Real-time Imaging Research Articles

Real-Time Millimeter-Wave Imaging With Linear Frequency Modulation Radar and Scattered Power Mapping

Real-Time Millimeter-Wave Imaging With Linear Frequency Modulation Radar and Scattered Power Mapping

Design and validation of a high-speed hyperspectral laparoscopic imaging system.

Minimally invasive surgery (MIS) has shown vast improvement over open surgery by reducing post-operative stays, intraoperative blood loss, and infection rates. However, in spite of these improvements, there are still prevalent issues surrounding MIS that may be addressed through hyperspectral imaging (HSI). We present a laparoscopic HSI system to further advance the field of MIS. We present an imaging system that integrates high-speed HSI technology with a clinical laparoscopic setup and validate the system's accuracy and functionality. Different configurations that cover the visible (VIS) to near-infrared (NIR) range of electromagnetism are assessed by gauging the spectral fidelity and spatial resolution of each hyperspectral camera. Standard Spectralon reflectance tiles were used to provide ground truth spectral footprints to compare with those acquired by our system using the root mean squared error (RMSE). Demosaicing techniques were investigated and used to measure and improve spatial resolution, which was assessed with a USAF resolution test target. A perception-based image quality evaluator was used to assess the demosaicing techniques we developed. Two configurations of the system were developed for evaluation. The functionality of the system was investigated in a phantom study and by imaging ex vivo tissues. Multiple configurations of our system were tested, each covering different spectral ranges, including VIS (460 to 600nm), red/NIR (RNIR) (610 to 850nm), and NIR (665 to 950nm). Each configuration is capable of achieving real-time imaging speeds of up to 20 frames per second. RMSE values of , , and 3.47% were achieved for the VIS, RNIR, and NIR systems, respectively. We obtained sub-millimeter resolution using our demosaicing techniques. We developed and validated a high-speed hyperspectral laparoscopic imaging system. The HSI system can be used as an intraoperative imaging tool for tissue classification during laparoscopic surgery.

Technology Focus: Offshore Facilities (September 2024)

Offshore wind power involves generating electricity from wind farms located at sea. Because wind speeds are generally higher offshore than on land, these farms produce more electricity for the same amount of installed capacity. Both wind and solar energy are renewable and do not emit carbon, which helps reduce dependence on fossil fuels such as oil and gas. The papers synopsized in this year’s Offshore Facilities feature cover a breadth of related subjects that will be critical to the future of the industry. Automation in the offshore oil and gas industry makes production safer, faster, and more efficient. It reduces human error, improves quality control, and allows real-time monitoring of operations. Machine learning improves offshore industries by enhancing safety, efficiency, and sustainability It tracks and predicts environmental changes, such as oil spills or marine life patterns, aiding in better environmental management and response. Remotely operated vehicles are used in offshore operations to inspect, maintain, and repair underwater infrastructure; the offshore industry uses 3D video imaging technology to provide detailed real-time images of the underwater environment. Corrosion in offshore environments occurs because of factors such as seawater, sea salts, temperature, salinity, and moisture. It is crucial to monitor corrosion because it can lead to major structural failures. Hydrogen can be produced offshore using renewable energy sources such as wind and solar, which do not emit greenhouse gases and help combat climate change. Supply-chain logistics boost efficiency by cutting costs, removing unnecessary steps, and speeding up product delivery. An efficient supply chain can lead to higher revenue, lower operational costs, and better overall performance. Decommissioning is the final stage of an oil or gas project and involves significant technical, economic, social, and environmental challenges. It is a lengthy, costly, and complex process that must be carefully planned and managed to avoid serious problems. Several ideas have come to the surface that show innovative ways to repurpose a decommissioned platform or ways to cut the cost of decommissioning. Recommended additional reading at OnePetro: www.onepetro.org. SPE 217706 Dynamic Simulation of Riser-Gas Handling With a Fixed-Choke Constant Outflow by Zhaoguang Yuan, SLB, et al. OTC 35092 Reducing Carbon Footprint by Using Fiber-Reinforced Polymers in FPSOs by S. Eves, NOV, et al. OTC 35393 Challenges and Approaches to Solutions for Green Decommissioning and Recycling of Offshore Facilities by Carlos Alberto Machado, American Bureau of Shipping, et al.

Assessment of Point-of-Care Ultrasound (POCUS) Knowledge Among Lebanese Medical Residents.

