Imaging Detection Research Articles

Deep learning-based spectroscopic single-molecule localization microscopy.

Spectroscopic single-molecule localization microscopy (sSMLM) takes advantage of nanoscopy and spectroscopy, enabling sub-10nm resolution as well as simultaneous multicolor imaging of multi-labeled samples. Reconstruction of raw sSMLM data using deep learning is a promising approach for visualizing the subcellular structures at the nanoscale. Develop a novel computational approach leveraging deep learning to reconstruct both label-free and fluorescence-labeled sSMLM imaging data. We developed a two-network-model based deep learning algorithm, termed DsSMLM, to reconstruct sSMLM data. The effectiveness of DsSMLM was assessed by conducting imaging experiments on diverse samples, including label-free single-stranded DNA (ssDNA) fiber, fluorescence-labeled histone markers on COS-7 and U2OS cells, and simultaneous multicolor imaging of synthetic DNA origami nanoruler. For label-free imaging, a spatial resolution of 6.22nm was achieved on ssDNA fiber; for fluorescence-labeled imaging, DsSMLM revealed the distribution of chromatin-rich and chromatin-poor regions defined by histone markers on the cell nucleus and also offered simultaneous multicolor imaging of nanoruler samples, distinguishing two dyes labeled in three emitting points with a separation distance of 40nm. With DsSMLM, we observed enhanced spectral profiles with 8.8% higher localization detection for single-color imaging and up to 5.05% higher localization detection for simultaneous two-color imaging. We demonstrate the feasibility of deep learning-based reconstruction for sSMLM imaging applicable to label-free and fluorescence-labeled sSMLM imaging data. We anticipate our technique will be a valuable tool for high-quality super-resolution imaging for a deeper understanding of DNA molecules' photophysics and will facilitate the investigation of multiple nanoscopic cellular structures and their interactions.

Giant Optical Nonlinear Response up to 60th‐Order Induced by the Ytterbium Energy Relay Mediated Photon Avalanches

AbstractThe exploration of photon avalanching (PA) luminescence in different lanthanide emitters has profound implications in plentiful frontier applications. However, studies for universal mechanisms at the nanoscale to exhibit giant nonlinear responses in various avalanching emitters are limited. Here, record‐breaking nonlinear responses up to 60th‐order in high‐lying emitting levels of various emitters at room temperature are generated by proposing a universal mechanism named energy relay‐mediated photon avalanche (enrePA). By harnessing energy from the avalanching nano‐engine through the energy relay of Yb3+ ions, the brightness of blue emissions from Tm3+ and Ho3+ can be enhanced with giant optical nonlinearity up to 60th and 38th order, respectively. Further incorporating gadolinium‐based systems, more emitters (Tb3+, Eu3+, Dy3+, Nd3+) can be activated with extreme optical nonlinearities up to 48th‐order. By expanding PA into the full‐spectrum range, the enrePA mechanism opens exciting avenues for flexible and high‐efficiency PA modulation in multilayer nanostructures, enabling the applicability of PA in more technologies such as super‐resolution imaging, lithography, and optical detection.

Synergistically enhanced water-resistive perovskite nanocrystals for cell nucleus imaging and acid phosphatase detection

Despite the impressive success of metal halide perovskite nanocrystals (NCs) in diverse fields, such as solar cells, light-emitting diodes, photodetectors, and so forth, their intrinsic sensitivity to water limits the extending applications in aqueous systems. Herein, enhanced water stability is achieved for NH2-PEG-COOH capped Mn2+-doped CsPbCl3/CsPb2Cl5 core/shell NCs through the synergistic effect of ion doping, core/shell construction and polymer encapsulation. The NH2-PEG-COOH encapsulation is validated crucial for the formation of CsPbCl3/CsPb2Cl5 core/shell structure, since it retains contact for CsPbCl3 NCs with trace water, and guarantees a slow water-etching process, triggering the CsPbCl3 to CsPb2Cl5 transformation. Thanks to the high formation energy induced by Mn2+ doping, CsPbCl3/CsPb2Cl5 hetero core/shell construction and NH2-PEG-COOH protective barrier, the obtained NCs exhibit unattenuated photoluminescence quantum yield after dipped in water for 15 days. Based on the robust water stability, the NH2-PEG-COOH capped Mn2+-doped CsPbCl3/CsPb2Cl5 NCs work well in cell nucleus imaging and acid phosphatase detection. This work not only produces a novel perovskite fluorescence probe in aqueous systems but also provides a general and effective combination strategy to enhance the water stability of perovskite materials.

