Search Results (102)

Electrochemical detection is widely used in environmental, health, and food analysis due to its portability, low cost, and high sensitivity. However, when analytes with similar redox potentials coexist, overlapping voltammetric signals often occur, which compromises detection accuracy and sensitivity. In this study, a simple second-derivative image sharpening (IS) algorithm is applied to the electrochemical detection of chlorophenol (CP) isomers with similar redox behaviors. Specifically, a graphene-modified electrode was employed for the electrochemical detection of two chlorophenol isomers: ortho-CP (o-CP) and meta-chlorophenol (m-CP) in the range from 1.0 to 10.0 μmol/L. After image-sharpening, the peak potential difference between o- and m-CP increased from 0.08 V to 0.12 V. The limits of detection (LOD) for o-CP and m-CP decreased from 0.6 to 0.9 μmol/L to 0.12 and 0.31 μmol/L, respectively. The corresponding sensitivities also improved from 0.92 to 1.35 A/(mol L⁻¹) to 4.11 and 3.71 A/(mol L⁻¹), respectively. Moreover, the sharpened voltammograms showed enhanced peak resolution, facilitating visual discrimination of the two isomers. These results demonstrate that image sharpening can significantly improve peak shape, peak separation, sensitivity, and detection limit in electrochemical analysis. The obtained algorithm is computationally efficient (<30 lines of C++ (Version 6.0)/OpenCV, executable in <1 ms on an ARM-M0 microcontroller) and easily adaptable to various programming environments, offering a promising approach for data processing in portable electrochemical sensing systems. Full article

Background: Midfacial trauma involving the zygomatic-maxillary-orbital (ZMO) complex poses significant reconstructive challenges due to anatomical complexity and the necessity for high-precision alignment. Traditional manual reduction techniques often result in inconsistent outcomes, necessitating revisions. Methods: This feasibility study presents two clinical cases treated using a novel, fully digital workflow incorporating computer-aided design and manufacturing (CAD/CAM) of patient-specific osteosynthesis plates and surgical drill guides. Following virtual fracture reduction and implant design, drill guides and implants were fabricated using selective laser melting. Surgical procedures included intraoral and transconjunctival approaches with intraoperative 3D imaging (mobile C-arm CT) to verify implant positioning. Postoperative results were compared to the virtual plan through image fusion. Results: Both cases demonstrated precise fit and anatomical restoration. The “one-position-fits-only” orbital implant design enabled highly accurate orbital wall reconstruction. Key procedural refinements between cases included enhanced interdisciplinary collaboration and improved guide designs, resulting in decreased planning-to-surgery intervals (<7 days) and seamless intraoperative application. Image fusion confirmed near-identical congruence between planned and achieved outcomes. Conclusions: The presented method demonstrates that fully digital, CAD/CAM-based midface reconstruction is feasible in the primary trauma setting. The technique offers reproducible precision, reduced intraoperative time, and improved functional and aesthetic outcomes. It may represent a paradigm shift in trauma care, particularly for complex ZMO fractures. Broader clinical adoption appears viable as production speed and workflow integration continue to improve. Full article

Background: The diagnostic work-up of small peripheral pulmonary nodules (PPNs) is becoming increasingly important, especially in light of the upcoming lung cancer screening programs and recommendations in practice. The systematic clinical introduction of the ION robotic-assisted navigational bronchoscopy (RNB) system represents a significant innovation in Germany, whereas clinical experience in the United States has already yielded promising results. The objective of this study is to present the outcomes of the first 50 patients examined with the ION system at our institutions. Materials and Methods: This is a retrospective, single-center analysis. We included the first 50 consecutive patients who underwent diagnostic evaluation of pulmonary nodules using the ION-RNB system, either in the Department of Thoracic Surgery or the Department of Pulmonology. Results: A total of 50 patients were evaluated, including 24 from the Department of Thoracic Surgery and 26 from the Department of Pulmonology. The pulmonary nodules were found in the peripheral third of the lung in 74% of cases, in the middle third in 18% of cases, and in the central third in 8% of cases. The mean lesion size was 1.64 cm (±0.91 cm). In all, 84% of the nodules were solid, 4% were subsolid, and 12% presented as ground-glass opacities (GGOs). Cone beam computed tomography (CBCT) was used to confirm tool-in-lesion position in 68% of cases compared to C-arm fluoroscopy in 32%. Additionally, radial endobronchial ultrasound (rEBUS) was applied in 30% of procedures. The overall diagnostic yield, independent of imaging modality or histological processing method, was 78%. When CBCT and formalin-fixed paraffin-embedded (FFPE) histological analysis were utilized, the diagnostic yield exceeded 90%. Conclusions: Initial clinical experience with the ION-RNB system in Germany shows encouraging results. The high diagnostic accuracy underlines the system’s potential for evaluating peripheral pulmonary lesions precisely. The use of advanced imaging techniques, particularly CBCT, and the choice of histopathological processing methods are critical variables in optimizing patient-centered diagnostic pathways. Further prospective studies are warranted to assess the long-term clinical utility of robotic-assisted bronchoscopy in diverse clinical settings. Full article

