Accurate Image Research Articles

Spatial Resolution Enhancement Framework Using Convolutional Attention-Based Token Mixer

Spatial resolution enhancement in remote sensing data aims to augment the level of detail and accuracy in images captured by satellite sensors. We proposed a novel spatial resolution enhancement framework using the convolutional attention-based token mixer method. This approach leveraged spatial context and semantic information to improve the spatial resolution of images. This method used the multi-head convolutional attention block and sub-pixel convolution to extract spatial and spectral information and fused them using the same technique. The multi-head convolutional attention block can effectively utilize the local information of spatial and spectral dimensions. The method was tested on two kinds of data types, which were the visual-thermal dataset and the visual-hyperspectral dataset. Our method was also compared with the state-of-the-art methods, including traditional methods and deep learning methods. The experiment results showed that the method was effective and outperformed state-of-the-art methods in overall, spatial, and spectral accuracies.

Performance of ChatGPT-4o in the Adult Clinical Cardiology Self-Assessment Program

Abstract Objective This study evaluates the performance of OpenAI's latest large language model, ChatGPT-4o, on the Adult Clinical Cardiology Self-Assessment Program (ACCSAP). Methods ChatGPT-4o was tested on 639 ACCSAP questions, excluding 45 questions containing video clips, resulting in 594 questions for analysis. The questions included a mix of text-based and static image-based (ECG, angiogram, CT scan, echocardiogram) formats. The model was allowed one attempt per question. Further evaluation of image-only questions was performed on 25 questions from the database. Results ChatGPT-4o correctly answered 69.2% (411/594) of the questions. The performance was higher for text-only questions (73.9%) compared to those requiring image interpretation (55.3%, p < 0.001). The model performed worse on questions involving ECGs, with a correct rate of 56.5% compared to 73.3% for non-ECG questions (p < 0.001). Despite its capability to interpret medical images in the context of a text-based question, the model's accuracy varied, demonstrating strengths and notable gaps in diagnostic accuracy.It lacked accuracy in reading images (ECGs, echocardiography, angiograms) with no context. Conclusions ChatGPT-4o performed moderately well on ACCSAP questions. However, the model's performance remains inconsistent, especially in interpreting ECGs. These findings highlight the potential and current limitations of using large language models in medical education and clinical decision-making.

Enhancing amide proton transfer imaging in ischemic stroke using a machine learning approach with partially synthetic data.

Amide proton transfer (APT) imaging, a technique sensitive to tissue pH, holds promise in the diagnosis of ischemic stroke. Achieving accurate and rapid APT imaging is crucial for this application. However, conventional APT quantification methods either lack accuracy or are time-consuming. Machine learning (ML) has recently been recognized as a potential solution to improve APT quantification. In this paper, we applied an ML model trained on a new type of partially synthetic data, along with an optimization approach utilizing recursive feature elimination, to predict APT imaging in an animal stroke model. This partially synthetic datum is not a simple blend of measured and simulated chemical exchange saturation transfer (CEST) signals. Rather, it integrates the underlying components including all CEST, direct water saturation, and magnetization transfer effects partly derived from measurements and simulations to reconstruct the CEST signals using an inverse summation relationship. Training with partially synthetic data requires less in vivo data compared to training entirely with fully synthetic or in vivo data, making it a more practical approach. Since this type of data closely resembles real tissue, it leads to more accurate predictions than ML models trained on fully synthetic data. Results indicate that an ML model trained on this partially synthetic data can successfully predict the APT effect with enhanced accuracy, providing significant contrast between stroke lesions and normal tissues, thus clearly delineating lesions. In contrast, conventional quantification methods such as the asymmetric analysis method, three-point method, and multiple-pool model Lorentzian fit showed inadequate accuracy in quantifying the APT effect. Moreover, ML methods trained using in vivo data and fully synthetic data exhibited poor predictive performance due to insufficient training data and inaccurate simulation pool settings or parameter ranges, respectively. Following optimization, only 13 frequency offsets were selected from the initial 69, resulting in significantly reduced scan time.

