Accurate Image Research Articles

Current role of focal therapy in prostate cancer.

Thanks to the improved accuracy of multiparametric Magnetic Resonance Imaging (mpMRI) to detect and localize the dominant index lesion on prostate cancer (PCa), the concept of minimally invasive focal treatments (FT) has gained popularity. Nevertheless, although high-quality evidence that FT has favorable functional outcomes, definitive proof of its oncological effectiveness compared to standard treatments remains underreported. This study aims to explore the efficacy, safety, oncologic and functional outcomes of different type of FT for PCa. A non-systematic literature review was conducted by searching on PubMed "prostate cancer" AND "focal treatment" OR "HIFU" OR "irreversible electroporation" OR "cryoablation" OR "focal laser ablation." Overall, 22 articles published in English literature from 2018 to 2023 describing FT techniques for PCa were included. Related to the field ablated, focal therapies include hemiablation (half gland), focal ablation (zonal-base) or targeted therapy (lesion-based). FT modalities can be categorized in thermal and non-thermal energy: thermal energy techniques include high-intensity focused ultrasound (HIFU), cryotherapy, radiofrequency ablation (RFA), and focal laser ablation (FLA). Focal brachytherapy, irreversible electroporation (IRE), and vascular-targeted photodynamic therapy (VTP) are classified as non-thermal ablation. Each focal ablative treatments could be promising alternative to conventional radical therapies to treat PCa in term of mid-term oncologic and functional outcomes, however, limited data are available in comparison with standardized radical treatments. HIFU and Cryo are the most studied techniques and seem to be superior to radical treatments in term of functional outcomes despite associated with higher recurrence and retreatment rate. Current evidences of oncologic and functional outcomes of FT are based mainly on retrospective, non-comparative and single center studies. Thus, none of the focal therapies available should be considered safe alternative to standardized treatments in routine practice and should be offered only for well selected PCa patients with low or at least favorable-intermediate risk disease, unfit for radical options.

In-Situ visual reconstruction of strut profiles in pulsed wire arc additive manufacturing of lattice structures

ABSTRACT The wire arc additive manufacturing (WAAM) process often results in geometric inaccuracies in the produced struts, highlighting the need for in-situ monitoring. Industrial cameras struggle to capture accurate images due to interference from arc light and metal radiation. This study introduces a real-time visual reconstruction method that leverages regions of interest (ROI) to enhance profile accuracy. Initially, the melt pool is analyzed and segmented from a series of frames to establish an initial strut profile. This profile then serves as the ROI for segmenting the solidified weld bead regions, yielding a more precise strut profile. Comparative analysis shows that the proposed method achieves a high Intersection Over Union (IOU) score of 0.911 and an Average Symmetric Surface Distance (ASD) error of 0.355 mm, demonstrating both accuracy and robustness. Additionally, the inclination angle and diameter are extracted from the reconstructed profiles.

Image–Text Matching Model Based on CLIP Bimodal Encoding

Image–text matching is a fundamental task in the multimodal research field, connecting computer vision and natural language processing by aligning visual content with corresponding textual descriptions. Accurate matching is critical for applications such as image captioning and text-based image retrieval yet remains challenging due to the differences in data modalities. This paper addresses these challenges by proposing a robust image–text matching model inspired by Contrastive Language–Image Pre-training (CLIP). Our approach employs the Vision Transformer (ViT) model as the image encoder and Bidirectional Encoder Representations from Transformers (Bert) as the text encoder, integrating these into a shared vector space to measure semantic similarity. We enhance the model’s training efficiency using the LiT-tuning paradigm to optimize learning through a cosine decay strategy for dynamic adjustment of the learning rate. We validate our method on two benchmark datasets, WuKong and Flickr30k, demonstrating that our model achieves superior performance and significantly improves key evaluation metrics. The results underscore the model’s effectiveness in achieving accurate and robust image–text alignment.