When a CMOS (Complementary Metal–Oxide–Semiconductor) imaging system operates at a high frame rate or a high line rate, the exposure time of the imaging system is limited, and the acquired image data will be dark, with a low signal-to-noise ratio and unsatisfactory sharpness. Therefore, in order to improve the visibility and signal-to-noise ratio of remote sensing images based on CMOS imaging systems, this paper proposes a low-light remote sensing image enhancement method and a corresponding ZYNQ (Zynq-7000 All Programmable SoC) design scheme called the BGIR (Bilateral-Guided Image Restoration) algorithm, which uses an improved multi-scale Retinex algorithm in the HSV (hue–saturation–value) color space. First, the RGB image is used to separate the original image’s H, S, and V components. Then, the V component is processed using the improved algorithm based on bilateral filtering. The image is then adjusted using the gamma correction algorithm to make preliminary adjustments to the brightness and contrast of the whole image, and the S component is processed using segmented linear enhancement to obtain the base layer. The algorithm is also deployed to ZYNQ using ARM + FPGA software synergy, reasonably allocating each algorithm module and accelerating the algorithm by using a lookup table and constructing a pipeline. The experimental results show that the proposed method improves processing speed by nearly 30 times while maintaining the recovery effect, which has the advantages of fast processing speed, miniaturization, embeddability, and portability. Following the end-to-end deployment, the processing speeds for resolutions of 640 × 480 and 1280 × 720 are shown to reach 80 fps and 30 fps, respectively, thereby satisfying the performance requirements of the imaging system. Full article

The HepACAGA (Hepatic Arteriography and C-arm CT-Guided Ablation) technique, which integrates C-arm CT guidance with transcatheter C-arm CT hepatic arteriography (C-arm CTHA), significantly improves liver tumor ablation outcomes by enhancing tumor visualization, navigation, and the intraprocedural assessment of ablation margins. The two key advantages of using C-arm CT over conventional CT for image guidance are firstly that the entire procedure can be performed in the angiography suite, eliminating the need for patient transfer between the angiography suite (catheterization) and CT-room (ablation), and secondly, that integrated C-arm needle guidance software can greatly reduce the difficulty of needle placement. Beyond these advantages, the HepACAGA technique offers additional benefits across four domains: (1) the direct conversion of ablation to intra-arterial liver-directed therapies (e.g., radioembolization or chemoembolization) upon the intraprocedural detection of disease progression; (2) the direct combination of ablation with intra-arterial treatments or portal vein embolization in one session; (3) the enhanced ablation effect through heat sink effect reduction with adjunct bland embolization or balloon occlusion; and (4) the immediate hemorrhage control through direct embolization. This pictorial essay demonstrates the advantages of combining C-arm CT guidance with real-time C-arm CTHA in the percutaneous thermal ablation of liver tumors, with clinical cases illustrating each of the aforementioned four key domains. Full article

