Search

Solution-processed organic photodetectors (OPDs) with spectral response in the near-infrared (NIR) region have broad applications such as in industries, spectroscopic/medical instruments, communications, image sensing, and environmental monitoring. OPDs capable of selective spectral response are particularly appealing for applications including secure optical data link and color image sensing systems. Here, we report an organic photodetector with a highly selective spectral response to near-infrared radiation without the use of a color filter. The operation of the OPDs takes advantage of a charge collection narrowing effect, resulting in a narrowband response at 760 nm with a full width at half maximum of 47 nm and an external quantum efficiency of 46%. Owing to the features of the narrowband response in NIR, our organic photodetectors are used as optical signal receivers for an optical communication system, which achieves a data transmission rate of 12.6 kB/s with a low bit error rate on the order of 10−4. We further investigated the response bandwidth in devices with different active layer thicknesses and area size and their response bandwidth under different reverse bias conditions as well. The optimized device shows a −3 dB response bandwidth of 3.02 MHz and a data transmission rate of 1.41 Mbit/s. The results highlight the promising potential of the nonfiltered OPDs for a variety of applications, including light communication, short-range data transmission, and image sensing. [ABSTRACT FROM AUTHOR]

Electrothermal instability and its effect on the uniformity of discharge channels during underwater electrical wire explosions at different energy deposition rates have been investigated. According to the liquid–vapor transition mechanism, we classified underwater electrical explosion into "phase explosion mode" and "supercritical mode." Two sets of experiments corresponding to the two modes were conducted, and the exploding wires were observed by a shadow imaging system. Results showed that a bamboo-shaped discharge channel was formed in the phase explosion mode, whereas the discharge channel in the supercritical mode was a uniform cylinder. Self-consistent magnetohydrodynamic simulation results indicated that exploding wires transfer into liquid–vapor coexistence phase in the phase explosion mode, accompanied by a rapid increase in the density perturbation and the formation of bamboo-shaped structures. Moreover, a small perturbation model was derived to analyze the electrothermal instability in different thermodynamic states. It was indicated that the density perturbation is easier to grow in the liquid–vapor coexistence state than the supercritical state, which provides a theoretical explanation for the differences between the two electrical explosion modes. [ABSTRACT FROM AUTHOR]

The scanning electron microscope (SEM) recordings of dynamic nano-electromechanical systems (NEMS) are difficult to analyze due to the noise caused by low frame rate, insufficient resolution, and blurriness induced by applied electric potentials. Here, we develop an image processing platform enhanced by the physics of the underlying system to track the motion of buckling NEMS structures in the presence of high noise levels. The algorithm is composed of an image filter, two data filters, and a nonlinear regression model, which utilizes the expected form of the physical solution. The method was applied to the recordings of a NEMS beam about 150 nm wide, undergoing intra- and inter-well post-buckling states with a transition rate of approximately 0.5 Hz. The algorithm can track the dynamical motion of the NEMS and capture the dependency of deflection amplitude on the compressive force on the beam. With the help of the proposed algorithm, the transition from inter-well to intra-well motion is clearly resolved for buckling NEMS imaged under SEM. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid advancements in deep learning, image processing has progressively attracted the attention of medical researchers to achieve accurate diagnoses of numerous human diseases in a non-invasive manner. Image processing systems are among the most helpful and crucial disease identification technologies. Liver cancer is the world's fifth most prevalent and leading cause of cancer deaths. Diagnoses and treatment are essential because of the high mortality rate and recurrence after treatment. Medical image analysis is extremely beneficial in the healthcare industry. In the last several years, creating and developing computer-assisted imaging systems has been increasingly vital to aid physicians or doctors in strengthening their diagnoses. This research presents a deep model to identify cancerous regions in the liver aims using Computerized Tomography (CT) scans from the Kaggle dataset. Basic preprocessing is applied to CT images from the dataset acquired from the Kaggle repository. The performance of the proposed model is evaluated using different performance metrics, and an accuracy score of 94.3% was achieved in tumour identification. [ABSTRACT FROM AUTHOR]

The article discusses the challenges in accurately measuring acetabular version in the hip using radiographs and compares the reliability of radiographs with CT scans. It highlights technical factors that can affect the accuracy of radiographic measurements and emphasizes the importance of using CT for more precise assessments. The text also calls for standardized imaging protocols, multidisciplinary collaboration, and further research to improve the evaluation of acetabular version and its clinical significance. Additionally, it suggests exploring advanced imaging techniques, such as the EOS system, and leveraging artificial intelligence for automated measurements and analysis. [Extracted from the article]

X‐ray Computed Tomography (CT) images are widely used in various fields of natural, physical, and biological sciences. 3D reconstruction of the images involves segmentation of the structures of interest. Manual segmentation has been widely used in the field of biological sciences for complex structures composed of several sub‐parts and can be a time‐consuming process. Many tools have been developed to automate the segmentation process, all with various limitations and advantages, however, multipart segmentation remains a largely manual process. The aim of this study was to develop an open‐access and user‐friendly tool for the automatic segmentation of calcified tissues, specifically focusing on craniofacial bones. Here we describe BounTI, a novel segmentation algorithm which preserves boundaries between separate segments through iterative thresholding. This study outlines the working principles behind this algorithm, investigates the effect of several input parameters on its outcome, and then tests its versatility on CT images of the craniofacial system from different species (e.g. a snake, a lizard, an amphibian, a mouse and a human skull) with various scan qualities. The case studies demonstrate that this algorithm can be effectively used to segment the craniofacial system of a range of species automatically. High‐resolution microCT images resulted in more accurate boundary‐preserved segmentation, nonetheless significantly lower‐quality clinical images could still be segmented using the proposed algorithm. Methods for manual intervention are included in this tool when the scan quality is insufficient to achieve the desired segmentation results. While the focus here was on the craniofacial system, BounTI can be used to automatically segment any hard tissue. The tool presented here is available as an Avizo/Amira add‐on, a stand‐alone Windows executable, and a Python library. We believe this accessible and user‐friendly segmentation tool can benefit the wider anatomical community. [ABSTRACT FROM AUTHOR]

Referring image segmentation aims to accurately align image pixels and text features for object segmentation based on natural language descriptions. This paper proposes NSNPRIS (convolutional nonlinear spiking neural P systems for referring image segmentation), a novel model based on convolutional nonlinear spiking neural P systems. NSNPRIS features NSNPFusion and Language Gate modules to enhance feature interaction during encoding, along with an NSNPDecoder for feature alignment and decoding. Experimental results on RefCOCO, RefCOCO + , and G-Ref datasets demonstrate that NSNPRIS performs better than mainstream methods. Our contributions include advances in the alignment of pixel and textual features and the improvement of segmentation accuracy. [ABSTRACT FROM AUTHOR]