Detection of Indian Counterfeit Currency Notes with MATLAB-Based Feature Extraction

Fakes are a major threat to India’s economy hence the need for a solution that will help detect these materials easily and accurately. The detection of counterfeit currency is a major concern for almost all countries, and this research focuses on developing a MATLAB-based counterfeit currency detection system which evaluates a true image of the Indian Currencies with the help of image processing and pattern recognition technique. Benefiting from MATLAB’s powerful image processing resources, the system conducts the necessary preprocessing, feature extraction and classification of vital security elements of currency, such as watermarks, security threads and micro-lettering which play an important role in identifying the genuine currency from the counterfeit. The specific characteristics of edge and texture are statistically and geometrically calculated, and the normal and high-resolution light conditions are at high accuracy with varying resolutions. In order to determine a distinction between actual and fake notes, support vector machine (SVM) classifiers are used. By validating this MATLAB solution, it has been determined to be effective as an easy to use, robust and customizable software that has the potential to work in numerous operations within banking and retail and prevent the spreading of counterfeit money.

Quantitative Evaluation of Enamel Thickness in Maxillary Central Incisors in Different Age Groups Utilizing Cone Beam Computed Tomography a Retrospective Analysis

Background/Objectives: The presence of enamel on the tooth surface is crucial for the long-term success of minimally invasive adhesive restorations such as dental veneers. Our study aims to evaluate the enamel thickness in the incisal, middle, and cervical portions of the labial surface of the upper central incisors using cone beam computed tomography (CBCT). This imaging method provides detailed and accurate three-dimensional images with a low radiation dose, allowing an accurate assessment of enamel thickness. The analysis aims to identify variations in enamel thickness depending on the age and different levels of the labial tooth surface. Methods: 800 CBCT scans performed for diagnostic or therapeutic purposes on patients aged 18–60 years were analyzed. The data were gathered from the imaging archives of private practitioners from Targu Mures and the “George Emil Palade” University of Medicine, Pharmacy, Science, and Technology of Targu Mures. Enamel thickness measurements were conducted using the OnDemand3D Communicator CBCT evaluation program, with subsequent statistical analysis performed using GraphPad Instat Prism software. Results: Results showed significant variation in enamel thickness between the incisal, middle, and cervical segments of the labial surface of the upper central incisors. A decrease in enamel thickness with age has been observed. In patients aged 18–40, mean values of enamel thickness 1 mm and 3 mm above the cementoenamel junction (CEJ) were 0.48 ± 0.092, respectively, 0.819 ± 0.158. In patients over 40, the mean values were 0.454 ± 0.116 and 0.751 ± 0.067 at 1 mm, respectively, 3 mm above the CEJ. Statistically significant differences were found between the two age groups at 1 mm and 3 mm above the CEJ, with p < 0.0001 and p = 0.0214. Conclusions: A statistically significant decrease can be observed in enamel thickness in almost the entire labial surface of the upper central incisors with aging. The varied thickness of the enamel at different tooth levels requires individualized planning for each patient to maximize the long-term aesthetic and functional results.

Nonaqueous Contact-Electro-Chemistry via Triboelectric Charge.

Mechanochemistry revolutionizes traditional reactions through mechanical stimulation, but its reaction efficiency is limited. Recent advancements in utilizing triboelectric charge from liquid-solid contact electrification (CE) have demonstrated significant potential in improving the reaction efficiency. However, its efficacy remains constrained by interfacial electrical double-layer screening in aqueous solutions. This study pioneered chemistry in nonaqueous systems via CE for catalysis and luminescence. Density functional theory simulations and experiments revealed varying electron transfer capabilities and chemoselectivity of CE across different solvents. Phenol degradation via CE in dimethyl sulfoxide (DMSO) exhibited a rate over 40 times faster than that of traditional mechano-driven chemistry. A more intuitive comparison revealed that CE degradation of phenol in DMSO exhibits a 30-fold rate improvement compared to deionized water, where the degradation remains incomplete. Luminol oxidation by radicals generated solely via CE in DMSO eliminates the dependence on traditional catalysts and side reactions, establishing a pure and simple system for investigating the reaction mechanisms. A high and stable luminescence characteristic was maintained for 3 months, enhancing the imaging accuracy and stability exponentially. This study underscores the impact of triboelectric charge on reaction efficiency and chemoselectivity, establishing a new paradigm in nonmetal catalysis, mechanoluminescence, and providing profound insights into reaction kinetics.