Motion and anatomy dual aware lung ventilation imaging by integrating Jacobian map and average CT image using dual path fusion network.

Deep learning-based computed tomography (CT) ventilation imaging (CTVI) is a promising technique for guiding functional lung avoidance radiotherapy (FLART). However, conventional approaches, which rely on anatomical CT data, may overlook important ventilation features due to the lack of motion data integration. This study aims to develop a novel dual-aware CTVI method that integrates both anatomical information from CT images and motional information from Jacobian maps to generate more accurate ventilation images for FLART. A dataset of 66 patients with four-dimensional CT (4DCT) images and reference ventilation images (RefVI) was utilized to develop the dual-path fusion network (DPFN) for synthesizing ventilation images (CTVIDual). The DPFN model was specifically designed to integrate motion data from 4DCT-generated Jacobian maps with anatomical data from average 4DCT images. The DPFN utilized two specialized feature extraction pathways, along with encoders and decoders, designed to handle both 3D average CT images and Jacobian map data. This dual-processing approach enabled the comprehensive extraction of lung ventilation-related features. The performance of DPFN was assessed by comparing CTVIDual to RefVI using various metrics, including Spearman's correlation coefficients (R), Dice similarity coefficients of high-functional region (DSCh), and low-functional region (DSCl). Additionally, CTVIDual was benchmarked against other CTVI methods, including a dual-phase CT-based deep learning method (CTVIDLCT), a radiomics-based method (CTVIFM), a super voxel-based method (CTVISVD), a Unet-based method (CTVIUnet), and two deformable registration-based methods (CTVIJac and CTVIHU). In the test group, the mean R between CTVIDual and RefVI was 0.70, significantly outperforming CTVIDLCT (0.68), CTVIFM (0.58), CTVISVD (0.62), and CTVIUnet (0.66), with p<0.05. Furthermore, the DSCh and DSCl values of CTVIDual were 0.64 and 0.80, respectively, outperforming CTVISVD (0.63; 0.73) and CTVIUnet (0.62; 0.77). The performance of CTVIDual was also significantly better than that of CTVIJac and CTVIHU. A novel dual-aware CTVI model that integrates anatomical and motion information was developed to synthesize lung ventilation images. It was shown that the accuracy of lung ventilation estimation could be significantly enhanced by incorporating motional information, particularly in patients with tumor-induced blockages. This approach has the potential to improve the accuracy of CTVI, enabling more effective FLART.

High resolution computed tomography in systemic sclerosis: From diagnosis to follow-up.

Early diagnosis of interstitial lung disease (ILD) and pulmonary hypertension (PH) is crucial in systemic sclerosis (SSc) for both management and treatment. However, diagnosing SSc-ILD can be challenging because symptoms of lung involvement are often non-specific at the early stages of disease. High-resolution computed tomography (HRCT) of the chest is recognized as the most accurate imaging modality for baseline and follow-up evaluation of SSc-ILD. Key features of SSc-ILD on HRCT include a non-specific interstitial pneumonia (NSIP) pattern, with peripheral ground-glass opacities and extensive traction bronchiectasis. Less common HRCT manifestations include usual interstitial pneumonia (UIP) pattern, followed by diffuse alveolar damage (DAD), diffuse alveolar hemorrhage (DAH) and organizing pneumonia (OP). The extent of disease on HRCT is known to relate with prognosis and serial assessments can be helpful in monitoring disease progression or treatment response. We discuss the main chest computed tomography (CT) manifestations of SSc, highlighting the role of imaging at both baseline and follow-up evaluations.