In recent years, chitosan oligosaccharide (COS) has demonstrated potential applications in enhancing the protective immune function of sea cucumber Apostichopus japonicus (Selenka, 1867), owing to its superior biological properties. The fluorescent probe labeling technique is simple to operate and highly sensitive, enabling the visualization of polysaccharides. This study utilized the fluorescent probe fluorescein isothiocyanate (FITC) to label COS. A fluorescence imaging system was used to track the distribution of the FITC-tagged COS absorbed by A. japonicus. FITC-COS was prepared and administered to A. japonicus along with a basal diet supplemented with 0.5% FITC-COS. Subsequently, samples were collected at different time points to analyze the differential absorption and distribution of COS in the coelomocytes and the tissues of A. japonicus, including intestinal contents, intestine, respiratory tree, longitudinal muscle, and body wall tissues. Quantitative fluorescence detection with a microplate reader and fluorescence microscopy were used for analysis. The results revealed that COS is widely distributed in the various tissues of A. japonicus. Fluorescence peaks were observed at 9.5 h in the coelomocytes, respiratory tree, longitudinal muscle, and body wall tissues. Additionally, at 12 h, fluorescence peaks were observed in the intestinal contents and intestine tissue. At the same time, the distribution of COS in the intestinal contents decreased significantly at day 3 compared to the peak value. [ABSTRACT FROM AUTHOR]

This paper presents an enhanced real-time vehicle detection system using convolutional neural networks (CNNs) for both daytime and night-time conditions. Initially, the system determines the time of capture by analyzing the upper part of input images. For daytime detection, it uses normalized cross-correlation and two-dimensional discrete wavelet transform (2DDWT) techniques. Night-time detection involves identifying vehicle lamps through color thresholding and connected component techniques, followed by symmetry analysis and CNN classification. The dataset for training includes images from the Caltech Cars, AOLP, KITTI Vision, and nighttime vehicle detection datasets, ensuring robust performance across various lighting conditions. Experiments demonstrate the system's high accuracy, achieving 99.2% during the day and 98.27% at night, meeting real-time requirements and enhancing driving assistance systems' reliability. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Engineering Design & Technology (IJEDT) is the property of Burdur Mehmet Akif Ersoy University, Faculty of Architecture & Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: Advancing the development of 7 T MRI for spinal cord imaging is crucial for the enhanced diagnosis and monitoring of various neurodegenerative diseases and traumas. However, a significant challenge at this field strength is the transmit field inhomogeneity. Such inhomogeneity is particularly problematic for imaging the small, deep anatomical structures of the cervical spinal cord, as it can cause uneven signal intensity and elevate the local specific absorption ratio, compromising image quality. This multisite study explores several RF shimming techniques in the cervical spinal cord. Methods: Data were collected from 5 participants between two 7 T sites with a custom 8Tx/20Rx parallel transmission coil. We explored two radiofrequency (RF) shimming approaches from an MRI vendor and four from an open‐source toolbox, showcasing their ability to enhance transmit field and signal homogeneity along the cervical spinal cord. Results: The circularly polarized (CP), coefficient of variation (CoV), and specific absorption rate (SAR) efficiency shim modes showed the highest B1+ efficiency, and the vendor‐based "patient" and "volume" modes showed the lowest B1+ efficiency. The coefficient of variation method produced the highest CSF/spinal cord contrast on T2*‐weighted scans (ratio of 1.27 ± 0.03), and the lowest variation of that contrast along the superior–inferior axis. Conclusion: The study's findings highlight the potential of RF shimming to advance 7 T MRI's clinical utility for central nervous system imaging by enabling more homogenous and efficient spinal cord imaging. Additionally, the research incorporates a reproducible Jupyter Notebook, enhancing the study's transparency and facilitating peer verification. [ABSTRACT FROM AUTHOR]

At present, there are diverse data sources and mature technologies for the 3D modeling of cities, and 3D modeling is also developing towards component-level and refined modeling. In this study, we combined the needs of refined management in the central area of Beijing and elaborated on the practice of using various point cloud collection devices to carry out multisource point cloud data collection and ultrafine 3D modeling in areas where flight operations are not possible. There are three main areas of innovation: (1) the development of full-element ground acquisition and multisource point cloud joint 3D modeling technology, which solves the technical problems of acquiring all types of ground scanning data, such as those obtained using static, station-, cart-, backpack-, and vehicle-mounted laser scanning instruments, and handheld scanners, as well as multisource point cloud data fusion and modeling; (2) the development of a vehicle-mounted ultrahigh-resolution image acquisition system, which improves the efficiency of the vehicle scanner by increasing the exposure frequency to enhance its travel speed and (3) the use of tools to automatically extract urban components based on point clouds, which improved the efficiency of 3D modeling and achieved the fine-grained modeling of massive components in the region. On the basis of the system platform, the management and implementation of urban component-level information can be achieved. [ABSTRACT FROM AUTHOR]

Spectral imaging technology has gained widespread application across diverse fields due to its ability to capture spatial and spectral information simultaneously. However, conventional spectral scanning methods using single‐peak tunable filters face the challenges of low optical throughput. Inspired by Fellgett's advantage in Fourier‐transform infrared spectroscopy, this paper proposes a tunable filter with multiple resonances to improve optical throughput. It is composed of a simple Fabry‐Pérot cavity filled with liquid crystal. An artificial neural network is employed to match with the filter for spectrum reconstruction. Experimental results show a spectral resolution of 10 nm and a switching time of ≈23 ms between adjacent states. As a demonstration, biological specimens are spectrally imaged under different light conditions with good fidelity. The results suggest that the filter possesses over six times higher optical throughput than a commercial liquid crystal tunable filter (LCTF), leading to better spectrum accuracy for spectral imaging under low‐light conditions. The compact and cost‐effective design of this tunable filter enables seamless integration into imaging systems, presenting promising prospects for practical applications such as portable health management and food inspection in low‐light conditions. [ABSTRACT FROM AUTHOR]

With the wide application of unmanned aerial vehicles (UAVs), UAV thermal imaging technology has been brought to the attention of numerous experts and scholars. At present, the application of UAV thermal imaging technology is limited by the problem of low resolution of the images captured by the thermal imaging system itself. In order to solve this problem, this paper proposes a UAV thermal image super-resolution reconstruction method based on bimodal image feature fusion, which improves the UAV thermal image super-resolution characterization capability by making use of the feature information provided by the UAV visible image to compensate for the lack of texture and edge information perception in thermal images. The proposed methodology consists of two tasks: The first task involves a bimodal image feature fusion network that extracts texture and edge information from visible images. This addresses the lack of detailed feature information in thermal images with a differential complementary modal feature fusion module, ultimately producing a fused image with high-quality feature information. The second task entails the integration of a gated multilevel feature fusion module into the UAV thermal image reconstruction network as an attention mechanism, thereby improving the use of fused image feature information for generating high-resolution UAV thermal images. Finally, a joint adaptive training strategy is utilized to further enhance the super-resolution effect of UAV thermal images. The experimental results show that the proposed method can effectively extract the visible image information to compensate for the characterization information in the UAV thermal image super-resolution, and achieves the optimal values of 31.53 dB and 0.9116 in PSNR and SSIM metrics, respectively, providing a new solution for the thermal image super-resolution of UAVs. [ABSTRACT FROM AUTHOR]

This letter presents a model based on the Cramér–Rao bound theory for calculating the theoretical lower limit of imaging resolution of microwave computational imaging aided by re‐configurable metasurface. The model takes into account two crucial parameters: the signal‐to‐noise ratio and the number of measurements in the imaging process. By simultaneously considering the signal‐to‐noise ratio and the number of measurements, as well as other parameters, the model allows for a comprehensive assessment of the imaging resolution of the imaging system. In addition, both position estimation and intensity estimation are considered, and a new composite statistical resolution concept is proposed, which is more suitable for microwave computational imaging. [ABSTRACT FROM AUTHOR]

