Search Results (557)

To address the challenges of severe optical attenuation and dynamic feature extraction for moving target depth retrieval in complex underwater remote sensing environments, this paper proposes a dynamic target depth estimation method based on multi-source data fusion. Taking optical RGB imagery and neuromorphic vision (NeuroIV) data as joint inputs, the proposed method constructs a three-channel feature extraction and fusion network. By leveraging a hypergraph structure, it establishes association weights between dynamic (temporal) and static (spatial) nodes to capture spatiotemporal correlations. To efficiently process the high-dimensional multi-modal data, the traditional dot-product attention is replaced with element-wise multiplication, significantly reducing computational complexity. Furthermore, a lightweight deformable attention pyramid (DAP) and diffusion model is introduced to refine depth image edges, effectively suppressing discontinuities and abruptness in the estimation results. Compared to single-modality optical imagery, the fused multi-modal data yields a superior signal-to-noise ratio and foreground contrast, achieving an improvement of over 20% in the MAE index. These results validate the effectiveness and superiority of the proposed multi-modal fusion strategy for dynamic target observation and depth retrieval in aquatic environments. Full article

Prostate-specific membrane antigen (PSMA) has become a central molecular target in prostate cancer because it enables both high-performance imaging and targeted radioligand therapy. PSMA positron emission tomography/computed tomography (PET/CT) is now used across several clinical settings, including primary staging of higher-risk localized disease, localization of biochemical recurrence, salvage radiotherapy planning, and assessment of oligometastatic disease. In metastatic castration-resistant prostate cancer, PSMA-targeted radioligand therapy (RLT), particularly lutetium-177-labeled PSMA-617, has established therapeutic value and is moving into earlier disease states. From an applied science perspective, the clinical performance of PSMA theranostics depends not only on target expression and trial efficacy, but also on radiopharmaceutical design, radionuclide selection, radiochemical quality, PET acquisition and reconstruction, standardized reporting, dosimetry, and quantitative response assessment. This narrative review summarizes the biological, radiochemical, and technical foundations of PSMA theranostics, the clinical evidence supporting PSMA PET/CT in key disease states, and the pivotal data for PSMA-targeted RLT. It also discusses imaging-based treatment eligibility, dosimetry, post-therapy imaging, Response Evaluation Criteria in PSMA Imaging, and next-generation beta- and alpha-emitting platforms. PSMA theranostics should be understood as an integrated clinical and technological platform that links molecular imaging, treatment selection, radionuclide delivery, and longitudinal response assessment across the prostate cancer care pathway. Full article

The integration requirements of laser ranging, imaging, and communication functions in space target detection have placed higher demands on system performance. This paper takes a modularly designed integrated laser ranging, imaging, and communication system as an example and proposes a light source integration scheme based on fiber phased array beam splitting–coupling technology, effectively enhancing the system’s integration level and compactness. The system employs a Cassegrain optical system and beam splitting structure to achieve functional integration of laser communication, ranging, and polarization imaging. Ground experiments were conducted to evaluate the functional feasibility of the proposed integrated architecture. The visible light polarization imaging experiments at kilometer-level distances demonstrate that polarization-derived information can improve target–background separability under haze and low-contrast conditions. The UAV-based dynamic ranging experiment verifies that the system can acquire, track, and range a moving cooperative target under the tested field conditions, with the measured results being consistent with the designed meter-level ranging requirement. In addition, a 1 km coherent free-space laser communication experiment achieved 20 Gbps QPSK signal transmission with a bit error rate on the order of 10⁻⁷. These results provide experimental support and design references for integrated optoelectronic terminals used in space target observation, space debris monitoring, and related long-distance sensing and communication applications. Full article