Dual-lock-and-key virus-mimicking nanoprobes for ultra-high accurate and sensitive imaging of viral infections in vivo

Fluorescence in situ imaging of viral infection lesions in vivo is crucial for precise diagnosis of viral diseases and evaluation of the extent of viral infection. Nevertheless, achieving highly specific and sensitive fluorescence imaging of viral infection sites in vivo has posed a persistent challenge. Here, we developed a dual-lock-and-key virus-mimicking nanoprobe that consisted of polyamide dendrimers (PAMAM) loaded internally with molecular beacons double-triggered by apurinic/apyrimidinic nucleic acid endonuclease 1 (APE1) and viral RNA (vRNA), and surface-modified with the E protein of Japanese encephalitis virus (JEV). This activatable nanoprobe generated dramatically amplified fluorescent signals stimulated by expressed vRNA and APE1 during viral infection, enabling ultrahigh specific and sensitive imaging of the lesions of JEV infection in vivo. This study provides a potential approach for accurate and sensitive detection of viral infection levels and assessment of the efficacy of antiviral drugs in vivo.

Multivalent supramolecular fluorescent probes for accurate disease imaging.

Optical imaging is a powerful tool for early disease detection and effective treatment planning, but its accuracy is often compromised by the uptake of imaging materials by the mononuclear phagocyte system (MPS). Herein, we leverage multivalent host-guest interactions between cyanine dyes and β-cyclodextrin polymers to develop supramolecular probes with enhanced stability, optical, and transport profiles for accurate in vivo imaging. These multivalent interactions not only ensure the stability of the probes but also enhance fluorescence efficiency by minimizing nonradiative decay. Our self-assembly approach effectively modulates probe size and surface properties, enabling evasion of MPS clearance and promoting prolonged bloodstream circulation, thereby improving the signal-to-background ratio for imaging. The effectiveness of our design is demonstrated by substantial advancements in the early diagnosis of acute kidney injury and by providing high-contrast imaging and precise surgical navigation across various tumor models. Our strategy not only advances optical imaging materials toward clinical translation but also establishes a versatile platform applicable to multiple imaging modalities.

Fine-art recognition using convolutional transformers

Digital image processing is a constantly evolving field encompassing a wide range of techniques and applications. Researchers worldwide are continually developing various algorithms across multiple fields to achieve accurate image classification. Advanced computer vision algorithms are crucial for architectural and artistic analysis. The digitalization of art has significantly enhanced the accessibility and conservation of fine-art paintings, yet the risk of art theft remains a significant challenge. Improving art security necessitates the precise identification of fine-art paintings. Although current recognition systems have shown potential, there is significant scope for enhancing their efficiency. We developed an improved recognition system for categorizing fine-art paintings using convolutional transformers, specified by an attention mechanism to enhance focused learning on the data. As part of the most advanced architectures in the deep learning family, transformers are empowered by a multi-head attention mechanism, thus improving learning efficiency. To assess the performance of our model, we compared it with those developed using four pre-trained networks: ResNet50, VGG16, AlexNet, and ViT. Each pre-trained network was integrated into a corresponding state-of-the-art model as the first processing blocks. These four state-of-the-art models were constructed under the transfer learning strategy, one of the most commonly used approaches in this field. The experimental results showed that our proposed system outperformed the other models. Our study also highlighted the effectiveness of using convolutional transformers for learning image features.

Micro-XCT analysis of anatomical features and dimensions of the incisive canal: implications for dental implant treatment in the anterior maxilla

BackgroundThis study used micro-focus X-ray Computed Tomography (micro-XCT) to examine the anatomical differences and dimensions of the maxillary incisive canal (MIC) in a South African population. The accurate imaging yielded dependable results that support earlier research and enhance anterior maxilla surgery planning. Furthermore, these anatomical features are compared between various racial and gender groupings in the study.MethodsUsing a micro-XCT scanner, 108 human cadaver skulls from the Pretoria Bone Collection were scanned and included in the study. Advanced volume rendering software was employed for measuring the MIC length, diameter, shape, and the buccal bone wall measurements in relation to the MIC.ResultsSignificant anatomical variation in the size and shape of the MIC was identified in the population, with variations seen between racial and gender groups. The incisive foramen (ICO) mean diameter was 6.61 mm, and the MIC length varied from 4.96 to 20.10 mm. There were significant differences in the buccal alveolar bone height between different ethnic groups and gender. Regarding morphological patterns in coronal and sagittal views, single canals were more common in the black population while Y-shaped canals were more common in the white population. The study also introduced a new metric by measuring the mean distances between teeth #11 and #21 and the ICO (1.83 mm and 1.88 mm respectively).ConclusionsThe complex anatomical differences of the MIC in a South African population were clarified. Clinicians should be aware of tooth sockets in near proximity to the MIC and perform accurate preoperative assessment using sophisticated 3-D imaging and preferable guided implant placement in the anterior maxilla.