Characterizing soil moisture around drip irrigation pipes is crucial for precise and optimized farming. Machine learning (ML) approaches are particularly suitable for this task as they can reduce uncertainties caused by soil conditions and the drip pipe positions, using features extracted from relevant datasets. This letter addresses local moisture detection in the vicinity of dripping pipes using a portable microwave imaging system. The employed ML approach is fed with two dimensional images generated using back projection as a radar‐based algorithm and the Born approximation as an inverse scattering method, based on spatio‐temporal (collected data at various positions over the soil surface and at different time points.) measurements at various frequencies. The study investigates the performance of K‐nearest neighbour (KNN) and convolutional neural networks (CNN) algorithms for moisture classification based on these imaging techniques. We also explore the potential of KNN and CNN for moisture estimation around the plant roots and in the presence of pebbles. In general, CNN outperforms KNN in moisture content detection from microwave data, especially after applying imaging algorithms. A combination of CNN as the ML approach and the back projection algorithm to provide training data, yielded 20%$20\%$ accuracy more than other models for moisture content estimation. Also, the practical results demonstrate that our method can detect soil moisture with an estimation error of less than 10%. [ABSTRACT FROM AUTHOR]

Cone beam CT is integral to most modern radiotherapy treatments. The application of daily and repeat CBCT imaging can lead to high imaging doses over a large volume of tissue that extends beyond the treatment site. Hence, it is important to ensure exposures are optimised to keep doses as low as reasonably achievable, whilst ensuring images are suitable for the clinical task. This IPEM topical report presents the results of the first UK survey of dose indices in radiotherapy CBCT. Dose measurements, as defined by the cone beam dose index (CBDIw), were collected along with protocol information for seven treatment sites. Where a range of optimised protocols were available in a centre, a sample of patient data demonstrating the variation in protocol use were requested. Protocol CBDIw values were determined from the average dosimetry data for each type of linear accelerator, and median CBDIw and scan length were calculated for each treatment site at each centre. Median CBDIw values were compared and summary statistics derived that enable the setting of national dose reference levels (DRLs). A total of 63 UK radiotherapy centres contributed data. The proposed CBDIw DRLs are; prostate 20.6 mGy, gynaecological 20.8 mGy, breast 5.0 mGy, 3D-lung 6.0 mGy, 4D-lung 11.8 mGy, brain 3.5 mGy and head/neck 4.2 mGy. However, large differences between models of imaging system were noted. Where centres had pro-active optimisation strategies in place, such as sized based protocols with selection criteria, dose reductions on the 'average' patient were possible compared with vendor defaults. Optimisation of scan length was noted in some clinical sites, with Elekta users tending to fit different collimators for prostate imaging (relatively short) compared with gynaecological treatments (longest). This contrasts with most Varian users who apply the default scan length in most cases. [ABSTRACT FROM AUTHOR]

Interest in virus-based therapeutics for the treatment of genetic and oncolytic diseases has created a demand for high-yield, low-cost virus-manufacturing processes. However, traditional analytical methods of assessing infectious virus titer require multiple processing steps and manual counting, limiting sample throughput, and increasing human error. This bottleneck severely limits the development of new manufacturing unit operations to drive down costs. In this work, we utilize an Incucyte Live-Cell Analysis System to develop a high-throughput infectious titer assay for adenovirus expressing a GFP-transgene. Although previous studies have demonstrated live-cell imaging's potential for use with other viruses, they provide little guidance regarding the selection of the viewing and analysis parameters. To fill this gap, we develop an algorithmic approach to identify the optimum viewing and analysis parameters and create a statistical workflow for quantifying infectious adenovirus in a sample dilution series in a standard 24-well microplate. The developed assay is comparable to Hexon staining, the gold-standard for adenovirus infectious titer, with a Pearson correlation coefficient of 0.9. Finally, the developed algorithmic approach and statistical workflow were applied to create an assay for adenovirus titer using a 96-well microplate, allowing five times more samples to be quantified compared to the standard 24-well plate. While this assay uses a GFP-insert that precludes its use in a clinical environment, the key learnings surrounding the careful use of viewing and analysis parameters, and the statistical workflow are widely applicable to implementing life-cell imaging for dilution-series-based assays. Moreover, this method directly enables the fast and accurate evaluation of virus samples in a preclinical environment. • A preclinical live-cell imaging assay for infectious titer was validated against gold standard methods. • Titer estimates are significantly impacted by viewing and imaging analysis parameters. • Scale-up an automated workflow technique from 24-well microplates to 96-well microplates. [ABSTRACT FROM AUTHOR]

High transparency Ce3+-doped dense gadolinium aluminum borosilicate (GS x) glass scintillator was synthesized in reducing atmosphere by melt-quenching process. The transmittance of the glasses exceeds 80%, and the light attenuation length is between 5-20 cm within the range of 400–600 nm. GS x glass shows an photoluminescence (PL) in range of 350–550 nm, with the strongest emission peak around 420 nm. The PL decay time of GS x glasses is around 37 ns, with a maximum PL quantum yield of 44.05%. The integrated X-ray excited luminescence (XEL) intensity of GS S glass is 52.5% compared with BGO crystal. For X-ray imaging, GS L glass based imaging system shows a high spatial resolution of 9 lp/mm, which is comparable to CsI:Tl X-ray detectors. Under γ-ray, the highest light yield of GS S glass is 1235 ph/MeV with an energy resolution of 24.0% at 662 keV, close to that of BSO crystal. The scintillation decay time of GS x glasses is consisted of fast (∼100 ns) and slow (∼560 ns) components. In thermally stimulated luminescence (TSL), GS S glass exhibits a strong TSL peak at approximately 440 K, with low intensity shoulders at about 520 K, implying different types of defects and traps. Therefore, GS x glass has promising prospects for X-ray imaging and high-energy physics applications. [ABSTRACT FROM AUTHOR]

Urbanization and changes in land use are crucial for national development. Managing and monitoring these changes have become easier and more accurate due to advancements in imaging systems and change detection methods. In recent years, convolutional neural networks have gained significant attention and advancement in remote sensing change detection research. However, designing a model that reduces computational complexity while improving detection accuracy is still under discussion. In this regard, an efficient model based on pseudo-siamese convolutional networks had been developed in this research, which by combining multi-scale features at different levels, increased the accuracy of the final change map, especially in images with both different spatial resolutions and scales. Our model is inspired by Scale Invariant Feature Transform (SIFT), which uses multiple scale-spaces to identify scale-independent keypoints in images. We designed our model based on this concept, in which After passing the bi-temporal images through the encoder network and making the initial feature maps, the feature learning process takes place at several levels of different scales and finally, the change map is calculated. In addition, the proposed architecture leverages point-wise, depth-wise, and neighboring features across various scale levels to harness the optimal features present in high-resolution remote sensing images. Simplicity in design, effectiveness in calculations, and accuracy make our model surpass popular methods such as BIT, DSIFN, etc. The evaluation results on 4 different datasets and comparison with 12 state-of-the-art models demonstrate that the proposed method outperforms other popular models in change detection. Available at: https://github.com/farhadinima75/OctaveNet. [ABSTRACT FROM AUTHOR]