Revolutionizing early breast cancer screening: Advanced multi-spectral transmission imaging classification with improved Otsu's method and K-means clustering

Breast cancer stands as a formidable danger to the health of women worldwide, underscoring the critical need for effective screening methods. Multispectral transmission imaging offers a promising avenue due to its non-invasive potential for early screening. Some researchers already suggested registration to solve the problem of jitters due to respiration and movement and frame accumulation technology to solve the low grayscale problem. However, the classification of blood vessels and breast tissue in breast images often suffers from low signal-to-noise ratio (SNR) and low contrast, hindering accurate classification. This paper proposes a novel improved Otsu's method with K-Means clustering to address this challenge. The proposed method aims to enhance the foundation for classification using multispectral transmission images. The study utilizes multispectral transmission images captured at four wavelengths, representing an innovative avenue for early, affordable breast cancer screening research. Initially, 300 images are registered and accumulated to prove the efficiency of the suggested methodology. Then, median filtering is applied to reduce noise in the images. Improved Otsu's segmentation method is then employed to separate blood vessels from breast tissue. After that, K-means clustering is utilized to accurately classify these components. The results of the proposed method demonstrate significant improvements in classification accuracy and grayscale contrast of multispectral breast images. By effectively distinguishing blood vessels and breast tissue, the proposed methodology addresses the inherent challenges of low contrast in multispectral transmission imaging. This advancement offers a clearer pathway for early breast cancer screening.

Smartphone microscopy: Design and implementation of a dual magnification system

This paper presents a dual magnification smartphone microscope designed for versatile imaging, enabling low magnification for wide-field viewing and high magnification for detailed biological sample analysis and automatic control of the working distance. It offers a cost-effective alternative to traditional lab microscopes, particularly in low-resource settings. Simulated, optimized, and designed using Ansys Zemax OpticStudio, the microscope was fabricated with 3D printing technology. Dual magnification (150x and 1000x) was achieved with two optical paths, improving resolution to 1 µm at higher and 1.5 µm at lower magnifications. The design provides clear and accurate images. The smartphone integration enhances usability, allowing easy magnification switching, image capture, and potential use of diagnostic applications.

Ultrasound Imaging for the Evaluation of Anterior Talofibular Ligament Remnants in 547 Ankles With Chronic Lateral Ankle Instability.

There are few reports on the intra- and interobserver agreement and parameters for the diagnostic accuracy of ultrasound (US) imaging for chronic lateral ankle instability (LAI). The purpose of this study was to investigate the reliability and validity of US imaging for identifying anterior talofibular ligament (ATFL) remnants in patients with LAI. A total of 547 ankles from 406 patients underwent surgery for LAI between 2019 and 2022. If ligament fibers remained in US images, they were evaluated as positive. If the ligament was not visualized, it was evaluated as negative. Two observers performed repeated measurements. Arthroscopic findings were considered the "gold standard" for validity and diagnostic test accuracy purposes. The intra- and interobserver agreements and parameters for diagnostic accuracy, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of preoperative US imaging as intraoperative arthroscopic findings were used as reference standards. The intraobserver agreement was substantial, with an agreement of 98.54% and a kappa coefficient of 0.76. The interobserver agreement was also substantial, with an agreement of 98.72% and a kappa coefficient of 0.75. The sensitivity, specificity, and accuracy of preoperative US imaging were 98.7%, 100%, and 98.7%, respectively. The PPV and NPV of US imaging were 100% and 61.1%, respectively. In the arthroscopic evaluation of the 7 cases in which US imaging showed false negative results, the ATFL ruptured at the fibular attachment and ran in contact with the talus. A US examination finding of an intact ATFL is highly likely to be correct. An US examination finding of a ruptured ATFL can be false and may require arthroscopic confirmation.

Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence

In this paper, we propose a method for optimizing the parameter values in iterative reconstruction algorithms that include adjustable parameters in order to optimize the reconstruction performance. Specifically, we focus on the power divergence-based expectation-maximization algorithm, which includes two power indices as adjustable parameters. Through numerical and physical experiments, we demonstrate that optimizing the evaluation function based on the extended power-divergence and weighted extended power-divergence measures yields high-quality image reconstruction. Notably, the optimal parameter values derived from the proposed method produce reconstruction results comparable to those obtained using the true image, even when using distance functions based on differences between forward projection data and measured projection data, as verified by numerical experiments. These results suggest that the proposed method effectively improves reconstruction quality without the need for machine-learning techniques in parameter selection. Our findings also indicate that this approach is useful for enhancing the performance of iterative reconstruction algorithms, especially in medical imaging, where high-accuracy reconstruction under noisy conditions is required.

Diagnostic Accuracy of Magnetic Resonance Imaging in the 120° Flexed-Knee Position for Detecting and Classifying Meniscal Ramp Lesion.

Detection of meniscal ramp lesions concomitant with anterior cruciate ligament (ACL) injury using conventional magnetic resonance imaging (MRI) has low sensitivity, and these lesions are currently difficult to diagnose preoperatively. The purpose of this study was to assess the accuracy of MRI in detecting the presence of meniscal ramp lesions in the 120° flexed-knee position compared with that in the near-extended-knee position. It was hypothesized that the diagnostic performance of MRI in the 120° flexed-knee position would be better than that in the extended-knee position. Cohort study (Diagnosis); Level of evidence, 2. This retrospective study of prospectively collected data between February 2019 and January 2024 included 154 consecutive patients undergoing ACL reconstruction. All patients underwent 3-T MRI examination in the near extended- and 120° flexed-knee positions preoperatively. The presence and Thaunat classification of ramp lesions were separately detected on each MRI scan and confirmed via arthroscopy during ACL reconstruction. Diagnostic sensitivity, specificity, and conditional relative odds ratios for detecting ramp lesions and the classification accuracy were compared between 2 MRI modalities. The accuracies of these MRI scans in acute and chronic cases were also evaluated. This study included 154 patients (79 male and 75 female; mean age, 29.0 ± 14.2 years). A total of 62 ramp lesions (40.3%) were observed on arthroscopy. The sensitivity and specificity of MRI in near extension were 69.4% and 77.2%, respectively, and those in flexion were 91.9% and 94.6%, respectively, with significant superiority in MRI at knee flexion (P = .003 and P < .001, respectively). The conditional relative odds ratio between the MRI examinations at these 2 positions was 10.3 (95% CI, 4.82-21.8). The classification accuracy of MRI in flexion was significantly higher than that of MRI in near extension (accuracy, 49 vs 11; P < .001). The diagnostic accuracy of MRI in the 120° flexed-knee position was significantly higher than that in the near-extended-knee position in the acute cases (P < .05); however, no significant differences were observed in the chronic cases. The diagnostic accuracy of MRI in the 120° flexed-knee position for detecting and classifying meniscal ramp lesions was superior to that of MRI in the near-extended-knee position.

Achromatic metalenses for full visible spectrum with extended group delay control via dispersion-matched layers

Achieving achromaticity across the visible light spectrum is crucial for metalenses in imaging systems. Single-layer metalenses struggle with weak focusing power or small aperture sizes due to inadequate group delay control. Multilayer metalenses offer some improvement but come with increased design and fabrication complexity. Here, we demonstrate a strategy using meta-atoms with material layers engineered for matching dispersion, allowing large and fine adjustments of group delay. Our design substantially broadens the group delay range, allowing us to experimentally demonstrate several polarization-independent metalenses operating across the entire visible spectrum (400-700 nm). We design, fabricate, and characterize achromatic metalenses with aperture diameters of 16 μm, 66 μm, 200 μm, and 400 μm, and numerical apertures of 0.27, 0.11, 0.04, and 0.02, respectively. Among them, the 400-μm diameter, 0.02-numerical-aperture metalens is used to demonstrate full-color imaging capabilities. Our results exhibit diffraction-limited performance, high efficiency, and accurate full-color image reproduction.