Performance evaluation of image co-registration methods in photoacoustic mesoscopy of the vasculature

Objective:The formation of functional vasculature in solid tumours enables delivery of oxygen and nutrients, and is vital for effective treatment with chemotherapeutic agents. Longitudinal characterisation of vascular networks can be enabled using mesoscopic photoacoustic imaging, but requires accurate image co-registration to precisely assess local changes across disease development or in response to therapy. Co-registration in photoacoustic imaging is challenging due to the complex nature of the generated signal, including the sparsity of data, artefacts related to the illumination/detection geometry, scan-to-scan technical variability, and biological variability, such as transient changes in perfusion. To better inform the choice of co-registration algorithms, we compared five open-source methods, in physiological and pathological tissues, with the aim of aligning evolving vascular networks in tumours imaged over growth at different time-points.Approach:Co-registration techniques were applied to 3D vascular images acquired with photoacoustic mesoscopy from murine ears and breast cancer patient-derived xenografts, at a fixed time-point and longitudinally. Images were pre-processed and segmented using an unsupervised generative adversarial network. To compare co-registration quality in different settings, pairs of fixed and moving intensity images and/or segmentations were fed into five methods split into the following categories: affine intensity-based using 1)mutual information (MI) or 2)normalised cross-correlation (NCC) as optimisation metrics, affine shape-based using 3)NCC applied to distance-transformed segmentations or 4)iterative closest point algorithm, and deformable weakly supervised deep learning-based using 5)LocalNet co-registration. Percent-changes in Dice coefficients, surface distances, MI, structural similarity index measure and target registration errors were evaluated.Main results:Co-registration using MI or NCC provided similar alignment performance, better than shape-based methods. LocalNet provided accurate co-registration of substructures by optimising subfield deformation throughout the volumes, outperforming other methods, especially in the longitudinal breast cancer xenograft dataset by minimising target registration errors.Significance:We showed the feasibility of co-registering repeatedly or longitudinally imaged vascular networks in photoacoustic mesoscopy, taking a step towards longitudinal quantitative characterisation of these complex structures. These tools open new outlooks for monitoring tumour angiogenesis at the meso-scale and for quantifying treatment-induced co-localised alterations in the vasculature.

TRAITER: Transformer-guided diagnosis and prognosis of heart failure using cell nuclear morphology and DNA damage marker.

Heart failure (HF), a major cause of morbidity and mortality, necessitates precise diagnostic and prognostic methods. This study presents a novel deep learning approach, Transformer-based Analysis of Images of Tissue for Effective Remedy (TRAITER), for HF diagnosis and prognosis. Employing image segmentation techniques and a Vision Transformer, TRAITER predicts HF likelihood from cardiac tissue cell nuclear morphology images and the potential for left ventricular reverse remodeling (LVRR) from dual-stained images with cell nuclei and DNA damage markers. In HF prediction using 31,158 images from 9 patients, TRAITER achieved 83.1% accuracy. For LVRR prediction with 231,840 images from 46 patients, TRAITER attained 84.2% accuracy for individual images and 92.9% for individual patients. TRAITER outperformed other neural network models in terms of receiver operating characteristics, and precision-recall curves. Our method promises to advance personalized HF medicine decision-making. The source code and data are available at the following link: Https://github.com/HamanoLaboratory/predict-of-HF-and-LVRR. Supplementary data are available at Bioinformatics online.