Cone beam computed tomography (CBCT) has potential advantages for developing portable, cost-effective point-of-care CT systems for intracranial imaging, such as early stroke diagnosis, hemorrhage detection, and intraoperative navigation. However, large volume imaging with flat panel detector based CBCT significantly increases the scattered radiation fluence which reduces its image quality and utility. To address these issues, a compact CBCT concept with enhanced image quality was investigated for intracranial imaging. The new system features a novel antiscatter collimator and data correction method to address the challenges in imaging large volumes with CBCT. A benchtop CBCT prototype was constructed. Imaging studies with anthropomorphic phantoms showed that soft tissue visualization, Hounsfield Unit (HU) accuracy, contrast, and spatial resolution increased significantly with the proposed CBCT concept, and they were comparable to the values measured in the gold standard multidetector-row CT (MDCT) images. Contrast-to-noise ratio (CNR) in CBCT images was within 12–31% of the CNR in MDCT images. These findings indicate that a compact CBCT system integrated with effective scatter suppression techniques may have increased utility in the context of brain imaging, and the proposed approach may enable the development of point-of-care CT systems for head imaging based on flat panel detector based CBCT technology. [ABSTRACT FROM AUTHOR]

Breast cancer is the most common malignancy in women globally, stemming from gene mutations that prompt irregular cellular growth and subsequent tumor development. Early-stage detection of cancer cells results in a remarkable 99% survival rate. This research presents a microwave imaging technique for the non-invasive identification of tumors in the initial stages within the women's breast. A low-cost antenna array with an Artificial Magnetic Conductor (AMC) is proposed, featuring a compact structure size of 37.2 × 37.2 mm 2 . The AMC, a metamaterial, acts as a reflective surface to enhance frequency selectivity, specifically at 8.48 GHz. The maximum gains reached 9.35 dBi in simulated results and 10.51 dBi in measured results. The fabricated antenna validates the simulated findings, and its operational efficiency has undergone experimental validation. Moreover, fidelity factors in face-to-face (FtF) and side-by-side (SbS) scenarios are delineated. The antenna, operating as a transceiver, is applied to a modeled breast phantom across five distinct cases for numerical simulations pertaining to cancer cell detection applications. The outcomes of this research bear considerable implications for advancing early-stage breast cancer detection methodologies. [ABSTRACT FROM AUTHOR]

N6-methyladenosine (m6A)-modified mRNAs and their cytoplasmic reader YTHDFs are colocalized with stress granules (SGs) under stress conditions, but the interplay between m6A modification and SG stability remains unclear. Here, we presented a spatiotemporal m6A imaging system (SMIS) that can monitor the m6A modification and the translation of mRNAs with high specificity and sensitivity in a single live cell. SMIS showed that m6A-modified reporter mRNAs dynamically enriched into SGs under arsenite stress and gradually partitioned into the cytosol as SG disassembled. SMIS revealed that knockdown of YTHDF2 contributed to SG disassembly, resulting in the fast redistribution of mRNAs from SGs and rapid recovery of stalled translation. The mechanism is that YTHDF2 can regulate SG stability through the interaction with G3BP1 in m6A-modified RNA-dependent manner. Our results suggest a mechanism for the interplay between m6A modification and SG through YTHDF2 regulation. [ABSTRACT FROM AUTHOR]

A comprehensive strategy to enhance the polarization performance of mid‐wave infrared photodetectors (PDs) is developed and implemented by integrating wire‐grid polarizers (WGPs) using nanoimprint lithography and femtosecond laser (FSL) polishing. This combined approach offers significant advantages, including large‐area fabrication capabilities, practical device integration, and improved polarization characteristics. By addressing optical losses, the primary factor contributing to polarization degradation through the thermal effects of FSL polishing, substantial improvements are achieved in surface roughness and grain boundary reduction on the WGP, resulting in remarkable performance enhancements. As a result, the extinction ratio of the integrated WGP InAs/GaSb type‐II superlattice PD achieves an impressive value of up to 1044. This approach holds promising potential for advancing polarization‐based imaging and measurement systems to new heights. [ABSTRACT FROM AUTHOR]

Background: Near-infrared fluorescence (NIRF) imaging is an excellent choice for image-guided surgery due to its simple operation and non-invasiveness. Developing tumor-specific fluorescent molecular probes is key to fluorescence imaging-guided surgery. EGFR (epidermal growth factor receptor) is closely related to the proliferation and growth of tumor cells and is highly expressed in epithelial ovarian cancer (EOC). The study aims to construct a NIR fluorescent molecular probe using cetuximab (an EGFR monoclonal antibody) and investigate its feasibility for targeting EOC in vivo through fluorescence imaging. Methods: We determined the expression of EGFR in EOC. NIR fluorescent molecular probe with cetuximab (cetuximab-Cy7) was chemically engineered and identified. The subcutaneous xenografted tumor model of EOC was induced using SKOV3-Luc cell line with positive expression of EGFR. Cetuximab-Cy7 was used for in vivo fluorescence imaging, and phosphate-buffered saline, free Cy7 dye and mouse isotype immunoglobulin G-Cy7 were used as controls. NIRF imaging system was performed to study the distribution and targeting of the probes. Tumors were imaged in situ and ex vivo, and fluorescent intensity was quantified. Resected specimens were analyzed to confirm diagnosis, and immunohistochemical (IHC) staining was used to identify EGFR expression. Results: EGFR expression was increased in EOC tissues than fallopian tube tissues. The high expression of EGFR was significantly correlated with well-differentiation, residual lesions ≤ 1 cm, no recurrence and increased survival. NIRF imaging showed that the cetuximab-Cy7 enabled detection of tumor lesions in EOC-bearing mice with the optimal dose of 30 µg. The suitable imaging time window may be 24–96 h post-injection. Ex vivo fluorescence imaging indicated that fluorescent signal was mainly detected in the tumor and the lung. IHC results confirmed that xenografts were EGFR positive. Conclusion: Cetuximab-Cy7 can specifically target the tumors of EOC xenografted nude mice. This research lays the foundation for future studies on EOC surgery navigation. [ABSTRACT FROM AUTHOR]

DNBSEQ employs a patterned array to facilitate massively parallel sequencing of DNA nanoballs (DNBs), leading to a considerable boost in throughput. By employing the ultra-high-density (UHD) array with an increased density of DNB binding sites, the throughput of DNBSEQ can be further expanded. However, the typical imaging system of the DNBSEQ sequencer is unable to resolve adjacent DNBs spaced smaller than the resolution limit, resulting in poor base-calling performance of the UHD array and hindering its practical application. In this study, we propose a deep-learning-based DNB image super-resolution network named DNBSRN to address this problem. DNBSRN has a specifically designed structure for DNB images and employs a histogram-matching-based preprocessing approach. For the eight DNB image datasets generated from the DNBSEQ sequencer using UHD arrays with 360 nm pitch, the base-calling performances are significantly improved after super-resolution reconstruction by DNBSRN and reached a comparable level to those of the regular density array. In terms of reconstruction speed, DNBSRN takes only 7.61 ms for an input image with 500 × 500 pixels, which minimizes its influence on throughput. Furthermore, compared with state-of-the-art super-resolution networks, DNBSRN demonstrates superior performance in terms of both the quality and speed of DNB image reconstruction. DNBSRN successfully addresses the DNB image super-resolution task. Integrating DNBSRN into the image analysis workflow of DNBSEQ will allow for the application of UHD array, hence enabling a considerable improvement in throughput as well as tremendous savings in unit reagent cost. [ABSTRACT FROM AUTHOR]