Fibronectin-Targeting Dual-Modal MR/NIRF Imaging Contrast Agents for Diagnosis of Gastric Cancer and Peritoneal Metastasis.

The prevalence and fatality rates of gastric cancer (GC) remain elevated, with advanced stages presenting a grim prognosis. Noninvasive diagnosis of GC cancer often proves challenging until the disease has progressed to an advanced stage or metastasized. Initially, the level of fibronectin (FN) in cancer-associated fibroblasts (CAFs) of GC was at least 3.7 times higher than that in normal fibroblasts. Herein, two FN-targeting magnetic resonance/near-infrared fluorescence (MR/NIRF) imaging contrast agents were developed to detect GC and peritoneal metastasis noninvasively. The probes CREKA-Cy7-(Gd-DOTA) and CREKA-Cy7-(Gd-DOTA)3 demonstrated significant FN-targeting capability (with dissociation constants of 1.0 and 2.1 mM) and effective MR imaging performance (with proton relaxivity values of 9.66 and 27.44 mM-1 s-1 at 9.4 T, 37 °C). In vivo imaging revealed a high signal-to-noise ratio and successful visualization of GC metastasis using NIRF imaging as well as successful tumor detection in MR imaging. Therefore, this study highlights the potential of FN-targeting probes for GC diagnosis and aids in the advancement of new diagnostic strategies for the clinical detection of GC.

Mn(II)-Based Metal Halide with Near-Unity Quantum Yield for White LEDs and High-Resolution X-ray Imaging.

Metal halides have drawn great interest as luminescent materials and scintillators due to their outstanding optical properties. Exploring new types of phosphors with easy production processes, excellent photophysical properties, high light yields, and environmentally friendly compositions is crucial and quite challenging. Herein, a novel Mn(II)-based metal halide (4-BTP)2MnBr4 was produced using a facile solvent evaporation method, which exhibited a strong green emission peaking at 524 nm from the d-d transition of tetrahedral-coordinated Mn2+ ion and a near-unity quantum yield. The prepared white light-emitting diode device has a wide color gamut of 100.7% NTSC with CIE chromaticity coordinates of (0.32, 0.32). In addition, (4-BTP)2MnBr4 demonstrates excellent characteristics in X-ray scintillation, including a high light yield of 98 000 photons/MeV, a sensitive detection limit of 37.4 nGy/s, excellent resistance to radiation damage, and successful demonstration of X-ray imaging with high resolution at 21.3 lp/mm, revealing the potential for application in diagnostic X-ray medical imaging and industry radiation detection.

Spontaneous-stimulated Raman co-localization dual-modal analysis approach for efficient identification of tumor cells

The study of highly heterogeneous tumor cells, especially acute myeloid leukemia (AML) cells, usually relies on invasive analytical methods such as morphology, immunology, cytogenetics, and molecular biology classification, which are complex and time-consuming to perform. Mortality is high if patients are not diagnosed in a timely manner, so rapid label-free analysis of gene expression and metabolites within single-cell substructures is extremely important for clinical diagnosis and treatment. As a label-free and non-destructive vibrational detection technique, spontaneous Raman scattering provides molecular information across the full spectrum of the cell but lacks rapid imaging localization capabilities. In contrast, stimulated Raman scattering (SRS) provides a high-speed, high-resolution imaging view that can offer real-time subcellular localization assistance for spontaneous Raman spectroscopic detection. In this paper, we combined multi-color SRS microscopy with spontaneous Raman to develop a co-localized Raman imaging and spectral detection system (CRIS) for high-speed chemical imaging and quantitative spectral analysis of subcellular structures. Combined with multivariate statistical analysis methods, CRIS efficiently differentiated AML from normal leukocytes with an accuracy of 98.1 % and revealed the differences in the composition of nuclei and cytoplasm of AML relative to normal leukocytes. Compared to conventional Raman spectroscopy blind sampling without imaging localization, CRIS increased the efficiency of single-cell detection by at least three times. In addition, using the same approach for further identification of AML subtypes M2 and M3, we demonstrated that intracytoplasmic differential expression of proteins is a marker for their rapid and accurate classifying. CRIS analysis methods are expected to pave the way for clinical translation of rapid tumor cell identification.