Research on the Forward Simulation and Intelligent Detection of Defects in Highways Using Ground-Penetrating Radar

The increasing variety and frequency of subgrade defects in operational highways have led to a rise in road safety incidents. This study employed ground-penetrating radar (GPR) detection and forward simulation to analyze the characteristic patterns of common subgrade defects, such as looseness, voids, and cavities. Through the integration of instantaneous feature information from different defect patterns with complex signal techniques, the boundary judgment of structural layers and anomalies in GPR images of various subgrade defects was improved. An intelligent recognition platform was established, and a radar image dataset was created and trained to evaluate the recognition performance of the You Only Look Once (YOLO) v3 and Single-Shot Multi-Box Detector (SSD) algorithms. Evaluation metrics such as precision, recall, F1-score, average precision (AP), and mean average precision (mAP) were used to assess the detection efficiency and accuracy for subgrade defect images. The results showed that YOLO v3 achieved an average detection accuracy of 76.69%, while the SSD achieved 75.07%. This study demonstrates that the reliability of the intelligent recognition and classification of highway subgrade defects can be enhanced by using GPR for non-destructive testing.

A vessel bifurcation liver CT landmark pair dataset for evaluating deformable image registration algorithms.

Evaluating deformable image registration (DIR) algorithms is vital for enhancing algorithm performance and gaining clinical acceptance. However, there is a notable lack of dependable DIR benchmark datasets for assessing DIR performance except for lung images. To address this gap, we aim to introduce our comprehensive liver computed tomography (CT) DIR landmark dataset library. This dataset is designed for efficient and quantitative evaluation of various DIR methods for liver CTs, paving the way for more accurate and reliable image registration techniques. Forty CT liver image pairs were acquired from several publicly available image archives and authors' institutions under institutional review board (IRB) approval. The images were processed with a semi-automatic procedure to generate landmark pairs: (1) for each case, liver vessels were automatically segmented on one image; (2) landmarks were automatically detected at vessel bifurcations; (3) corresponding landmarks in the second image were placed using two deformable image registration methods to avoid algorithm-specific biases; (4) a comprehensive validation process based on quantitative evaluation and manual assessment was applied to reject outliers and ensure the landmarks' positional accuracy. This workflow resulted in an average of ∼56 landmark pairs per image pair, comprising a total of 2220 landmarks for 40 cases. The general landmarking accuracy of this procedure was evaluated using digital phantoms and manual landmark placement. The landmark pair target registration errors (TRE) on digital phantoms were 0.37±0.26 and 0.55±0.34mm respectively for the two selected DIR algorithms used in our workflow, with 97% of landmark pairs having TREs below 1.5mm. The distances from the calculated landmarks to the averaged manual placement were 1.27±0.79mm. All data, including image files and landmark information, are publicly available at Zenodo (https://zenodo.org/records/13738577). Instructions for using our data can be found on our GitHub page at https://github.com/deshanyang/Liver-DIR-QA. The landmark dataset generated in this work is the first collection of large-scale liver CT DIR landmarks prepared on real patient images. This dataset can provide researchers with a dense set of ground truth benchmarks for the quantitative evaluation of DIR algorithms within the liver.

MCI Net: Mamba- Convolutional lightweight self-attention medical image segmentation network.