Study Design: Preclinical experimental study. Objective: To develop an intraoperative ultrasound-assisted imaging device, which could be placed at the surgical site through an endoscopic working channel and which could help surgeons recognition of different tissue types during endoscopic spinal surgery (ESS). Summary of Background Data: ESS remains a challenging task for spinal surgeons. Great prof iciency and experience are needed to perform procedures such as intervertebral discectomy and neural decompression within a narrow channel. The limited surgical view poses a risk of damaging important structures, such as nerve roots. Methods: We constructed a spinal endoscopic ultrasound system, using a 4-mm custom ultrasound probe, which can be easily inserted through the ESS working channel, allowing up to 10 mm depth detection. This system was applied to ovine lumbar spine samples to obtain ultrasound images. Subsequently, we proposed a 2-stage classification algorithm, based on a pretrained DenseNet architecture for automated tissue recognition. The recognition algorithm was evaluated for accuracy and consistency. Results: The probe can be easily used in the ESS working channel and produces clear and characteristic ultrasound images. We collected 367 images for training and testing of the recognition algorithm, including images of the spinal cord, nucleus pulposus, adipose tissue, bone, annulus fibrosis, and nerve roots. The algorithm achieved over 90% accuracy in recognizing all types of tissues with a Kappa value of 0.875. The recognition times were under 0.1 s using the current configuration. Conclusion: Our system was able to be used in existing ESS working channels and identify at-risk spinal structures in vitro. The trained algorithms could identify 6 intraspinal tissue types accurately and quickly. The concept and innovative application of intraoperative ultrasound in ESS may shorten the learning curve of ESS and improve surgical efficiency and safety. [ABSTRACT FROM AUTHOR]

Cervical pedicle screws (CPS) provide biomechanically superior fixation compared to other techniques but are technically more demanding. Navigated CPS placement has been increasingly reported as a safe and accurate technique, yet there are few studies comparing different combinations of imaging and navigation systems under comparable conditions. With this study, we aimed to compare different imaging and navigation systems for CPS placement in terms of accuracy, screw placement time and applied radiation dose. For this experimental study, navigated CPS placement was performed at levels C2 to C7 in 24 identical radiopaque artificial spine models by two surgeons with different levels of experience using three different combinations of intraoperative 3D imaging devices and navigation systems. Accuracy, time and radiation dose were compared between the groups. In total, 288 screws were placed. Accuracy was > 98% in all groups with no significant differences between groups or between surgeons (P = 0.30 and P = 0.31, respectively), but the inexperienced surgeon required significantly more time (P < 0.001). Radiation dose was significantly higher with iCT compared to CBCT (P < 0.0001). Under experimental conditions, accuracy rates of > 98% were achieved for navigated CPS placement regardless of the imaging modality or navigation system used. Radiation doses were significantly lower for CBCT compared to iCT guidance. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to evaluate the inter-fraction reproducibility and intra-fraction stability of breast radiotherapy using voluntary deep-inspiration breath hold (DIBH) and free breathing (FB) based on an optical surface imaging system (OSIS). Methods: Seventeen patients (510 breath-hold sessions) treated using a field-in-field (FiF) technique and twenty patients (600 breath-free sessions) treated with a volume-modulated arc therapy (VMAT) technique were included in this retrospective study. All the patients were positioned with the guidance of CBCT and OSIS, and also monitored with OSIS throughout the whole treatment session. Eight setup variations in three directions were extracted from the treatment reports of OSIS for all sessions and were subsequently manually introduced to treatment plans, resulting in a total of 296 perturbed plans. All perturbed plans were recalculated, and the dose volume histograms (DVH) for the target and organs at risk (OAR) were analyzed. Results: The OSIS and CBCT for both DIBH and FB treatments showed a good agreement of less than 0.30 cm in each direction. The intra-fraction respiratory motion data during DIBH were −0.06 ± 0.07 cm, 0.12 ± 0.15 cm, and 0.12 ± 0.12 cm in the lateral, longitudinal, and vertical directions, respectively; for FB, the respiratory motion data were −0.02 ± 0.12 cm, 0.08 ± 0.18 cm, and 0.14 ± 0.20 cm, respectively. For the target, DIBH plans were more sensitive to setup errors; the mean deviations in D95 for CTV were 39.78 Gy–40.17 Gy for DIBH and 38.46 Gy–40.52 Gy for FB, respectively. For the OARs, the mean deviations of V10, V20, and Dmean to the heart; V5, V20, and Dmean to the ipsilateral lung; and Dmean to the breast were lower for the FB plan compared with the DIBH plan. Conclusion: Based on OSIS, our results indicate that both DIBH and FB can provide good reproducibility in the inter-fractions and stability in the intra-fractions. When the patient respiratory motion is large, the FB technology has greater possibility for the undercoverage of the target volume, while DIBH technology is more likely to result in increases in dose to OARs (the lung, heart, and contralateral breast). [ABSTRACT FROM AUTHOR]

ABSTRACT Background and Aims Methods Results Conclusions Ultrasound imaging and shear wave elastography (SWE) can be used to noninvasively stage hepatopathologies and are widespread in clinical practice. These techniques have recently been adapted for small animal use in a novel 3D in vivo imaging system capable of high‐throughput automated scanning. Our goal was to evaluate the feasibility of using this imaging tool in the murine Western diet (WD) model, a highly translatable preclinical model of obesity, metabolic disease and liver fibrosis.Female C57BL/6 mice (N = 48) were placed on WD or chow diet and imaged longitudinally for a period of 48 weeks. Imaging consisted of 3D B‐mode and targeted SWE captures. Liver volume, liver echogenicity and liver stiffness were quantified from in vivo imaging data. A subset of mice was sacrificed at various timepoints (0, 12, 24 and 48 weeks) for histological workup. Correlation analysis was performed between in vivo imaging and histological measurements to determine level of agreement.Noninvasive imaging showed statistically significant increases in liver volume and echogenicity, but non‐significant increase in liver stiffness in the WD‐fed cohort, suggesting development of hepatomegaly and steatosis, but negligible fibrosis. Ex vivo analysis confirmed significant increases in liver weight, liver triglycerides and ALT, but limited increases in fibrosis corroborating noninvasive imaging results. Correlation analysis between imaging and histology demonstrated good agreement between liver volume/liver weight (R2 = 0.85) and echogenicity/triglycerides (R2 = 0.76).This study demonstrated that noninvasive ultrasound liver assessments are feasible in the WD mouse model and closely reflect the underlying pathological state of the animal. Automated ultrasound can serve as a high‐throughput noninvasive screening method for preclinical liver disease research and drug development. [ABSTRACT FROM AUTHOR]

The transition from optical film to digital image sensors (DIS) in imaging systems has brought great convenience to human life. However, the sampling resolution of DIS is considerably lower than that of optical film due to the limitation that the pixels are significantly larger than the silver halide molecules. How to break DIS's sampling limit and achieve high‐resolution imaging is highly desired for imaging applications. In the research, a novel mechanism that allows for a significant reduction in the smallest sampling unit of DIS to as small as 1/16th of a pixel, or even smaller, through measuring the intra‐pixel quantum efficiency for the first time and recomputing the image — a technique referred to as hyper‐sampling imaging (HSI) is developed. Employing the HSI method, the physical sampling resolution of regular DIS can be enhanced by 4 × 4 times or potentially higher, and detailed object information can be acquired. The HSI method has undergone rigorous testing in real‐world imaging scenarios, demonstrating its robustness and efficiency in overcoming the sampling constraints of conventional DIS. This advancement is particularly beneficial for applications such as remote sensing, long‐range reconnaissance, and astronomical observations, where the ability to capture fine details is paramount. [ABSTRACT FROM AUTHOR]