Electrochemiluminescence in Graphitic Carbon Nitride Decorated with Silver Nanoparticles for Dopamine Determination Using Machine Learning.

Electrochemiluminescence (ECL) luminophores with wavelength-tunable multicolor emissions are essential for multicolor ECL imaging detection and multiplexed analysis. In this work, silver nanoparticle (Ag NP)-decorated graphitic carbon nitride (g-CN@Ag) nanocomposites were synthesized. The morphology, chemical composition, structure, and ECL property of g-CN@Ag were investigated. The prepared g-CN, g-CN@Ag1, g-CN@Ag5, and g-CN@Ag10 can produce blue, blue-green, chartreuse, and yellow colored ECL emissions, respectively, by using K2S2O8 as the coreagent. The ECL emission wavelength of g-CN@Ag can be regulated from 460 to 565 nm by modulating the content of the immobilized Ag NPs. Then, a multicolor ECL detection array was fabricated by using g-CN, g-CN@Ag1, g-CN@Ag5, and g-CN@Ag10 as four ECL luminophores. Dopamine was detected based on its inhibition effect on the multicolor ECL emissions. The linear range is from 0.1 nM to 1 mM with the lowest detection limit of 44 pM. Then, machine learning-assisted multiparameter concentration prediction of dopamine was further carried out by combining the deep neural network (DNN) algorithm. This work provides a new avenue to regulate the ECL emission wavelength of g-CN by using the metal nanoparticle modification strategy and presents an effective machine learning-assisted multicolor ECL detection strategy for accurate multiparameter quantitative detection.

Emerging Technologies for Remote Sensing of Floating and Submerged Plastic Litter

Most advances in the remote sensing of floating marine plastic litter have been made using passive remote-sensing techniques in the visible (VIS) to short-wave-infrared (SWIR) parts of the electromagnetic spectrum based on the spectral absorption features of plastic surfaces. In this paper, we present developments of new and emerging remote-sensing technologies of marine plastic litter such as passive techniques: fluid lensing, multi-angle polarimetry, and thermal infrared sensing (TIS); and active techniques: light detection and ranging (LiDAR), multispectral imaging detection and active reflectance (MiDAR), and radio detection and ranging (RADAR). Our review of the detection capabilities and limitations of the different sensing technologies shows that each has their own weaknesses and strengths, and that there is not one single sensing technique that applies to all kinds of marine litter under every different condition in the aquatic environment. Rather, we should focus on the synergy between different technologies to detect marine plastic litter and potentially the use of proxies to estimate its presence. Therefore, in addition to further developing remote-sensing techniques, more research is needed in the composition of marine litter and the relationships between marine plastic litter and their proxies. In this paper, we propose a common vocabulary to help the community to translate concepts among different disciplines and techniques.

Imaging performance prediction of a hypersonic target in a geosynchronous orbit based on multi-dimensional information correlation of radiation, multi-spectral, and polarization