Over the past few years, point-of-care ultrasound (POCUS) has emerged as a critical diagnostic tool in emergency medicine, providing real-time imaging at the bedside. This study aims to assess POCUS knowledge and competency among medical residents in Lebanon, identify possible gaps and deficiencies in their training, and recommend guidelines for further improvement of the curriculum in Lebanese medical schools and residency programs. Our study reveals that 58.3% (N=119) of resident doctors from multiple specialties in Lebanon have only basic knowledge about POCUS, 19.6% (N=40) have no knowledge, and only 21.6% (N=44) have sufficient knowledge to perform diagnostic studies on a routine basis. Lebanese medical residents currently possess suboptimal POCUS knowledge and proficiency due to disparities in training and educational obstacles. To address this, residency programs should focus on standardized POCUS training, simulation-based learning, and faculty development. This approach will help ensure residents gain the necessary skills to use POCUS effectively in clinical practice.

A hybrid hue saturation lightness, gray level co-occurrence matrix, and k-nearest neighbour for palm-sugar classification

<span lang="EN-US">In recent years, there has been an increasing demand for high-quality raw materials driven by consumers and the food industry. This study aims to build a model to predict the type of </span><span lang="IN">palm</span><span lang="EN-US"> sugar using a hybrid method of hue</span><span lang="IN">-</span><span lang="EN-US">saturation</span><span lang="IN">-l</span><span lang="EN-US">ightness (HSL), gray level co-occurrence matrix (GLCM), and K-nearest neighbor (KNN). The price of palm sugar is determined based on the type and ingredients used. However, due to the lack of public knowledge in distinguishing the types of </span><span lang="IN">palm</span><span lang="EN-US"> sugar, there is the potential for price manipulation that can harm the community. The accuracy rate of 9</span><span lang="IN">7.6</span><span lang="EN-US">% of the palm sugar type prediction results shows that the model that was built has worked very well. The results have practical implications, such as developing automated systems to classify palm species in specific industries to benefit economics and operational efficiency. Future research directions may explore the integration of advanced machine-learning techniques and real-time image processing for further improving classification performance and scalability in industrial applications</span><span lang="IN">.</span>

Automatic human height measurement system based on camera sensor with deep-learning and linear regression analysis

This study proposes a new approach for automatically measuring human height using a camera sensor with deep learning and linear regression analysis. The camera sensor is used to capture real-time images of human objects. The image is then processed with a YOLO4-based convolutional neural network (CNN) to separate the region of interest (ROI) of the human object from the background. The pixel value of the ROI vertical line is then converted into height in centimeters by the linear regression equation. The system was tested on 40 primary samples, with 20 samples used as control data and 20 samples used as test data. From the results of testing 20 control data samples, the linear regression equation was obtained as y' = 0.4034x + 24.938, which was then applied to convert the system's predicted height in centimeters for 20 test samples. The test results for 20 test samples showed that an average F1_score was 1, the R_square obtained was 0.93, the root mean square errors (RMSE) was 0.02, and the percentage of accuracy was 99.00%. The test results showed that the system was able to automatically detect human height with a very high level of correlation/similarity and accuracy between actual and predicted height.

A Study on the Improvement of YOLOv5 and the Quality Detection Method for Cork Discs

Combining machine vision and deep learning, optical detection technology can achieve intelligent inspection. To address the issues of low efficiency and poor consistency in the quality classification of cork discs used for making badminton heads, research on optimizing the YOLOv5 image-processing algorithm was conducted and applied to cork disc quality detection. Real-time images of cork discs were captured using industrial cameras, and a dataset was independently constructed. A GAN-based defect synthesis algorithm was employed to resolve the lack of defect samples. An attention mechanism was embedded in the YOLOv5 backbone network to enhance feature representation. The number of anchors in the YOLOv5 detection layer was reduced to address similar sample sizes, a center-matching strategy was designed to balance positive samples, and a shortest-distance label assignment algorithm was developed to eliminate ambiguities, improving accuracy and reducing postprocessing complexity. Detection results were integrated into quality classification. Experiments on the NVIDIA RTX3080 GPU demonstrated that the optimized algorithm improved the original YOLOv5 F1 score by 2.4% and mF1 score by 9.0%, achieving a quality classification F1 score of 95.1%, a processing speed of 178.5 FPS, and an mAP of 81.5%. Comparative experiments showed that the improved algorithm achieved the best detection accuracy on the cork disc dataset while maintaining high processing speed.