Summary: This paper develops a new image encryption algorithm based on a novel two‐dimensional chaotic map and bit‐level dynamic scrambling. First, multiple one‐dimensional chaotic maps are coupled to construct a novel two dimensions Logistic‐Sine‐coupling chaos map (2D‐LSCCM). The performance analysis shows that the 2D‐LSCCM has more complex chaotic characteristics and wider chaotic range than many extant 2D chaos maps. Second, original image matrix combines with hash algorithm SHA‐256 to generate a hash value. The initial values of 2D‐LSCCM are generated based on the hash value. Third, the original image matrix is divided into multiple sub‐matrices by wavelet transform, followed by scrambling by an improved Knuth shuffle algorithm. Fourth, the scrambled multiple sub‐matrices are stitched into an image matrix of M×N×3$$ M\times N\times 3 $$ and converted into a binary matrix. The chaotic sequence generated by 2D‐LSCCM is introduced as a control sequence to control the bit‐level scrambling of pixel points, which realizes the bit‐level dynamic scrambling. Finally, the diffusion operation is performed by parameter par and chaotic sequence to obtain the final encrypted image. The algorithm security analysis and simulation examples demonstrate the effectiveness of the proposed encryption scheme. [ABSTRACT FROM AUTHOR]

Post-drilling wireline acoustic single-well imaging technology can now detect geological structures tens of meters away from boreholes. Further development of this single-well imaging technology in the logging-while-drilling (LWD) environment will have significant values in real-time applications such as geosteering and reservoir navigation. Based on the wireline imaging application, we propose a new method for the LWD application. In wireline imaging, the four-component (4C) dipole acoustic data are azimuthally rotated to scan the reflectors around the borehole. In LWD, azimuthal scanning is achieved by drilling rotation such that the 4C dipole system in the wireline is replaced by a one-dipole-source and two-receiver LWD system, where the two receivers are mounted on opposite sides of the drill collar. For the LWD application, we first developed the theory for LWD dipole shear-wave reflection imaging and validated the theory using 3D finite-difference waveform modeling. Using the analytical solution, we analyzed the far-field radiation directivity of an acoustic LWD dipole source and the effect of drilling rotation on the shear-wave reflection imaging using the LWD acoustic system. The LWD analysis results show that, for fast formations, the SH-wave is the dominant component for imaging, whereas for slow formations, the P-wave becomes important and can be used for imaging. Our results also indicate that the reflection data acquired by the system are affected by the speed of drilling rotation. The take-off azimuth at the wave radiation may be different from the incident azimuth at the wave reception. Knowing the rotation speed, this azimuth difference can be corrected. A further advantage of using the oppositely mounted receivers is that the reflected wave arrives earlier (later) at the front (back)-side receiver; thus, the arrival time difference between the receivers can be used to eliminate the 180°-azimuth ambiguity of dipole acoustic imaging. For reflection imaging, using the proposed LWD system configuration, we tested its azimuth sensitivity and validated its 180°-ambiguity solution using synthetic LWD and field wireline dipole data. The results of this work, therefore, provide a theoretical foundation for the development of the LWD acoustic reflection imaging system. [ABSTRACT FROM AUTHOR]

Imaging systems are inherently prone to aberrations. We present an optimization method to design two-dimensional (2D) freeform reflectors that minimize aberrations for various parallel ray beams incident on the optical system. We iteratively design reflectors using inverse methods from non-imaging optics and optimize them to obtain a system that produces minimal aberrations. This is done by minimizing a merit function that quantifies aberrations and is dependent on the energy distributions at the source and target of an optical system, which are input parameters essential for inverse freeform design. The proposed method is tested for two configurations: a single-reflector system and a double-reflector system. Classical designs consisting of aspheric elements are well-known for their ability to minimize aberrations. We compare the performance of our freeform optical elements with classical designs. The optimized freeform designs outperform the classical designs in both configurations. [ABSTRACT FROM AUTHOR]

Introduction: Although drought resistance of a plant may be achieved through morphological, structural, physiological, cellular, and molecular adaptations, most studies remain limited to quantifying the effect of drought on biomass. Methods: Using a highthroughput phenotypic imaging system, we evaluated the drought resistance of 151 bean accessions (Phaseolinae; Fabaceae) in an explorative approach, by quantifying five different traits simultaneously: biomass, water use efficiency (WUE), relative water content (RWC), chlorophyll content (NDVI), and root/shoot ratio. Since crop wild relatives are important resources for breeding programs, we analyzed both wild and cultivated accessions, most of which have never been evaluated for drought resistance before. Results: We demonstrate that the five traits are affected very differently by drought in the studied accessions, with significant correlations existing only between the biomass and WUE indicators (r=0.39), and between the RWC and NDVI indicators (r=0.40). When grouping accessions by subgenus or by species, large intraspecific and withinsubgenus variation was found. For this reason, we performed a cluster analysis, which grouped the accessions into five distinct clusters with similar response profiles. We also correlated the drought resistance for each accession to local climate variables at their original collection sites. The biomass, WUE, and RWC indicators were significantly correlated to annual precipitation (r=0.40, r=0.20, r=0.22, respectively), confirming that accessions from arid environments are generally more drought resistant. Discussion: Our results demonstrate that the drought resistance of Phaseolinae beans is a multifaceted characteristic and cannot be simply quantified through biomass. Furthermore, the broader knowledge of the drought resistance of the accessions studied here may prove an invaluable resource for future crop production. [ABSTRACT FROM AUTHOR]

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. Researchers created a metalens focusing all visible light equally across 400–700 nm by engineering meta-atoms with dispersion-matched layers. This improves group delay control, enabling efficient, large-aperture achromatic metalenses for practical use. [ABSTRACT FROM AUTHOR]

A non‐linear epipole‐featured model was developed and evaluated for structure computation in active convergent stereovision. In this paper, using the developed analytic model, the terms virtual depth and depth factor are introduced, which together define the depth of a world point relative to the coordinate frame of the reference camera. Furthermore, an equivalence relation between coplanar–parallel and convergent stereo‐camera imaging systems was established, in which baseline‐to‐depth‐factor ratio is introduced and termed as convergent stereo disparity. This convergent stereo disparity can be equated with the image rectification process in a practical conventional coplanar–parallel stereo‐camera setup. Subsequently, generalised mathematical analyses were done to model and study the variation of the depth sensitivity coefficient and relative depth uncertainty with respect to convergent stereovision system parameters using the developed model. It was observed that different values of the left and right focal lengths are required to achieve a high sensitivity coefficient, a condition that is not conformable with the conventional practice of having the same left and right focal lengths in stereo‐camera imaging. Regarding the variation of the stereo projection and stereo convergence angles, there are trade‐offs between the sensitivity coefficient and relative depth uncertainty. Finally, it was found that a stereo convergence angle of 90° yields the best relative depth uncertainty value at which the focal length–normalized epipole‐to‐principal point distances on both image planes are reciprocals. [ABSTRACT FROM AUTHOR]