A New Kiwi Fruit Detection Algorithm Based on an Improved Lightweight Network

To address the challenges associated with kiwi fruit detection methods, such as low average accuracy, inaccurate recognition of fruits, and long recognition time, this study proposes a novel kiwi fruit recognition method based on an improved lightweight network S-YOLOv4-tiny detection algorithm. Firstly, the YOLOv4-tiny algorithm utilizes the CSPdarknet53-tiny network as a backbone feature extraction network, replacing the CSPdarknet53 network in the YOLOv4 algorithm to enhance the speed of kiwi fruit recognition. Additionally, a squeeze-and-excitation network has been incorporated into the S-YOLOv4-tiny detection algorithm to improve accurate image extraction of kiwi fruit characteristics. Finally, enhancing dataset pictures using mosaic methods has improved precision in the characteristic recognition of kiwi fruits. The experimental results demonstrate that the recognition and positioning of kiwi fruits have yielded improved outcomes. The mean average precision (mAP) stands at 89.75%, with a detection precision of 93.96% and a single-picture detection time of 8.50 ms. Compared to the YOLOv4-tiny detection algorithm network, the network in this study exhibits a 7.07% increase in mean average precision and a 1.16% acceleration in detection time. Furthermore, an enhancement method based on the Squeeze-and-Excitation Network (SENet) is proposed, as opposed to the convolutional block attention module (CBAM) and efficient channel attention (ECA). This approach effectively addresses issues related to slow training speed and low recognition accuracy of kiwi fruit, offering valuable technical insights for efficient mechanical picking methods.

Accuracy of the LaserSAFE technique for detecting positive surgical margins during robot-assisted radical prostatectomy: blind assessment and inter-rater agreement analysis.

Fluorescence confocal microscopy (FCM) is a new imaging modality capable of generating digital microscopic resolution scans of fresh surgical specimens, and holds potential as an alternative to frozen section (FS) analysis for intra-operative assessment of surgical margins. Previously, we described the LaserSAFE technique as an application of FCM for margin assessment in robot-assisted radical prostatectomy (RARP) using the Histolog® scanner. This study describes the accuracy and inter-rater agreement of FCM imaging compared to corresponding paraffin-embedded analysis (PA) among four blinded pathologists for the presence of positive surgical margins (PSM). RARP specimens from patients enrolled in the control arm of the NeuroSAFE PROOF study (NCT03317990) were analysed from April 2022 to February 2023. Prostate specimens were imaged using the Histolog® scanner before formalin fixation and PA. Four trained assessors, blinded to PA, reviewed and analysed FCM images of the posterolateral prostatic surface. A total of 31 prostate specimens were included in the study. PA per lateral side of the prostate identified 11 instances of positive margins. Among the four histopathologists included in our study, FCM achieved a sensitivity of 73-91 and specificity of 94-100% for the presence of PSM. Fleiss' Kappa for inter-rater agreement on PSM was 0.78 (95% confidence interval = 0.64-0.92), indicating substantial agreement. This blinded analysis of FCM versus PA among histopathologists with different experience levels demonstrated high accuracy and substantial inter-rater agreement for diagnosing PSM. This supports the role of the FCM as an alternative to FS.

Isotropic Brain MRI Reconstruction from Orthogonal Scans Using 3D Convolutional Neural Network.

As an alternative to true isotropic 3D imaging, image super-resolution (SR) has been applied to reconstruct an isotropic 3D volume from multiple anisotropic scans. However, traditional SR methods struggle with inadequate performance, prolonged processing times, and the necessity for manual feature extraction. Motivated by the exceptional representational ability and automatic feature extraction of convolutional neural networks (CNNs), in this work, we present an end-to-end isotropic MRI reconstruction strategy based on deep learning. The proposed method is based on 3D convolutional neural networks (3D CNNs), which can effectively capture the 3D structural features of MRI volumes and accurately predict potential structure. In addition, the proposed method takes multiple orthogonal scans as input and thus enables the model to use more complementary information from different dimensions for precise inference. Experimental results show that the proposed algorithm achieves promising performance in terms of both quantitative and qualitative assessments. In addition, it can process a 3D volume with a size of 256 × 256 × 256 in less than 1 min with the support of an NVIDIA GeForce GTX 1080Ti GPU, which suggests that it is not only a quantitatively superior method but also a practical one.