Preoperative and Intraoperative CT Imaging for Orbital Foreign Bodies Identification and Surgical Planning in Veterinary Medicine

Abstract The goal of this study was to evaluate sensitivity of intraoperative Computed Tomography (iCT) imaging for the detection of orbital foreign bodies (OFBs) in an in vitro model and evaluate iCT efficacy for surgical planning of OFB detection and removal in veterinary patients. Three canine patients were presented to our hospital for potential orbital foreign body removal. In vitro studies were conducted using a canine skull model with placement of various OFBs. Four different examiners utilized CT imaging to evaluate the detection of OFBs. A surgical navigation system was employed to assess the feasibility of stereotactic orbital foreign body retrieval in vitro. iCT imaging was applied for surgical planning and guidance of orbital surgery for the removal of OFBs in three clinical patients. In vitro experiments revealed a high detection rate for objects with high radiopacity such as metal and glass. The detection rate for organic foreign bodies such as wood was moderate, while for plastic foreign bodies ranged from moderate to low. Navigation was successfully used for OFB retrieval. iCT was effectively employed for detecting OFBs in clinical patients. However, porcupine quills were better detected using standard ultrasound imaging. The use of iCT potentially represents an evolving technological practice that enables real-time imaging to improve the precision of surgical procedures.

Smart farming: Leveraging IoT and deep learning for sustainable tomato cultivation and pest management

Since the world's population is rising continuously, more cultivable land is being utilized for their dwellings. As a result, the amount of food supply is decreasing day by day. In order to address the food shortage, a proper plan and technological breakthroughs is must. Tomato is a kind of vegetable which has the healthy ingredients and essential for our daily food list. The proposed system suggests an IoT based tomato cultivation and pest management system, with the help of deep learning methods. In the IoT implementation, camera module and moisture sensor are used to collect images of tomato plant, soil condition respectively. Based on the moisture content, the water pump will supply the water when it necessary. Besides, the real-time images of tomato leaf will be sent to the server to identify and classify natural enemies like various insect species. In the proposed system seven types of pests are identified with the help of ten deep learning models like InceptionV3, Xception, InceptionResNetV2, MobileNet, MobileNetV2, MobileNetV3Large, MobileNetV3Small, DenseNet121, DenseNet169, DenseNet201. This study has trained with leaves and insects separately to identify whether an image from a tomato plant is insectoid or not. 458 images of pests and 912 images of leaves are utilized in the proposed architecture. The accuracy of classifying insects or leaves using DenseNet201 is 100 ​%. The highest accuracy of 94 ​% is obtained to classify the different insects using the DenseNet201 model.

Transcranial focused ultrasound stimulation enhances cerebrospinal fluid movement: Real-time in vivo two-photon and widefield imaging evidence

BackgroundCerebrospinal fluid (CSF) flow is crucial for brain homeostasis and its dysfunction is highly associated with neurodegenerative diseases. Restoring CSF circulation is proposed as a key strategy for the treatment of the diseases. Among the methods to improve CSF circulation, focused ultrasound (FUS) stimulation has emerged as a promising non-invasive brain stimulation technique, with effectiveness evidenced by ex vivo studies. However, due to technical disturbances in in vivo imaging combined with FUS, direct evidence of real-time in vivo CSF flow enhancement by FUS remains elusive. ObjectiveTo investigate whether FUS administered through the skull base can enhance CSF influx in living animals with various real-time imaging techniques. MethodsWe demonstrate a novel method of applying FUS through the skull base, facilitating cortical CSF influx, evidenced by diverse in vivo imaging techniques. Acoustic simulation confirmed effective sonication of our approach through the skull base. After injecting fluorescent CSF tracers into cisterna magna, FUS was administered at the midline of the jaw through the skull base for 30 min, during which imaging was performed concurrently. ResultsEnhanced CSF influx was observed in macroscopic imaging, demonstrated by the influx area and intensity of the fluorescent dyes after FUS. In two-photon imaging, increased fluorescence was observed in the perivascular space (PVS) after stimulation. Moreover, particle tracking of microspheres showed more microspheres entering the imaging field, with increased mean speed after FUS. ConclusionOur findings provide direct real-time in vivo imaging evidence that FUS promotes CSF influx and flow in the PVS.