Sperm quality analysis plays an important role in diagnosing infertility, which is widely implemented by computer‐assisted sperm analysis (CASA) of sperm‐swimming imaging from commercial phase‐contrast microscopy. A well‐equipped microscope comes with a high cost, increasing the burden of assessment, and it also occupies a large volume. For point‐of‐care testing (POCT) of sperm quality, these factors are confronted with the challenges of low‐cost and portable instruments. In this study, an encoded light‐emitting diode (LED) array illumination is employed to achieve a portable microscope with multicontrast imaging for sperm quality analysis. This microscopy has dimensions of 16.5 × 14.0 × 25.0 cm, and its dark‐field (DF) imaging provides high‐contrast sperm image data which is suitable for CASA. According to DF imaging, we developed a software of LabCASA, which can used to assess the motility characteristics of sperm. Compared with TrackMate, the difference in motility parameters from our software was less than 10% in the coefficient of variation (CV). The sperm motility parameters vary with the chamber temperature, which further confirms the reliability of our system with DF imaging. The DF imaging provides strong robustness for tracking sperm's motion under different microscopes. For assessment of the motility parameters, our system can work at a lower cost with a plastic structure. This system with DF imaging is suitable for portable POCT of sperm quality analysis, which is highly cost‐effective in resource‐constrained circumstances. [ABSTRACT FROM AUTHOR]

A non‐destructive method for determining the color value of pelletized red peppers is crucial for pepper processing factories. This study aimed to investigate the potentiality of visible and fluorescence images for the determination of color value of pelletized red pepper. The imaging problem, caused by the cylindrical shape and irregular cross‐sectional features of the pelletized red peppers, was reduced through the extraction of an approximate plane region. To integrate the information in the visible and fluorescence images, a baseline convolutional neural network (CNN) architecture was designed and low level, middle level, and high level fusion models (denoted as LL‐CNN, ML‐CNN, and HL‐CNN, respectively) were developed upon the baseline CNN. The effects of input image size and color space were examined. According to the training result, CNN fusion models were developed using visible image in L*a*b* color space and fluorescence image in RGB color space using 56 × 56 input image size. Among the three types of CNN fusion models, the HL‐CNN obtained the best performance, resulting in Rv2 of 0.828 and RMSEV of 0.351. This study suggests that the fusion of visible and fluorescence image through CNN is a practical approach to save testing time and replace traditional destructive method. The low cost and compact structure of the imaging systems can maintain the commercial appeal of pepper industry. [ABSTRACT FROM AUTHOR]

The residue number system is widely used in cryptography, digital signal processing, image processing systems, and other areas where high-performance computing is required. One of the main tools used in the residue number system is the Akushsky core function. However, its use is limited due to the existence of so-called critical cores. This study aims to develop Akushsky core functions that effectively eliminate the occurrence of critical cores, thereby enhancing their applicability in real-world scenarios. We introduce a fundamental approach to critical core detection that reduces the average time for critical core detection by 99.48% compared to the brute force algorithm. The results of our analysis indicate not only a substantial improvement in the speed of core detection but also an enhancement in the overall performance of systems utilizing the Akushsky core function. Our findings provide important insights into optimizing residue number systems and encourage further exploration into advanced computational techniques within this domain. [ABSTRACT FROM AUTHOR]

Current super-resolution algorithms exhibit limitations when processing noisy remote sensing images rich in surface information, as they tend to amplify noise during the recovery of high-frequency signals. To mitigate this issue, this paper presents a novel approach that incorporates the concept of compressed sensing and explores the super-resolution problem of remote sensing images for space cameras, particularly for high-speed imaging systems. The proposed algorithm employs K-singular value decomposition (K-SVD) to jointly train high- and low-resolution image blocks, updating them column by column to obtain overcomplete dictionary pairs. This approach compensates for the deficiency of fixed dictionaries in the original algorithm. In the process of dictionary updating, we innovatively integrate the circle chaotic mapping into the solution process of the dictionary sequence, replacing pseudorandom numbers. This integration facilitates balanced traversal and simplifies the search for global optimal solutions. For the optimization problem of sparse coefficients, we utilize the orthogonal matching pursuit method (OMP) instead of the L1 norm convex optimization method used in most reconstruction techniques, thereby complementing the K-SVD dictionary update algorithm. After upscaling and denoising the image using the dictionary pair mapping relationship, we further emphasize image edge details with local gradients as constraints. When compared with various representative super-resolution algorithms, our algorithm effectively filters out noise and stains in low-resolution images. It not only performs well visually but also stands out in objective evaluation indicators such as the peak signal-to-noise ratio and information entropy. The experimental results validate the effectiveness of the proposed method in super-resolution remote sensing images, yielding high-quality remote sensing image data. [ABSTRACT FROM AUTHOR]

Diffractive multispectral optical imaging plays an essential role in optical sensing, which typically suffers from the image blurring problem caused by the spatially variant point spread function. Here, we propose a novel high-quality and efficient hybrid space calibrated 3D network "HSC3D" for spatially variant diffractive multispectral imaging that utilizes the 3D U-Net structure combined with space calibration modules of magnification and rotation effects to achieve high-accuracy eight-channel multispectral restoration. The algorithm combines the advantages of the space calibrated module and U-Net architecture with 3D convolutional layers to improve the image quality of diffractive multispectral imaging without the requirements of complex equipment modifications and large amounts of data. A diffractive multispectral imaging system is established by designing and manufacturing one diffractive lens and four refractive lenses, whose monochromatic aberration is carefully corrected to improve imaging quality. The mean peak signal-to-noise ratio and mean structural similarity index of the reconstructed multispectral images are improved by 3.33 dB and 0.08, respectively, presenting obviously improved image quality compared with a typical Unrolled Network algorithm. The new algorithm with high space calibrated ability and imaging quality has great application potential in diffraction lens spectroscopy and paves a new method for complex practical diffractive multispectral image sensing. [ABSTRACT FROM AUTHOR]

Recent advancements in quantum and quantum-inspired imaging techniques have enabled high-resolution 3D imaging through photon correlations. These techniques exhibit reduced degradation of image resolution for out-of-focus samples compared to conventional methods (i.e., intensity-based incoherent imaging). A key advantage of these correlation-based approaches is their independence from the system numerical aperture (NA). Interestingly, both improved resolution of defocused images and NA-independent scaling are linked to the spatial coherence of light. This suggests that while correlation measurements exploit spatial coherence, they are not essential for achieving this imaging advantage. This discovery has led to the development of optical systems that achieve similar performance by using spatially coherent illumination and relying on intensity measurements: direct 3D imaging with NA-independent resolution was recently demonstrated in a correlation-free setup using LED light. Here, we explore the physics behind the enhanced performance of defocused coherent imaging, showing that it arises from the modification of the sample's spatial harmonic content due to diffraction, unlike the blurring seen in conventional imaging. The results we present are crucial for understanding the implications of the physical differences between coherent and incoherent imaging, and are expected to pave the way for the practical application of the discovered phenomena. [ABSTRACT FROM AUTHOR]