Phase stability at low radio frequencies is severely impacted by ionospheric propagation delays. Radio interferometers such as the giant metrewave radio telescope (GMRT) are capable of detecting changes in the ionosphere’s total electron content (TEC) over larger spatial scales and with greater sensitivity compared to conventional tools like the global navigation satellite system (GNSS). Thanks to its unique design, featuring both a dense central array and long outer arms, and its strategic location, the GMRT is particularly well-suited for studying the sensitive ionospheric region located between the northern peak of the equatorial ionization anomaly (EIA) and the magnetic equator. In this study, we observe the bright flux calibrator 3C48 for ten hours to characterize and study the low-latitude ionosphere with the upgraded GMRT (uGMRT). We outline the methods used for wideband data reduction and processing to accurately measure differential TEC ($\delta \mathrm{TEC}$) between antenna pairs, achieving a precision of< mTECU (1 mTECU = ${10}^{-3}$ TECU) for central square antennas and approximately mTECU for arm antennas. The measured $\delta \mathrm{TEC}$ values are used to estimate the TEC gradient across GMRT arm antennas. We measure the ionospheric phase structure function and find a power-law slope of $\beta =1.72\pm 0.07$, indicating deviations from pure Kolmogorov turbulence. The inferred diffractive scale, the spatial separation over which the phase variance reaches $1{\mathrm{rad}}^2$, is ∼6.66 km. The small diffractive scale implies high phase variability across the field of view and reduced temporal coherence, which poses challenges for calibration and imaging. Full article

Objective: The aim of this study is to present the initiation of robotic-guided (RG) spine surgery into routine clinical care at a single center with the use of intraoperative CT (iCT) automatic registration-based navigation. The workflow included iCT with automatic registration, fusion with preoperative imaging, verification of preplanned screw trajectories, RG introduction of K-wires, and the insertion of pedicle screws (PSs), followed by a control iCT scan. Methods: All patients who underwent RG implantation of pedicle screws using the Cirq^® robotic arm (BrainLab, Munich, Germany) in the thoracolumbar spine at our department were included in the study. The accuracy of the pedicles screws was assessed using the Gertzbein–Robbins scale (GRS). Results: In total, 108 patients (60 female, mean age 68.7 ± 11.4 years) in 109 surgeries underwent RG PS placement. Indications included degenerative spinal disorders (n = 30 patients), spondylodiscitis (n = 24), tumor (n = 33), and fracture (n = 22), with a mean follow-up period of 7.7 ± 9 months. Thirty-seven cases (33.9%) were performed percutaneously, and all others were performed openly. Thirty-three operations were performed on the thoracic spine, forty-four on the lumbar and lumbosacral spine, thirty on the thoracolumbar, one on the cervicothoracic spine, and one on the thoracolumbosacral spine. The screws were inserted using a fluoroscopic (first 12 operations) or navigated technique (latter operations). The mean operation time was 228.8 ± 106 min, and the mean robotic time was 31.5 ± 18.4 min. The mean time per K-wire was 5.35 ± 3.98 min. The operation time was lower in the percutaneous group, while the robot time did not differ between the two groups. Robot time and the time per K-wire improved over time. Out of 688 screws, 592 were GRS A screws (86.1%), 54 B (7.8%), 22 C (3.2%), 12 D (1.7%), and 8 E (1.2%). Seven screws were revised intraoperatively, and after revision, all were GRS A. E screws were either revised or removed. In the case of D screws, screws located at the end of the construct were revised, while so-called in-out-in screws in the middle of the construct were not revised. Conclusions: Brainlab’s Cirq^® Robotic Alignment Module feature enables placement of pedicle screws in the thoracolumbar spine with high accuracy. A learning curve is shown through improvements in robotic time and time per K-wire. Full article

This study presents a bilateral control architecture that links fluoroscopic image feedback directly to the kinematics of a tendon-driven, three-joint robotic catheter and a 3-DoF motorised C-arm, intending to preserve optimal imaging geometry during autonomous catheter insertion and thereby mitigating radiation exposure. Forward and inverse kinematics for both manipulators were derived via screw theory and geometric analysis, while a calibrated projection model generated synthetic X-ray images whose catheter bending angles were extracted through intensity thresholding, segmentation, skeletonisation, and least-squares circle fitting. The estimated angle fed a one-dimensional extremum-seeking routine that rotated the C-arm about its third axis until the apparent bending angle peaked, signalling an orthogonal view of the catheter’s bending plane. Implemented in a physics-based simulator, the framework achieved inverse-kinematic errors below 0.20% for target angles between ${20}^{°}$ and ${90}^{°}$, with accuracy decreasing to 3.00% at ${10}^{°}$. The image-based angle estimator maintained a root-mean-square error $\le 3$% across most of the same range, rising to 6.4% at ${10}^{°}$. The C-arm search consistently located the optimal perspective, and the combined controller steered the catheter tip along a predefined aortic path without collision. These results demonstrate sub-degree angular accuracy under idealised, noise-free conditions and validate real-time coupling of image guidance to dual-manipulator motion; forthcoming work will introduce realistic image noise, refined catheter mechanics, and hardware-in-the-loop testing to confirm radiation-dose and workflow benefits. Full article

