a standalone technology where computation is done at the edge in real time.

EventHDR: From Event to High-Speed HDR Videos and Beyond.

Event cameras are innovative neuromorphic sensors that asynchronously capture the scene dynamics. Due to the event-triggering mechanism, such cameras record event streams with much shorter response latency and higher intensity sensitivity compared to conventional cameras. On the basis of these features, previous works have attempted to reconstruct high dynamic range (HDR) videos from events, but have either suffered from unrealistic artifacts or failed to provide sufficiently high frame rates. In this paper, we present a recurrent convolutional neural network that reconstruct high-speed HDR videos from event sequences, with a key frame guidance to prevent potential error accumulation caused by the sparse event data. Additionally, to address the problem of severely limited real dataset, we develop a new optical system to collect a real-world dataset with paired high-speed HDR videos and event streams, facilitating future research in this field. Our dataset provides the first real paired dataset for event-to-HDR reconstruction, avoiding potential inaccuracies from simulation strategies. Experimental results demonstrate that our method can generate high-quality, high-speed HDR videos. We further explore the potential of our work in cross-camera reconstruction and downstream computer vision tasks, including object detection, panoramic segmentation, optical flow estimation, and monocular depth estimation under HDR scenarios.

Neuromorphic Synergy for Video Binarization.

Bimodal objects, such as the checkerboard pattern used in camera calibration, markers for object tracking, and text on road signs, to name a few, are prevalent in our daily lives and serve as a visual form to embed information that can be easily recognized by vision systems. While binarization from intensity images is crucial for extracting the embedded information in the bimodal objects, few previous works consider the task of binarization of blurry images due to the relative motion between the vision sensor and the environment. The blurry images can result in a loss in the binarization quality and thus degrade the downstream applications where the vision system is in motion. Recently, neuromorphic cameras offer new capabilities for alleviating motion blur, but it is non-trivial to first deblur and then binarize the images in a real-time manner. In this work, we propose an event-based binary reconstruction method that leverages the prior knowledge of the bimodal target's properties to perform inference independently in both event space and image space and merge the results from both domains to generate a sharp binary image. We also develop an efficient integration method to propagate this binary image to high frame rate binary video. Finally, we develop a novel method to naturally fuse events and images for unsupervised threshold identification. The proposed method is evaluated in publicly available and our collected data sequence, and shows the proposed method can outperform the SOTA methods to generate high frame rate binary video in real-time on CPU-only devices.

IN SITU MELT TEMPERATURE ANALYSIS DURING HIGH PRESSURE GAS ATOMIZATION OF LIQUID METALS

Imaging of the melt plume during high pressure gas atomization (HPGA) using consumer digital single lens reflex (DSLR) equipment provides useful information about the process. Color imaging and high spatial resolution can be a useful adjunct to the more widely reported imaging using specialist high frame rate cameras. With knowledge of the camera's color response curves, the ratio of the signals in the red, green, and blue channels can be used to make spatially resolved temperature estimates of the material within the melt plume. Moreover, by combining these temperature estimates, which depend only upon intensity ratios, with the actual intensity of the optical signal, we propose that it is possible to obtain estimates of the relative surface area of the melt within the plume. This in turn can be related to the local melt fragmentation rate within the atomization plume.

Contrast-Free High Frame Rate Ultrasound Imaging for Assessment of Vascular Remodeling During Wound Healing

BackgroundMonitoring of wound healing progression is critical due to the risk of infection, non-healing wounds, or evolution towards a chronic state. Tissue vasculature is one of the most representative features reflecting healing status. This study explores the feasibility of vascular ultrasound imaging of open wounds and the extraction of vascular-related features in a longitudinal study. Material and methodsC57BL/6 mice received a 1 cm-diameter full-thickness wound on their dorsum and were imaged using ultrasound from the surgical day (Day 0) to 25 days post-wounding. The high frame rate, plane waves acquisitions with a 15 MHz transducer were postprocessed with Singular Value Decomposition (SVD) filtering to provide vascular information. ResultsVascularity Index (VI) calculations showed an increased vascular signal in the wound from Day 2 post-wounding and were significantly higher from day 6 to day 10 post-wounding compared to Day 0 (p<0.05). VI values were back to the basal level after 3 weeks. In comparison, no significant difference was highlighted for the vascular signal in the peri-wound area. ConclusionsThese results show that vascular ultrasound imaging can be applied to track vascular changes of open wounds during the healing process. This approach may also be extended to other types of wounds for detecting early signs likely to cause complications.