With the development of deep learning in the field of medical image segmentation, various network segmentation models have been developed. Currently, the most common network models in medical image segmentation can be roughly categorized into pure convolutional networks, Transformer-based networks, and networks combining convolution and Transformer architectures. However, when dealing with complex variations and irregular shapes in medical images, existing networks face issues such as incomplete information extraction, large model parameter sizes, high computational complexity, and long processing times. In contrast, models with lower parameter counts and complexity can efficiently, quickly, and accurately identify lesion areas, significantly reducing diagnosis time and providing valuable time for subsequent treatments. Therefore, this paper proposes a lightweight network named MCI-Net, with only 5.48 M parameters, a computational complexity of 4.41, and a time complexity of just 0.263. By performing linear modeling on sequences, MCI-Net permanently marks effective features and filters out irrelevant information. It efficiently captures local-global information with a small number of channels, reduces the number of parameters, and utilizes attention calculations with exchange value mapping. This achieves model lightweighting and enables thorough interaction of local-global information within the computation, establishing an overall semantic relationship of local-global information. To verify the effectiveness of the MCI-Net network, we conducted comparative experiments with other advanced representative networks on five public datasets: X-ray, Lung, ISIC-2016, ISIC-2018, and capsule endoscopy and gastrointestinal segmentation. We also performed ablation experiments on the first four datasets. The experimental results outperformed the other compared networks, confirming the effectiveness of MCI-Net. This research provides a valuable reference for achieving lightweight, accurate, and high-performance medical image segmentation network models.

Spondylodiscitis of the thoraco-lumbar spine: diagnostic performance of dual-energy CT vs MRI.

Dual-energy computed tomography (DECT) could combine the high-resolution bone window images made available by multi-detector CT technology with its capability to identify bone marrow edema (BME) in the spine, for diagnosing spondylodiscitis. Our objective was to compare the diagnostic performance of contrast-enhanced MRI and non-contrast DECT to identify spondylodiscitis of the thoraco-lumbar spine. This prospective study included 77 consecutive participants (39 males; mean age of 61) who underwent DECT and MRI (within 7 days) between January 2020 and October 2023. DECT data were post-processed on a dedicated offline workstation (SyngoVia® VB20) by using a three-material decomposition algorithm. Four radiologists, blinded to clinical data, evaluated non-contrast DECT and contrast-enhanced MRI images. The diagnosis of spondylodiscitis was based on vertebral edema, disc edema, endplate erosions, and paraspinal involvement. Diagnostic accuracy values were calculated by using biopsy as a standard of reference. A multi-reader multi-case analysis was performed. Biopsy revealed a diagnosis of spondylodiscitis in 46 patients (60%). Thoracic and lumbar spondylodiscitis were diagnosed in 37/46 (80%) and 9/46 (20%) patients, respectively. DECT and MRI overall sensitivity, specificity, and AUC were 0.91, 0.89, and 0.90, and 0.94, 0.93, and 0.93, respectively. At lumbar and thoracic levels, the difference between AUC values between DECT and MRI was not significant (p = 0.15). For DECT and MRI, a very good inter-reader agreement was achieved (k = 0.90 and k = 0.97, respectively). Contrast-enhanced MRI represents the most accurate imaging tool for the diagnosis of spondylodiscitis. However, only a non-significant drop in diagnostic performance was achieved by evaluating non-contrast DECT images. Question To compare the diagnostic performance of contrast-enhanced MRI and non-contrast DECT for identifying spondylodiscitis of the thoraco-lumbar spine. Findings MRI was not significantly superior compared to DECT in diagnosing spondylodiscitis, whereas the inter-reader agreement was near perfect for both MRI and DECT. Clinical relevance DECT represents a fast and accurate imaging tool for the demonstration of BME, erosions, and peri-vertebral inflammation in thoraco-lumbar spondylodiscitis.

Deep learning-assisted local resonance strategy for accurate internal damage imaging in composites

In this paper, we propose a deep neural network-assisted strategy to accurately and efficiently identify local defect resonance (LDR) modes and accurately image the internal damage in composites. A two-dimensional convolutional neural network (2D-CNN) model was constructed to identify LDR modes. The frequency-domain contour maps were used as input data, given that the LDR phenomenon exhibits discernible physical attributes in the frequency domain that are conducive to deep neural network assimilation. The obtained results demonstrate effective training outcomes and transferability, even with a limited number of samples. The LDR modes are efficiently extracted by the developed 2D-CNN model and used to obtain the accurate imaging of internal damages in composites.