We propose a method to reconstruct a 3D coordinate system and a vascular map for the operation of magnetic medical robots (MMRs) controlled by a magnetic navigation system (MNS) using two 2D X-ray images and four corners of an MNS. Utilizing the proposed method, we calculated the relative rotation angle of a C-arm considering its rotational precision error. We derived the position information and 3D coordinate system of an MNS workspace in which the magnetic fields are generated and controlled by an MNS. The proposed method can also be utilized to reconstruct vascular maps. Reconstructed vascular maps are in the 3D coordinate system of the C-arm and can be transformed into the 3D coordinate system of an MNS workspace to generate the magnetic flux density with the desired direction and magnitude at the position of the MMR. The proposed method allows us to remotely and precisely control the MMR inserted into the vessel by controlling the external magnetic field. The proposed method was validated through in vitro experiments with an MNS mock-up and a vascular jig. Finally, the proposed method was applied to in vivo experiments where the MMR was inserted into the superficial femoral artery of a mini pig to remotely control the motion of the MMR. This research will enable precise and effective control of MMRs in various medical procedures utilizing an MNS. Full article

Background: Pelvic fractures require precise reduction and stabilization, necessitating high-quality C-arm imaging and accurate patient positioning. Standard operating tables often obstruct optimal C-arm maneuverability. To address this, we developed a new auxiliary surgical table that integrates with existing tables and evaluated its clinical utility compared to a specialized carbon surgical table. Methods: Between March 2018 and June 2023, we conducted a retrospective study involving 162 patients (97 men and 65 women; average age 45.7 years) who underwent percutaneous sacroiliac screw fixation for pelvic fractures. Ninety patients were treated using the newly developed table, and seventy-two patients were treated using the carbon table. The new table, measuring 200 cm in length, 50 cm in width, and 2 cm in thickness, was constructed from waterproof plywood and designed to be securely attached to existing operating tables. We compared surgical preparation times, economic costs, and intraoperative imaging feasibility between the two groups. Results: Use of the new table significantly reduced the surgical preparation time by an average of 21 min and saved approximately $43,000 in cost compared to the carbon table. Subjective assessments indicated no notable difference in intraoperative C-arm image quality between the two groups. The new table allowed free C-arm rotation by overcoming the mechanical limitations of conventional tables. Conclusions: The new auxiliary table demonstrated clinical feasibility and economic advantages without compromising intraoperative imaging quality, offering a practical and cost-effective alternative for pelvic fracture surgeries. Full article

We developed a license plate recognition platform based on the Zynq-7000 SoC. A field-programmable gate array (FPGA) was used to build a low-power, high-speed neural network. The system leveraged the ARM processor for initial image processing and used standard license plate characters as a training dataset. After filtering and processing, the images were resized to 28 × 28 pixels in the grayscale format and then transmitted to the FPGA for high-speed recognition. The digital circuit in the FPGA was implemented using Verilog in a deep learning neural network architecture, with the neurons configured as (57, 12, 57, 36) in a hidden layer. The model was trained for 60 epochs. The neural network was also trained with a dataset consisting of 26 English alphabet characters and 10 digits, augmented using image dilation and erosion. Recognition accuracy was 83.33%. Using Vivado, the system was successfully deployed on the Zynq-7000 SoC, demonstrating its potential in intelligent applications. Full article

Breast cancer detection is a critical task in healthcare, requiring fast, accurate, and efficient diagnostic tools. However, the high computational demands and latency of deep learning models in medical imaging present significant challenges, especially in resource-constrained environments. This paper addresses these challenges by presenting an FPGA hardware accelerator tailored for breast cancer classification, leveraging the Zynq XC7Z020 SoC. The system integrates FPGA-accelerated layers with an ARM Cortex-A9 processor to optimize both performance and resource efficiency. We developed modular IP cores, including Conv2D, Average Pooling, and ReLU, using Vivado HLS to maximize FPGA resource utilization. By adopting 8-bit fixed-point arithmetic, the design achieves a 15.8% reduction in execution time compared to traditional CPU-based implementations while maintaining high classification accuracy. Additionally, our optimized approach significantly enhances energy efficiency, reducing power consumption from 3.8 W to 1.4 W a 63.15% reduction. This improvement makes our design highly suitable for real-time, power-sensitive applications, particularly in embedded and edge computing environments. Furthermore, it underscores the scalability and efficiency of FPGA-based AI solutions for healthcare diagnostics, enabling faster and more energy-efficient deep learning inference on resource-constrained devices. Full article