MicroLOCK: Highly stable microgel biosensor using locked nucleic acids as bioreceptors for sensitive and selective detection of let-7a

Chemically modified oligonucleotides can solve biosensing issues for the development of capture probes, antisense, CRISPR/Cas, and siRNA, by enhancing their duplex-forming ability, their stability against enzymatic degradation, and their specificity for targets with high sequence similarity as microRNA families. However, the use of modified oligonucleotides such as locked nucleic acids (LNA) for biosensors is still limited by hurdles in design and from performances on the material interface. Here we developed a fluorogenic biosensor for non-coding RNAs, represented by polymeric PEG microgels conjugated with molecular beacons (MB) modified with locked nucleic acids (MicroLOCK). By 3D modeling and computational analysis, we designed molecular beacons (MB) inserting spot-on LNAs for high specificity among targets with high sequence similarity (95%). MicroLOCK can reversibly detect microRNA targets in a tiny amount of biological sample (2μL) at 25°C with a higher sensitivity (LOD 1.3fM) without any reverse transcription or amplification. MicroLOCK can hybridize the target with fast kinetic (about 30min), high duplex stability without interferences from the polymer interface, showing high signal-to-noise ratio (up to S/N=7.3). MicroLOCK also demonstrated excellent resistance to highly nuclease-rich environments, in real samples. These findings represent a great breakthrough for using the LNA in developing low-cost biosensing approaches and can be applied not only for nucleic acids and protein detection but also for real-time imaging and quantitative assessment of gene targeting both in vitro and in vivo.

Unmanned aerial vehicle landing localization technology based on visual dual-channel uniqueness coding

Unmanned aerial vehicle (UAV) mainly uses GPS (Global Positioning System) for positioning, but it is difficult to locate in areas where GPS signal is missing or being disturbed. Therefore, the relative positioning technology based on visual method is also widely used in UAV landing, but due to the influence of cross wind and the motion of the landing plane, the traditional visual landing sign and positioning method is prone to lose the positioning target at the end stage of the landing. Therefore, this article proposes a landing sign based on double-layer unique coding, which can realize continuous positioning in the horizontal direction and accurate positioning of large-scale changes in the vertical direction. Firstly, real-time image acquisition and image processing are carried out through the airborne camera. Based on the flight altitude and encoding extraction effect of the UAV, the red or blue channel component is extracted, and then the image is preprocessed, edge detection, line detection, coding grid extraction, etc. As long as any 5 × 5 small squares of the whole pattern can be extracted, the perspective-n-point algorithm is used to obtain the relative pose estimation between the UAV and the landing sign. Experiments show that the landing sign and recognition algorithm effectively improve the UAV landing reliability.

The Role of Microsurgery and Fluorescent-reporter Genes in Establishing Mouse Models for Real-Time Imaging of Metastatic Cancer-Cell Trafficking and Colony Formation: A Revolutionary and Disruptive Technology for Metastasis Research.

The field of experimental microsurgery was pioneered by the great microsurgeon Sun Lee, who developed the foundation of transplant surgery in the clinic. Dr Lee also played a seminal role in introducing microsurgery to establish mouse models of cancer. In 1990, at the age of 70, Dr Lee demonstrated microsurgery techniques to the mouse-model team at AntiCancer Inc., leading to the development of the surgical orthotopic implant (SOI) technique and the first orthotopic mouse models of cancer that metastasized in a pattern similar to clinical cancer. At the beginning of the present century, one of us (NY) from Kanazawa University School of Medicine became a visiting scientist at AntiCancer to learn SOI and develop mouse models of cancer using cancer cells expressing fluorescent reporter genes, such as green fluorescent protein (GFP) and red fluorescent protein (RFP), in order to image metastatic cancer cells trafficking in real time. Since then, a total of eight young surgeons from Kanazawa University have been visiting researchers at AntiCancer, developing SOI mouse models of cancer to visualize cancer cells in vivo, tracking all stages of metastasis in real time. The present perspective review summarizes this seminal work, which has revolutionized the field of metastasis research.