3D-Aligner: advanced computational tool for correcting image distortion in expansion microscopy

Expansion Microscopy (ExM) is an innovative and cost-effective super-resolution microscopy technique that has become popular in cell biology research. It achieves super-resolution by physically expanding specimens. Since its introduction, ExM has undergone continuous methodological developments to enhance its resolution and labeling capabilities. However, ExM imaging often encounters sample drift during image acquisition due to the physical movement of the expanded hydrogel, posing a significant challenge for accurate image reconstruction. Despite many proposed experimental solutions to mitigate sample drift, a universal solution has yet to be established. In response to this challenge, we developed 3D-Aligner, an advanced and user-friendly image analysis tool designed to computationally correct drift in ExM images for precise three-dimensional image reconstruction and downstream quantification. We demonstrate that 3D-Aligner effectively determines and corrects drift in ExM images with different expansion rates and various fluorescently labeled biological targets, showcasing its capabilities and robustness in drift correction. Additionally, we validate the precision of 3D-Aligner by comparing drift values across different labeled targets and highlight the importance of drift correction in quantification of biological structures.

Kilohertz volumetric imaging of in-vivo dynamics using squeezed light field microscopy.

Volumetric functional imaging of transient cellular signaling and motion dynamics poses a significant challenge to current microscopy techniques, primarily due to limitations in hardware bandwidth and the restricted photon budget within short exposure times. In response to this challenge, we present squeezed light field microscopy (SLIM), a computational imaging method that enables rapid detection of high-resolution three-dimensional (3D) light signals using only a single, low-format camera sensor area. SLIM pushes the boundaries of 3D optical microscopy, achieving over one thousand volumes per second across a large field of view of 550 μm in diameter and 300 μm in depth with a spatial resolution of 3.6 μm laterally and 6 μm axially. Using SLIM, we demonstrated blood cell velocimetry across the embryonic zebrafish brain and in a free-moving tail exhibiting high-frequency swinging motion. The millisecond temporal resolution also enables accurate voltage imaging of neural membrane potentials in the leech ganglion, and in the hippocampus of behaving mice. These results collectively establish SLIM as a versatile and robust imaging tool for high-speed microscopy applications.

Iliac vein obstruction: accuracy of Direct Multidetector Computed Tomographic Venography and duplex ultrasound.

To evaluate the accuracy of direct computed tomographic venography (DCTV) and duplex ultrasound (DUS) in the identification of iliac vein obstruction in highly symptomatic patients with severe chronic venous disease (CVD) compared with intravascular ultrasound (IVUS). this study involved patients who had advanced CVD (CEAP C3-6). All patients underwent DCTV, venous duplex scanning, and IVUS. The presence of iliac vein obstruction was detected, and degree of obstruction was classified into 3 grades (grade I, 0% to 49%; grade II, 50% to 79%; and grade III, 80% or greater). The sensitivity, specificity, PPV, NPV, and accuracy were calculated for each modality compared with IVUS. Inter-observer agreement was assessed using the κ coefficient. of 94 patients with CVD, IVUS identified iliac vein obstruction in 55 (58.5%) patients (25.5% was grade 1, 27.3% was grade 2, 47.3% was grade 3). The sensitivity, specificity of DCTV in diagnosing obstruction was (96%, 95% in grade 1; 100%, 100% grade 2; 100%, 100% in grade 3, respectively). The sensitivity, specificity of DUS was (63.9%, 65% in grade 1; 68%, 82% in grade 2, and 70%, 85% in grade 3, respectively). The overall agreement of DUS was 0.73 (95% CI, 0.70-0.79), and DCTV was 0.96 (95% CI, 0.91- 0.97). DUS is a reasonable initial imaging modality for the identification of significant iliac vein obstruction. DCTV provides an accurate reproducible imaging for accurate estimation needed for treatment planning. Direct CT Venography provides accurate reproducible radiological information required for treatment planning of patients with Iliac vein obstruction including accurate assessment of site of obstruction, its morphology, and degree of obstruction and it can replace the use of indirect CT venography using smaller amount of contrast media with accurate diagnosis.