Hypersonic target detection based on infrared intensity characteristics is easily disturbed by sea surface and cloud flares when detected by space-based optical systems, which results in a low detection rate, high false alarm, and difficulty in stable detection. This paper explores a method to improve target detection performance based on the correlation of infrared radiation, multi-spectral and polarization. Firstly, the comprehensive factors that influence complex ambient illumination, atmospheric transmission, and clutter background on spectral-polarization characteristics of hypersonic targets are analyzed. Based on the global radiation scattering theory, the temperature distribution model of the hypersonic target is established by using FLUENT. The polarization emission and pBRDF model of the target is established, and the radiation polarization transfer model is generated. Secondly, the sea surface temperature distribution is obtained by inversion of Landsat8 remote sensing data. The radiation polarization transfer model of the sea surface is established based on the Cox-Munk model combined with pBRDF and the polarization emission model. Thirdly, the polarization scattering effect of atmospheric particles on the upward radiation of the interaction of the target with the sunlight is considered comprehensively, and the 6SV radiative transfer model is used to calculate the polarization effect of atmospheric particles on the upward radiation transmission of the target and the background. Then, combined with the point diffusion of the optical system and the photoelectric conversion of the detector, the multi-dimensional full-chain imaging prediction model of the hypersonic target-sea background-ambient atmosphere-optical system-detector is established. The imaging characteristics and detection performance of the target in different imaging dimensions are simulated and analyzed with the signal-to-clutter ratio (SCR). The research shows that in the direction of reflected sunlight from the sea surface, the sea surface glare is suppressed and the target is highlighted through a target detection method of multi-dimensional information. This method has better detection results than the infrared multi-spectral detection method.

Optimizing scan time and bayesian penalized likelihood reconstruction algorithm in copper-64 PET/CT imaging: a phantom study

Objectives: The aim of this study was to evaluate Cu-64 PET phantom image quality using Bayesian Penalized Likelihood (BPL) and Ordered Subset Expectation Maximum with point-spread function modeling (OSEM-PSF) reconstruction algorithms. In the BPL, the regularization parameter β was varied to identify the optimum value for image quality. In the OSEM-PSF, the effect of acquisition time was evaluated to assess the feasibility of shortened scan duration. Methods: A NEMA IEC PET body phantom was filled with known activities of water soluble Cu-64. The phantom was imaged on a PET/CT scanner and was reconstructed using BPL and OSEM-PSF algorithms. For the BPL reconstruction, various β values (150, 250, 350, 450, and 550) were evaluated. For the OSEM-PSF algorithm, reconstructions were performed using list-mode data intervals ranging from 7.5 to 240 s. Image quality was assessed by evaluating the signal to noise ratio (SNR), contrast to noise ratio (CNR), and background variability (BV). Results: The SNR and CNR were higher in images reconstructed with BPL compared to OSEM-PSF. Both the SNR and CNR increased with increasing β, peaking at β = 550. The CNR for all β, sphere sizes and tumor-to-background ratios (TBRs) satisfied the Rose criterion for image detectability (CNR > 5). BPL reconstructed images with β = 550 demonstrated the highest improvement in image quality. For OSEM-PSF reconstructed images with list-mode data duration ≥ 120 s, the noise level and CNR were not significantly different from the baseline 240 s list-mode data duration. Conclusions: BPL reconstruction improved Cu-64 PET phantom image quality by increasing SNR and CNR relative to OSEM-PSF reconstruction. Additionally, this study demonstrated scan time can be reduced from 240 to 120 s when using OSEM-PSF reconstruction while maintaining similar image quality. This study provides baseline data that may guide future studies aimed to improve clinical Cu-64 imaging.

Research on internal quality testing method of dry longan based on terahertz imaging detection technology

Research on internal quality testing method of dry longan based on terahertz imaging detection technology

Ultra-broadband metamaterial absorber for collecting electromagnetic waves from the ultraviolet to the mid-infrared

Due to the inherent local limitations of surface plasmon resonance, it is challenging to design an ultra-broadband electromagnetic wave absorber covering multiple bands. In this paper, a novel Ultra-broadband perfect absorber is designed, which is a simple-type structure composed of metal-dielectric-metal (MDM), and the resonant cavity formed by metal and dielectric realizes high absorption. The calculated results of the absorber show that the average absorption rate in the range of 200–8000 nm is 95.70%, and the absorption bandwidth over 90% is 7800 nm. The ultra-broadband absorption can be attributed to the coupling effect of multiple resonances inside and outside the resonant cavity, which gives our proposed structure a wider bandwidth and higher light absorption capacity than the previously reported work and further reduces the complexity of the structure. When it is replaced by other materials, the structure also maintains high absorbency. In addition, the structure is not only polarization insensitive but also has wide-angle absorption, and it maintains high absorption in transverse magnetic (TM) mode and transverse electric (TE) mode. Even when the incident angle increases to 58°, the average absorption rate is still above 80%. It also has the characteristics of large process tolerance and strong adaptability to changing environments. These results allow the absorber to be applied in solar energy collection, infrared imaging and thermal detection, and optoelectronic devices.