Background: High hypericin-loaded polyvinylpyrrolidone (HHL-PVP) constitutes a novel approach to utilize the promising characteristics of hypericin for photodynamic diagnosis (PDD) and therapy (PDT) of brain tumors in an orally bioavailable formulation. The aim of this study was to investigate the ability of a Complementary Metal-Oxide-Semiconductor (CMOS) camera-based fluorescence imaging system to selectively visualize HHL-PVP in glioblastoma tissue even in the presence of 5-Aminolvevulinic acid (5-ALA) induced fluorescence, which is widely utilized in brain tumor surgery. Methods: We applied a previously established system with a non-hypericin specific filter for 5-ALA fluorescence visualization and a newly introduced hypericin-specific filter at 575–615 nm that transmits the spectrum of hypericin, but not 5-ALA fluorescence. Glioblastoma specimens obtained from 12 patients (11 with preoperative 5-ALA intake) were ex vivo incubated with HHL-PVP. Subsequently, fluorescence intensity and lifetime changes using both the non-hypericin specific filter and hypericin-specific filter were measured before and after HHL-PVP incubation and after subsequent rinsing. Results: While no significant differences in fluorescence signal were observed using the non-hypericin specific filter, statistically significant increases in fluorescence intensity (p = 0.001) and lifetime (p = 0.028) after HHL-PVP incubation were demonstrated using the hypericin-specific filter. In consequence, specimens treated with HHL-PVP could be identified according to the fluorescence signal with high diagnostic sensitivity (87.5%) and specificity (100%). Conclusions: Our CMOS camera-based system with a hypericin-specific filter is capable of selectively visualizing hypericin fluorescence in glioblastoma tissue after ex vivo HHL-PVP incubation. In the future, this technique could facilitate clinical investigations of HHL-PVP for PDD and PDT while maintaining the current standard of care with 5-ALA guidance. [ABSTRACT FROM AUTHOR]

Background/Objectives: This study investigated whether decompression surgery for lumbar spinal stenosis can effectively improve sagittal alignment in elderly patients. With the growing focus on sagittal balance in spinal surgery, this study aimed to evaluate post-decompression alignment changes and identify the factors influencing these changes using the EOS imaging system. Methods: A retrospective analysis was conducted on 49 elderly patients who underwent decompression surgery alone for lumbar spinal stenosis. Radiologic parameters, measured using the EOS system, and clinical outcomes were assessed preoperatively, at two weeks postoperatively, and at one year postoperatively. Patients were grouped based on the improvement of the sagittal vertical axis (SVA) by 25 mm or more. A multivariate analysis was performed to identify factors affecting sagittal alignment changes. Results: Significant sagittal alignment improvements were observed postoperatively, including a notable increase in thoracic kyphosis and a decrease in SVA observed at one year. Clinical outcomes, such as the Oswestry disability index (ODI) and EQ-5D, significantly improved at both two weeks and one year postoperatively compared to preoperative values (all p-values < 0.05). Multivariate analysis revealed that greater preoperative SVA and higher ODI were significant predictors of sagittal alignment changes (odds ratio [OR] for SVA = 1.014, OR for ODI = 1.034). Conclusions: Decompression surgery for lumbar spinal stenosis in elderly patients can result in significant improvements in sagittal alignment and clinical outcomes. The study suggests that decompression alone is a viable surgical option for elderly patients, particularly those with a greater preoperative sagittal imbalance and disability, even in the absence of major deformities. [ABSTRACT FROM AUTHOR]

The waveguide chip with a phase measurement function has garnered significant attention in the field of imaging optics, emerging as a crucial component in optical interferometric imaging systems. Enhancing the working bandwidth of these waveguide chips is essential for improving the imaging quality of interferometric systems. However, most existing designs primarily focus on narrow bands, with no reported research on broadband designs. This paper introduces a novel broadband waveguide chip design that incorporates a phase measurement function. We explore the fundamental structure and working principle of this innovative design. Fabricated on a silicon substrate, the chip features a silicon dioxide cladding layer and a germanium-doped silicon dioxide core layer, strategically optimized for performance. Utilizing the Beam Propagation Method (BPM), we conduct detailed simulations to determine the optimal device parameters. The simulation results demonstrate the effectiveness of our design, showing a phase measurement deviation of approximately 5° at a center wavelength of 1550 nm across a 300 nm wavelength range. The loss of the device is approximately 0.8 dB. These findings provide a solid foundation for future experimental implementations and fabrications, offering both a theoretical framework and technical reference for advancing the practical use of broadband waveguide chips with phase measurement functions in optical interferometric imaging. [ABSTRACT FROM AUTHOR]

This paper presents a novel deoxyribonucleic acid (DNA) network encryption method combined with a hyperchaotic system for image encryption. The DNA network encryption algorithm contains 12 different DNA operations, which overcomes the limitations of existing DNA encryption schemes that rely on limited fixed DNA operation types. Firstly, secure hash algorithm 256-bit (SHA-256) is employed to generate secret keys associated with the plaintext. Secondly, the hyperchaotic system is employed to derive key streams with high plaintext sensitivity. Thirdly, the DNA encoding, DNA network encryption and decoding are performed sequentially to encrypt the plaintext image using the keystreams. Finally, an additional row–column disruption is executed to strengthen the resistance of the encryption scheme to attacks. Security analysis reveals that the information entropy of our cipher image reaches 7.9975 and the average pixel correlation coefficients are below 0.01, implying that our encryption algorithm has excellent security. [ABSTRACT FROM AUTHOR]

Manipulable lenses with tunable focal length have garnered great attention due to their unique advantages including strong adjustability, low power consumption, fast response times, and ease of integration. However, in the context of advancing intellectualization, weight reduction, and miniaturization of imaging systems, mere focal length adjustment is no longer sufficient. Herein, an innovative method is introduced for creating a dynamic light manipulable (DLM) lens with surface reshaping. This breakthrough is accomplished through the collaborative integration of a medium lens with an actuator based on photo‐active liquid crystal polymer (LCP) film, where the deformation of the photo‐active LCP film can be arbitrarily controlled by meticulously designing the orientation distributions of liquid crystal (LC) mesogens within the film. The lens with three distinct surfaces, including quasi‐cone, paraboloid, and 3 × 3 paraboloid array, are demonstrated with infrared stability, robust fatigue resistance, long natural recovery time, outstanding thermal stability, and excellent solvent resistance, further confirming the feasibility of the lens application in tunable display, imaging, infrared optics, and other systems. This work holds the promise of paving the way for a new generation of imaging systems. [ABSTRACT FROM AUTHOR]

We present a dense and metric 3D mapping pipeline designed for embedded operation on-board UAVs, by loosely coupling deep neural networks trained to infer dense depth single images with a SLAM system that restores metric scale from sparse depth. In contrast to computationally restrictive approaches that leverage multiple views, we propose a highly efficient, single-view approach without sacrificing 3D mapping performance. This enables real-time construction of a global 3D voxel map by iterative fusion of the rescaled dense depth maps obtained via raycasting from the estimated camera poses. Quantitative and qualitative experimentations of our framework in challenging environmental conditions show comparable or superior performance with respect to state-of-the-art approaches via a better effectiveness-efficiency trade-off. [ABSTRACT FROM AUTHOR]

The aim of this guideline is to provide recommendations for the implementation of an effective and efficient quality control (QC) programme for SPECT and PET systems in a preclinical imaging lab. These recommendations aim to strengthen the translational power of preclinical imaging results obtained using preclinical SPECT and PET. As for clinical imaging, reliability, reproducibility, and repeatability are essential when groups of animals are used in a longitudinal imaging experiment. The larger the variability of the imaging endpoint, the more animals are needed to be able to observe statistically significant differences between groups. Therefore, preclinical imaging requires quality control procedures to maintain reliability, reproducibility, and repeatability of imaging procedures, and to ensure the accuracy and precision of SPECT and PET quantification. While the Physics Committee of the European Association of Nuclear Medicine (EANM) has already published excellent procedure guidelines for Routine Quality Control Recommendations for Nuclear Medicine Instrumentation that also includes procedures for small animal PET systems, and important steps have already been made concerning preclinical quality control aspects, this new guideline provides a review and update of these previous guidelines such that guidelines are also adapted to new technological developments. [ABSTRACT FROM AUTHOR]