Measuring brain perfusion by computed tomography or magnetic resonance as ancillary tests for diagnosis of brain death: a systematic review and meta-analysis

Abstract Objectives To gather and synthesise evidence regarding diagnostic accuracy of perfusion imaging by CT (CTP) or MR (MRP) for brain death (BD) diagnosis. Methods A systematic review and meta-analysis was prospectively registered with PROSPERO (CRD42022336353) and conducted in accordance with the PRISMA guidelines and independently by three reviewers. PubMed/MEDLINE®, EMBASE® and Cochrane Database were searched for relevant studies. Quality Assessment of Diagnostic Accuracy Studies-2 was used to assess studies’ quality. Meta-analysis was performed using univariate random-effects models. Results Ten studies (328 patients) were included. Perfusion imaging (most commonly CTP, n = 8 studies) demonstrated a high sensitivity of 96.1% (95% CI: 89.5–98.6) for BD, consistent in subgroup analysis at 95.5% (95% CI: 86.5–98.6). Unfortunately, it was not feasible to calculate other metrics. Additionally, evidence of publication bias was identified in our findings. Conclusions The sensitivity of CTP or MRP for BD diagnosis is very high, comparable to CTA and TCD. However, considering most studies were retrospective, and lacked control groups and unambiguous criteria for perfusion imaging in BD assessment, results should be interpreted with caution. Future studies, ideally prospective, multi-centre and with control groups are of utmost importance for validation of these methods, particularly with standardized technical parameters. Advances in Knowledge Cerebral perfusion imaging using CT or MRI demonstrates high sensitivity in diagnosing BD, on par with CTA and TCD. Recommended by the World Brain Death group, this method holds promise for further investigation in this area.

Evaluating the Effectiveness of ICESat-2 ATL03 Data for Georeferencing Satellite Images

Abstract. ICESat-2 laser points, as a kind of public geographic data, have high ranging accuracy and have great potential in improving the geometric positioning accuracies of satellite images. This paper aims to evaluate the effectiveness of ICESat-2 ATL03 data for georeferencing satellite image. Our study combines ATL03 laser points with an image basemap processed using open-source data positioning to construct digital control photo (DCP), facilitating the rapid transformation of laser points into laser control points for image georeferencing. Through a comprehensive analysis involving comparison with traditional georeferencing methods using ground control points (GCPs), our findings indicate a significant enhancement in accuracy and efficiency in georeferencing, showcasing the potential of ICESat-2 data in improving the quality and reliability of remote sensing images. Experiments using the extracted laser points and GeoEye-1 images to construct DCP for georeferencing IKONOS images in Hobart, Australia demonstrate that the proposed method improved the horizontal accuracy from 2.33 meters to 0.97 meters and the vertical accuracy from 3.44 meters to 0.59 meters, compared to the georeferencing method without GCPs.

Exploring the Interplay of Wavelength, Quantum Yield, and Penetration Depth in In Vivo Fluorescence Imaging.

The intricate interplay between the irradiation wavelength, the fluorophore quantum yield (QY) and penetration depth profoundly influences the efficacy of in vivo fluorescence imaging in various applications. Understanding the complex behavior of fluorescence in vivo, specifically how variations in wavelength affect the QY of commonly used dyes and the depth of imaging is crucial for optimizing fluorescence imaging techniques, as it directly impacts the accuracy and efficiency of imaging in biological tissues. In our study, we explore these dynamics through Monte Carlo simulations conducted under conditions reflective of wide-field fluorescence imaging, examining how variations in wavelength impact the dye's QY and depth of imaging, and consequently, the fluorescence behavior. A transition in the exponential decay of the emission depth exponent is observed around the 500-600nm range, indicating varying degrees of influence of depth on the fluorescence emission. The analysis of the fluorophore's QY reveals wavelength-dependent variations, with the most significant impact observed in the 600-700nm range. Moreover, we continued our investigation to explore multiplexing, unveiling insights into the spacing between identical spots in multiplexing images across various depths and wavelengths.