In the field of security screening imaging, millimeter-wave technology offers high imaging resolution and low radiation energy. However, it faces challenges such as difficulty in imaging non-cooperative moving targets, as well as bulky equipment and high costs. This paper proposes a high-resolution imaging method based on MIMO millimeter-wave radar. Firstly, the array model and slant range model are established, and a two-dimensional resolution scheme in range and height is constructed using a one-dimensional MIMO linear array and wideband signals. Then, the algorithm flow for MIMO millimeter-wave radar imaging is designed, and a range-domain super-resolution algorithm is introduced. This paper compensates for the phase coupling introduced by the transmitting array and target motion and successfully achieves two-dimensional imaging of non-cooperative targets based on the back-projection principle. Subsequently, the influence of array errors on the imaging results is analyzed. This method compensates for the phase coupling introduced by the transmit array and target motion and provides a theoretical analysis of array arrangement errors. Finally, the experimental results of the MIMO radar are analyzed. The final measured processing results show that the system can clearly reveal metal objects through cloth occlusion, and super-resolution processing yields sharper contours in the imaging of metal plates. Simulation analysis of imaging with array errors indicates that among the azimuth–elevation–range array position errors, the range array position error has a relatively significant impact. Full article

Deep learning-based automatic target recognition (ATR) for synthetic aperture radar (SAR) imagery requires large quantities of high-quality annotated data, yet real SAR samples are costly and difficult to obtain. Existing generative adversarial network (GAN)-based SAR generation methods often suffer from limited authenticity and insufficient diversity. To address these issues, we propose CA-DDPM, a conditionally embedded attention-aided denoising diffusion probabilistic model (DDPM) for high-quality multi-category SAR image generation. CA-DDPM employs a unified conditional embedding that fuses time-step and category information, injected into a U-Net backbone through a feature-wise linear modulation (FiLM)-based mechanism to achieve step-aware and class-aware denoising. Attention blocks are further incorporated to enhance the modeling of structural dependencies and fine scattering details. To evaluate generation quality, we develop a three-dimensional assessment framework that jointly examines authenticity, diversity, and utility in ATR. Authenticity is quantified using local and global similarity metrics under a unified Hungarian-matched statistical procedure, together with an SAR-adapted Fréchet inception distance (SAR-FID). Diversity is assessed through inter-category feature clustering, an SAR Inception Score (SAR-IS), and a newly proposed intra-category grayscale histogram-based metric. Utility is evaluated by hybrid training experiments across multiple ATR models. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset demonstrate that CA-DDPM produces more realistic and diverse SAR images than representative GAN- and DDPM-based baselines, and it effectively improves downstream ATR performance through data augmentation. Full article

To support low Earth orbit (LEO) debris monitoring, this paper investigates a tethered-balloon-based optical observation concept intended to complement ground- and space-based sensors. The system comprises a high-altitude tethered aerostat, an optical payload, a three-axis stabilization subsystem, and a ground control station. Key payload parameters, including field of view, spatial resolution, and atmospheric transmittance, are analyzed, and the configuration is examined in terms of spectral-band selection, aperture, and multi-camera mosaic imaging. A multi-station angular-measurement model and a weighted least-squares estimator are developed for debris localization. Monte Carlo and scenario-based simulations indicate that a wide field of view can increase observation duration and availability, with mean continuous observation arcs exceeding 400 s, thereby improving estimator conditioning and localization performance. A 5 km flight experiment further validates the operability of the SWIR imaging chain through star-field imaging and a representative image-sequence example with a highlighted moving point-source target. The results suggest that tethered balloons can provide a cost-effective and rapidly deployable supplementary observation layer for multi-layer space situational awareness. Full article