Application of motion prediction based on a long short-term memory network for imaging dose reduction in real-time tumor-tracking radiation therapy

PurposeTo demonstrate the possibility of using a lower imaging rate while maintaining acceptable accuracy by applying motion prediction to minimize the imaging dose in real-time image-guided radiation therapy. MethodsTime-series of three-dimensional internal marker positions obtained from 98 patients in liver stereotactic body radiation therapy were used to train and test the long-short-term memory (LSTM) network. For real-time imaging, the root mean squared error (RMSE) of the prediction on three-dimensional marker position made by LSTM, the residual motion of the target under respiratory-gated irradiation, and irradiation efficiency were evaluated. In the evaluation of the residual motion, the system-specific latency was assumed to be 100 ms. ResultsExcept for outliers in the superior–inferior (SI) direction, the median/maximum values of the RMSE for imaging rates of 7.5, 5.0, and 2.5 frames per second (fps) were 0.8/1.3, 0.9/1.6, and 1.2/2.4 mm, respectively. The median/maximum residual motion in the SI direction at an imaging rate of 15.0 fps without prediction of the marker position, which is a typical clinical setting, was 2.3/3.6 mm. For rates of 7.5, 5.0, and 2.5 fps with prediction, the corresponding values were 2.0/2.6, 2.2/3.3, and 2.4/3.9 mm, respectively. There was no significant difference between the irradiation efficiency with and that without prediction of the marker position. The geometrical accuracy at lower frame rates with prediction applied was superior or comparable to that at 15 fps without prediction. In comparison with the current clinical setting for real-time image-guided radiation therapy, which uses an imaging rate of 15.0 fps without prediction, it may be possible to reduce the imaging dose by half or more. ConclusionsMotion prediction can effectively lower the frame rate and minimize the imaging dose in real-time image-guided radiation therapy.

ENPP1 induces blood-brain barrier dysfunction and promotes brain metastasis formation in HER2-positive breast cancer.

Brain metastasis (BrM) is a devastating end-stage neurological complication that occurs in up to 50% of HER2+ breast cancer patients. Understanding how disseminating tumor cells manage to cross the blood-brain barrier (BBB) is essential for developing effective preventive strategies. We identified the ecto-nucleotidase ENPP1 as specifically enriched in the secretome of HER2+ brain metastatic cells, prompting us to explore its impact on BBB dysfunction and BrM formation. We used in vitro BBB and in vivo premetastatic mouse models to evaluate the effect of tumor-secreted ENPP1 on brain vascular permeability. BBB integrity was analyzed by real-time fluorescence imaging of 20kDa Cy7.5-dextran extravasation and immunofluorescence staining of adherens and tight junction proteins. Pro-metastatic effects of ENPP1 were evaluated in an experimental brain metastatic model. Systemically secreted ENPP1 from primary breast tumors impaired the integrity of BBB with loss of tight and adherens junction proteins early before the onset of BrM. Mechanistically, ENPP1 induced endothelial cell dysfunction by impairing insulin signaling and its downstream AKT/GSK3β/β-catenin pathway. Genetic ablation of ENPP1 from HER2+ brain metastatic cells prevented endothelial cell dysfunction and reduced metastatic burden while prolonging the overall and metastasis-free survival of mice. Furthermore, plasmatic ENPP1 levels correlate with brain metastatic burden and inversely with overall survival. We demonstrated that metastatic breast cancer cells exploit the ENPP1 signaling for cell transmigration across the BBB and brain colonization. Our data implicate ENPP1 as a potential biomarker for poor prognosis and early detection of BrM in HER2+ breast cancer.

Spatiotemporal dynamics of microscopic biological barrier visualized by electric-double-layer modulation imaging

Live-cell label-free imaging of a microscopic biological barrier, generally referred to as ‘tight junction’, was realized by a recently developed electric-double-layer modulation imaging (EDLMI). The method allowed quantitative imaging of barrier integrity in real time, thus being an upper compatible of transepithelial electrical resistance (TEER) which is a conventional standard technique to evaluate spatially averaged barrier integrity. We demonstrate that the quantitative and real-time imaging capability of EDLMI unveils fundamental dynamics of biological barrier, some of which are totally different from conventional understandings.

Detection of real-time deep fakes and face forgery in video conferencing employing generative adversarial networks