Gradient transformation-weighted centroid: enhancing peak localization precision in the line chromatic confocal sensing system

Line chromatic confocal imaging (LCI) is an advanced system used for quick and accurate three-dimensional surface imaging without vertical mechanical scanning. It is extensively employed for fast industrial inspection. For high-speed and accurate measurement characteristics of the LCI technique, a rapid and accurate peak localization method is important. In this paper, we propose a gradient transform weight centroid method (GTWC) to improve the accuracy of peak positioning, without compromising high-speed characteristics. This method helps reconstruct the 3D profile at a higher axial resolution by reducing the full width at half maximum (FWHM) without altering the original structure of the LCI system. Both simulations and experiments show the feasibility and good performance of this method.

High-precision geometric positioning for optical remote sensing satellite in dynamic imaging

ABSTRACT Dynamic imaging of optical remote sensing satellites refers to the active acquisition of images while the satellite is maneuvering at a high angular velocity, which significantly enhances the efficiency and application value of remote sensing imagery. However, due to the influence of rapid maneuvering, the random error of star sensors significantly increases, resulting in a decrease in the geometric positioning accuracy of dynamic imaging remote sensing images. This paper proposes a dynamic fusion method for multisource attitude measurement data based on the noise adaptive estimation and bidirectional filter to achieve high-precision attitude determination and geometric positioning in dynamic imaging. Based on the measurement error model of star sensors, the weights of the star sensor and gyroscope are adaptively adjusted in multisource data fusion to reduce the impact of star sensor measurement errors on the gyroscope. Moreover, a bidirectional fusion filter that includes a low-velocity maneuvering stage is proposed to realize the optimal estimation of the satellite attitude parameter. The simulation data and onboard data of the Luojia3-01 (LJ3–01) satellite were tested to verify the effectiveness of the proposed method. The geometric positioning accuracy of the staring images of LJ3–01 improved from 10.048 m to 7.538 m. The registration accuracy of the sequential images improved from 3.568 pixels to 1.179 pixels. The proposed method can significantly improve the attitude determination accuracy and the geometric positioning accuracy of LJ3–01 satellite staring images. Moreover, for simulation data with various angular velocities, the attitude determination accuracies of the proposed method are better than 0.93”. The experimental results show that the proposed method can achieve high-precision attitude determination in dynamic imaging, reaching the accuracy in the traditional passive imaging.

Light-Triggered Plasmonic DNAzyme Walker Enables Precise Subcellular Molecular Imaging with Reduced Off-Mitochondria Signal Leakage.

The development of highly sensitive and precise imaging techniques capable of visualizing crucial molecules at the subcellular level is essential for elucidating mitochondrial functions and uncovering novel mechanisms in biological processes. However, traditional molecular imaging strategies are still limited by off-mitochondria signal leakage because of the "always-active" sensing mode. To address this limitation, we have developed a light-triggered activation sequence activated plasmonic DNAzyme walker (PDW) for accurate subcellular molecular imaging by the combination of an organelle localized strategy, upconversion nanotechnology, and a plasmon enhanced fluorescence (PEF) technique. Exploiting the advantage of light activation enables precise control over when and where to activate the probe's sensing function, effectively reducing off-mitochondria signal leakage as validated by the dynamic monitoring of changes in off-mitochondria signals during the mitochondrial entry process. Furthermore, by leveraging the PEF capability of triangular gold nanoprisms (Au NPRs), the fluorescence intensity can be enhanced by approximately 11.9 times, ensuring highly sensitive and accurate subcellular molecular imaging.