Exosomes are small vesicles released by cells that have attracted growing interest as drug delivery vehicles, particularly for brain diseases, where getting therapeutics across the BBB remains a fundamental problem. While conventional platforms such as liposomes, polymeric nanoparticles, and viral vectors often suffer from immune clearance and poor brain accumulation, engineered exosomes leverage natural cellular transport mechanisms to cross the BBB, protect cargo from degradation, and enable biocompatible interactions with target cells. This review takes a mechanistic and translational look at how exosomes are being engineered for CNS disorders, with a particular focus on glioblastoma. We cover exosome biogenesis through ESCRT-dependent and ESCRT-independent pathways, and how the competition between Rab27-driven secretion and Rab7-driven lysosomal degradation determines how many exosomes a cell releases, which has direct consequences for therapeutic production. We then discuss cargo loading strategies, from genetic approaches where donor cells are engineered to package specific molecules during biogenesis to physical methods like electroporation and sonication applied to isolated vesicles, alongside surface modification techniques for directing exosomes toward specific cell types. In glioblastoma, engineered exosomes have shown real promise for delivering chemotherapeutics across the BBB, targeting glioma stem cells, enabling CRISPR-based gene editing, and functioning as combined treatment and imaging tools. Applications in stroke and neurodegenerative diseases, where engineered exosomes carrying microRNAs and neuroprotective cargo have produced encouraging preclinical results, are also discussed. Scalable manufacturing and consistent targeting remain the hardest unsolved problems, and we outline emerging approaches including bioreactor-based production, programmable cargo loading, and patient-specific exosome design that are beginning to address these gaps. Overall, the progress reviewed here suggests that engineered exosomes are moving from an interesting biological concept toward a practically viable platform for CNS drug delivery. Full article

Recent improvements in hardware and software have enabled a paradigm shift in satellite computing, moving from purpose-built satellites running a single application to platforms capable of executing and even receiving new workloads on orbit. This evolution has allowed image processing to migrate from ground stations to single- or multi-node satellite clusters, with only processed results transmitted, significantly reducing end-to-end latency. This paper proposes ORCHIDE, an orchestration solution built on cloud-native technologies such as Kubernetes and Argo, purpose built for space edge computing. A key capability of ORCHIDE is its support for unikernels—minimal, single-application virtual machines—alongside containers. Compared to traditional containerized deployments, unikernels substantially reduce CPU and memory footprint, achieve short boot times, and produce smaller binary images. ORCHIDE further enables unikernel workloads to leverage heterogeneous accelerator hardware, including FPGAs, through a dedicated accelerator management library. We describe the system architecture, the scheduling model, and the minimum target hardware required for deployment. Three clusters of varying topology were used to evaluate ORCHIDE, demonstrating that it operates effectively on both single- and multi-node heterogeneous configurations. Preliminary results show the ORCHIDE platform being able to run in heterogeneous and single-node environments with as low as 4 cores and 8 GB of memory, offering potential users the flexibility to compose satellite hardware to best match their mission requirements. Full article

Dynamic three-dimensional (3-D) reconstruction of small objects moving at high speed is fundamentally limited by the number of viewpoints that a fixed camera array can provide at any single time instant. When the camera count is insufficient, single-frame multi-view stereo produces incomplete or inaccurate geometry. This paper proposes a multi-frame temporal integration approach that overcomes this limitation by exploiting the rigid-body assumption: because a falling object maintains its shape across consecutive frames, images captured at different time instants can be combined into a single, viewpoint-enriched reconstruction. A three-layer circular array of 32 synchronized RGB cameras captures 1440 × 1080 images at 160 fps, and a free-fall-oriented algorithm automatically detects active frames, selects informative temporal windows, and feeds the accumulated multi-frame images into a structure-from-motion and multi-view stereo (SfM-MVS) pipeline, effectively multiplying the number of viewpoints without additional hardware. The algorithm simultaneously recovers the 6-DOF pose trajectory of each object from the SfM-estimated camera parameters. Progressive accumulation experiments on freely falling soybeans (approximately 9–10 mm diameter) show that a single 32-camera frame already achieves an F-score exceeding 0.97 at a 0.5 mm threshold against an industrial structured-light scanner reference, and that accumulating additional temporal frames reaches a stable convergence plateau with both objects reaching a plateau F-score of 0.984. Beyond approximately one to two accumulated frames, additional frames yield diminishing returns, confirming that a small number of temporal frames is sufficient for convergent sub-millimeter accuracy. Across 30 independent free-fall trials with three objects, the system achieves an overall mean error of $0.146\pm 0.033$ mm and an overall F-score of $0.980\pm 0.006$—a mean relative error of approximately 1.6% on 8–10 mm targets—and fine surface features such as structural cracks are resolved at a fidelity sufficient for visual defect identification. These results establish rigid-body multi-frame temporal integration as an effective strategy for high-throughput, non-contact 3-D inspection of small objects in motion. Full article