As facial modification technology advances rapidly, it poses a challenge to methods used to detect fake faces. The advent of deep learning and AI-based technologies has led to the creation of counterfeit photographs that are more difficult to discern apart from real ones. Existing Deep fake detection systems excel at spotting fake content with low visual quality and are easily recognized by visual artifacts. The study employed a unique active forensic strategy Compact Ensemble-based discriminators architecture using Deep Conditional Generative Adversarial Networks (CED-DCGAN), for identifying real-time deep fakes in video conferencing. DCGAN focuses on video-deep fake detection on features since technologies for creating convincing fakes are improving rapidly. As a first step towards recognizing DCGAN-generated images, split real-time video images into frames containing essential elements and then use that bandwidth to train an ensemble-based discriminator as a classifier. Spectra anomalies are produced by up-sampling processes, standard procedures in GAN systems for making large amounts of fake data films. The Compact Ensemble discriminator (CED) concentrates on the most distinguishing feature between the natural and synthetic images, giving the generators a robust training signal. As empirical results on publicly available datasets show, the suggested algorithms outperform state-of-the-art methods and the proposed CED-DCGAN technique successfully detects high-fidelity deep fakes in video conferencing and generalizes well when comparing with other techniques. Python tool is used for implementing this proposed study and the accuracy obtained for proposed work is 98.23 %.

Damage-induced basal epithelial cell migration modulates the spatial organization of redox signaling and sensory neuron regeneration.

Epithelial damage leads to early reactive oxygen species (ROS) signaling, which regulates sensory neuron regeneration and tissue repair. How the initial type of tissue injury influences early damage signaling and regenerative growth of sensory axons remains unclear. Previously we reported that thermal injury triggers distinct early tissue responses in larval zebrafish. Here, we found that thermal but not mechanical injury impairs sensory axon regeneration and function. Real-time imaging revealed an immediate tissue response to thermal injury characterized by the rapid Arp2/3-dependent migration of keratinocytes, which was associated with tissue scale ROS production and sustained sensory axon damage. Isotonic treatment was sufficient to limit keratinocyte movement, spatially restrict ROS production, and rescue sensory neuron function. These results suggest that early keratinocyte dynamics regulate the spatial and temporal pattern of long-term signaling in the wound microenvironment during tissue repair.

Activatable near-infrared fluorescence probe for real-time imaging of PD-L1 expression in tumors.

In clinical practice, determining programmed death-ligand 1 (PD-L1) expression is crucial for selecting patients and monitoring immune checkpoint blockade therapies. Currently, PD-L1 expression is quantified using immunohistochemistry (IHC). However, IHC-based methods do not capture the heterogeneous and dynamic nature of PD-L1 expression. Thus, there is a pressing need for a rapid and efficient method for monitoring PD-L1 expression both in vitro and in vivo, which would considerably aid in prognosis and treatment selection. In this study, we present for the first time an activatable near-infrared (NIR) fluorescence imaging probe (Q-Atezol) for the real-time monitoring of PD-L1 expression in vitro and in vivo. The ability of Q-Atezol to detect PD-L1 expression quickly and in real-time was evaluated in both tumor spheroid and lung cancer xenograft models. An always-on optical probe (ON-Atezol) was synthesized and tested for comparison. In vivo NIR fluorescence imaging studies were conducted on A549 and H1975 tumor-bearing mice, and their tumor-to-background ratios (TBRs) were analyzed. The quenched NIR fluorescence of Q-Atezol is activated upon binding to PD-L1 proteins on the surface of cancer cells, thereby enabling PD-L1 detection in the three-dimensional (3D) tumor spheroids without a washing step. Notably, PD-L1-positive H1975 tumors were clearly visualized with a high TBR 6 hours after Q-Atezol injection, whereas ON-Atezol treatment could not detect H1975 tumors even 24 hours post-injection. The activatable fluorescence probe Q-Atezol demonstrated great potential as an exceptional sensor for assessing PD-L1 expression in 3D cell structures and for in vivo applications.

Ultrasound Imaging of Macrophages Intracellularly Labelled with Biosynthetic Gas Vesicles.

This study aimed to develop a novel method for real-time imaging to track macrophages and to make it possible to visually track their dynamic features. The archaeon Halobacterium NRC-1 was cultured in an ATCC medium. Buoyant cells were allowed to produce biosynthetic gas vesicles (GVs), and isolated GVs were collected after lysis. Gas vesicle-labelled macrophages (GV@RAWs) were obtained by incubating macrophage (RAW 264.7) cells with GVs. The ability of GV@RAWs to track macrophages in real-time for a long term was assessed using a high-frequency ultrasound imaging system. We successfully synthesised and isolated GV@RAWs by co-incubating them with RAW 264.7. The results showed that GV@RAW produced significant ultrasound signals without affecting cell survival and could achieve real-time imaging for up to 3 days in vitro. This research provides a new way to achieve long-term real-time imaging of macrophages, opening up new possibilities for immune response research, clinical diagnosis and therapeutic strategies for inflammatory diseases.