Background/Objectives: Fluoroscopy has been widely adopted in interventional pulmonology, as it facilitates real-time visualization of the bronchoscope, endobronchial ultrasound, and biopsy tools during procedures. The purpose of this study was to evaluate the effectiveness of radiation shields in minimizing scattered X-ray dose to the bronchoscopist in a phantom study and to determine the dose of scattered X-ray dose to medical staff with radiation shields in clinical application. Methods: An anthropomorphic torso phantom was positioned on the fluoroscopic table between the C-arm X-ray tube and the image detector to mimic bronchoscopic operations. Upper and lower body lead shields were used to examine the effectiveness of radiation shielding. Scatter radiation rates were assessed at a first operator location using real-time dosimeters with and without protective devices. In clinical application, the scattered X-ray dose of the first operator and main assistant was measured using wearable radiation dosimeters during 20 procedures. Results: In the phantom study, scattered radiation without shielding was 266.34 ± 8.86 μSv/h (glabella), 483.90 ± 8.01 μSv/h (upper thorax), 143.97 ± 8.20 μSv/h (hypogastrium), and 7.22 ± 0.28 μSv/h (ankle). The combination of upper and lower body lead shields reduced the scattered X-ray dose by 98.7%, 98.3%, 66.2%, and 79.9% at these levels, respectively. In clinical application, mean scattered X-ray dose rates were 0.14 ± 0.05 μSv/procedure (eye), 0.46 ± 0.51 μSv/procedure (chest), 0.67 ± 0.50 μSv/procedure (hypogastrium), and 1.57 ± 2.84 μSv/procedure (assistant’s wrist). Conclusions: The combination of radiation shields significantly reduced the scattered X-ray dose at the operator site in the phantom study. The scattered X-ray dose to medical staff during bronchoscopy can be kept at a low level with the aid of a shielding system. Full article

In recent years, underwater image enhancement (UIE) processing technology has developed rapidly, and underwater optical imaging technology has shown great advantages in the intelligent operation of underwater robots. In underwater environments, light absorption and scattering often cause seabed images to be blurry and distorted in color. Therefore, acquiring high-definition underwater imagery with superior quality holds essential significance for advancing the exploration and development of marine resources. In order to resolve the problems associated with chromatic aberration, insufficient exposure, and blurring in underwater images, a high-dimensional attention generative adversarial network framework for underwater image enhancement (HDAGAN) is proposed. The introduced method is composed of a generator and a discriminator. The generator comprises an encoder and a decoder. In the encoder, a channel attention residual module (CARM) is designed to capture both semantic features and contextual details from visual data, incorporating multi-scale feature extraction layers and multi-scale feature fusion layers. Furthermore, in the decoder, to refine the feature representation of latent vectors for detail recovery, a strengthen–operate–subtract module (SOSM) is introduced to strengthen the model’s capability to comprehend the picture’s geometric structure and semantic information. Additionally, in the discriminator, a multi-scale feature discrimination module (MFDM) is proposed, which aids in achieving more precise discrimination. Experimental findings demonstrate that the novel approach significantly outperforms state-of-the-art UIE techniques, delivering enhanced outcomes with higher visual appeal. Full article

Background: The main challenge in human–object interaction detection (HOI) is how to accurately reason about ambiguous, complex, and difficult to recognize interactions. The model structure of the existing methods is relatively single, and the image input may be occluded and cannot be accurately recognized. Methods: In this paper, we design a Pose-Aware Interaction Network (PAIN) based on transformer architecture and human posture to address these issues through two innovations: A new feature fusion method is proposed, which fuses human pose features and image features early before the encoder to improve the feature expression ability, and the individual motion-related features are additionally strengthened by adding to the human branch; the Cross-Attention Relationship fusion Module (CARM) better fuses the three-branch output and captures the detailed relationship information of HOI. Results: The proposed method achieves 64.51%$A{P}_{role}^{\#1}$, 66.42%$A{P}_{role}^{\#2}$ on the public dataset V-COCO and 30.83% AP on HICO-DET, which can recognize HOI instances more accurately. Full article