The squint synthetic aperture radar (SAR) offers flexible beam pointing control and a wider range of applications compared to the side-looking SAR. Unlike the latter, ground moving targets exhibit shifts in both along-track and cross-track directions in squint SAR systems. To address this issue, a two-dimensional relocation method for moving targets is proposed in this paper. Firstly, the shift characteristics of moving targets in squint SAR systems are analyzed, revealing that the two-dimensional location shifts are correlated with both the target’s radial velocity and its imaging location. The proposed algorithm initially performs clutter suppression on the SAR imagery and estimates the radial velocity of the moving target. The two-dimensional location information is then derived by solving a set of joint equations. Finally, some numerical experiments are provided to demonstrate the effectiveness of the proposed method in the squint SAR system. Full article

Perception-oriented evaluation of automotive headlamps still relies mainly on human-vision photometric criteria, although forward-facing cameras are increasingly safety-critical sensing elements for night driving. This paper benchmarks 16 measured production headlamp light distributions with a simulation chain that combines headlamp spectra and beam patterns, diffuse scene reflection, an imaging-transfer model, and an EMVA-based camera model. The quantitative chain maps scene radiance to sensor-domain signal-to-noise ratio, derives task-specific required signal-to-noise curves from a six-network object-recognition ensemble, and aggregates local threshold satisfaction as region-of-interest coverage across three target reflectances and five driving speeds using WLTP moving-time weights. For the baseline RGB camera, WLTP-weighted coverage ranges from 18.95% to 53.48% across the evaluated light distributions, corresponding to a factor of 2.82 between the weakest and strongest distribution. The camera-parameter sweeps show that favorable beam placement can deliver comparable benchmark coverage with roughly 60% smaller pixel pitch than the weakest distribution, corresponding to an 84% reduction in pixel area, or at materially shorter exposure times. The WLTP-weighted coverage score correlates positively with the established Headlamp Safety Performance Rating, with Pearson $r=0.68$ for the RGB configuration, indicating partial alignment between human-centric and camera-centric illumination needs while confirming that the metrics are not interchangeable. The results identify headlamp light distribution as a primary design parameter for nighttime camera perception and provide a quantitative basis for co-design of automotive lighting and camera-based systems. Full article

Focused ultrasound (FUS)-mediated therapies have evolved with the advent of modern ultrasound-guided technology and MRI imaging, moving from their initial use as thermal ablation to a multifunctional platform for thermal and non-thermal ablation, immunomodulation, and targeted drug delivery. This narrative review explores the potential, limitations, and challenges of ablative high-intensity focused ultrasound (HIFU) therapies: HIFU thermal ablation and non-thermal ablation, histotripsy, as well as non-ablative low-intensity focused ultrasound (LIFU) applications in the management of hepatobiliary cancers. HIFU and histotripsy are reviewed as alternative or complementary treatment options in liver tumors, as well as their potential as bridging therapy. Histotripsy is addressed as a theranostic tool, not only by combining ablation with real-time ultrasound imaging guidance, but also by integrating it with sonobiopsy. It facilitates a liquid sonobiopsy of the ablated tumor by releasing intact tumor antigens and damage-associated molecular patterns, leading to potential molecular profiling. LIFU-induced targeted drug delivery (sono-chemotherapy), sonodynamic therapy, radiosensitization, immunomodulation of the immunosuppressive tumor microenvironment (sono-immunotherapy), and the potential to enhance the effect of immune checkpoint inhibitors in these malignancies are discussed. Since FUS-assisted procedures exhibit dual actions through therapeutic functionality associated with intra- and post-procedural ultrasound imaging guidance, they could have value as a theranostic tool in hepatobiliary interventional oncology. Although promising, the available clinical evidence for FUS-mediated therapies in hepatobiliary malignancies consists predominantly of early-stage feasibility studies, retrospective observational cohorts, and non-randomized comparative analyses. Further studies focused on standardized protocols, validation through large-scale, multicenter, prospective randomized clinical trials comparing FUS-based therapies with established treatments, and long-term follow-up of oncological efficacy could define their future role in multimodal oncological strategies. Full article

In video synthetic aperture radar (Video SAR), target motion causes defocusing, making it impossible to determine the target’s real-time position using reflected echoes. However, the shadows formed by the target occluding ground reflections can accurately characterize the target’s real-time position. To address challenges such as varying shadow scales, low contrast with the moving background, and susceptibility to clutter interference, this paper proposes a shadow detection network called DSENet to enhance the detail and semantic features of shadows. First, to enhance shadow features and reduce sampling loss during backbone network feature extraction, we design a detailed information enhancement (DIE) module to achieve lossless downsampling and effectively preserve the detailed features of the shadowed target. Second, we propose a semantic spatial feature aggregation (SSFA) module to enhance global semantic space feature extraction, improve the contextual feature representation of the target’s shadow region, and provide robust semantic space prior information for the model. Finally, we designed a detailed semantic fusion (DSF) module to improve the neck network’s ability to fuse shadow details and semantic features in video SAR images, further enhancing the model’s localization performance for target shadow features and achieving accurate localization of moving targets in video SAR. Comparative and ablation experiments validate the effectiveness and superiority of the proposed method. Experimental results on the Sandia National Laboratories (SNL) public dataset demonstrate that DSENet is efficient and performs excellently, achieving a P of 92.4% and an F1 score of 83.1%. Full article

Source-free domain adaptation (SFDA) for cross-modality prostate MRI segmentation is challenging because source data are unavailable and pseudo-labels on target ADC images are often noisy. To address this problem, we propose Curriculum Anatomy-guided Learning Method with Population Template Priors (CALM), a source-free adaptation framework for this task. CALM constructs a population template prior from target predictions using top-k consensus aggregation and cross-round exponential moving average, then combines this prior with instance-level predictions through Soft-AND fusion. A high-confidence background constraint is further introduced to provide reliable negative supervision, and a coverage-driven curriculum is used to expand training from easy to hard cases based on pseudo-label/template agreement. This design forms an iterative process in which prior refinement and sample-reliability refinement reinforce each other during adaptation. Experiments on the PI-CAI dataset under the T2W-to-ADC setting show that CALM achieves an average Dice score of 73.63% and outperforms representative SFDA baselines in both segmentation accuracy and boundary quality. Ablation and model analyses support the contribution of each component. These results suggest that population-level anatomical priors can provide practical structural guidance for source-free cross-modality adaptation. Full article

Breast cancer surgery has evolved from radical procedures to increasingly individualized and less invasive approaches. This narrative review contextualizes this evolution, synthesizes current evidence supporting surgical de-escalation, and examines emerging strategies that may further reduce the need for surgery. The manuscript is based on a structured appraisal of PubMed/MEDLINE literature and major international guidelines, prioritizing randomized trials, prospective studies, and consensus statements. Contemporary practice is characterized by progressive reduction in both breast and axillary surgery, enabled by advances in tumour biology, neoadjuvant systemic therapy, sentinel node strategies, and oncoplastic techniques. Emerging approaches—including selective omission of axillary surgery, targeted axillary dissection, and investigational strategies aiming at omission of breast surgery in exceptional responders—highlight a shift toward response-adapted and biology-driven care. While technological innovations such as robotic surgery and intraoperative radiotherapy may influence surgical practice, their role in true de-escalation remains limited or context-dependent. Overall, the field is moving toward minimizing surgical burden without compromising oncological safety, with future progress likely driven by improved patient selection, imaging, and integration of systemic therapy response. Full article