Artificial Compound Eye with Tunable Properties for Enhancement in Fluorescence Imaging and Raman Detection

AbstractArtificial compound eye (CE) draws inspiration from nature, offering advanced imaging capabilities and an expansive field of view. In this work, an innovative technique is developed for the creation of CE with tunable dimensions. A solution‐based process is employed that involves in situ polymerization of surface nanodroplets prior to soft lithography to produce CE consisting of millions of ommatidia. The fabricated CE comprised of a densely arranged array of microwells, each with a base radius of 5 µm. Situated on a millimeter‐sized spherical dome, the CE can be tailored to arbitrary dimensions, enhancing its adaptability with a wide angular field of view up to 118°. The CE is used to enhance signal detection in fluorescent compounds, reaching a detection limit of 107 times lower concentration than that without using CEs in bulk solution. The signal enhancement capabilities are further utilized for surface‐enhanced Raman spectroscopy by using a portable handheld device, with an enhancement factor of 2. The fabrication technique underscores the advantages of the approach in simplicity, reproducibility, and efficiency in creating CE. The potential applications of CE may be extended to various domains, such as optical sensing, light‐dependent signal enhancement, motion perception, and medical endoscopy.

Towards a unified approach for unsupervised brain MRI Motion Artefact Detection with few shot Anomaly Detection

Automated Motion Artefact Detection (MAD) in Magnetic Resonance Imaging (MRI) is a field of study that aims to automatically flag motion artefacts in order to prevent the requirement for a repeat scan. In this paper, we identify and tackle the three current challenges in the field of automated MAD; (1) reliance on fully-supervised training, meaning they require specific examples of Motion Artefacts (MA), (2) inconsistent use of benchmark datasets across different works and use of private datasets for testing and training of newly proposed MAD techniques and (3) a lack of sufficiently large datasets for MRI MAD. To address these challenges, we demonstrate how MAs can be identified by formulating the problem as an unsupervised Anomaly Detection (AD) task. We compare the performance of three State-of-the-Art AD algorithms DeepSVDD, Interpolated Gaussian Descriptor and FewSOME on two open-source Brain MRI datasets on the task of MAD and MA severity classification, with FewSOME achieving a MAD AUC >90% on both datasets and a Spearman Rank Correlation Coefficient of 0.8 on the task of MA severity classification. These models are trained in the few shot setting, meaning large Brain MRI datasets are not required to build robust MAD algorithms. This work also sets a standard protocol for testing MAD algorithms on open-source benchmark datasets. In addition to addressing these challenges, we demonstrate how our proposed ‘anomaly-aware’ scoring function improves FewSOME’s MAD performance in the setting where one and two shots of the anomalous class are available for training. Code available at https://github.com/niamhbelton/Unsupervised-Brain-MRI-Motion-Artefact-Detection/.

Self-assembled nanobody-DNA nanomachine conjugate for parathyroid glands intraoperative imaging and parathyroid hormone detection

During thyroidectomy, inadvertent parathyroid damage commonly occurs owing to the varying location of the parathyroid glands and their similarity to adipose tissue, lymph nodes, and thyroid nodules. Therefore, in this study, we aimed to develop a novel self-assembled nanobody-DNA nanomachine conjugate probe (PTH-Nb-HCR) for precisely identifying the parathyroid glands and monitoring parathyroid hormone (PTH) levels in patient blood. The probe consists of two components: the first component is the nanobody (Nb)-nucleic acid conjugate PTH-Nb-Trigger, acting as a recognition unit for PTH and capable of initiating the hybridization chain reaction (HCR), whereas the second component comprises the HCR hairpin probes HP1-FAM (with a modified FAM signal molecule at the 5′ end) and HP2. The assembly of the PTH-Nb-HCR fluorescent probe takes only 20 min. Using the probe, the serum PTH level was quantitatively detected in the 0.1–1000 pg/mL range, and the parathyroid glands were rapidly distinguished from adipose, lymphoid, and thyroid tissues within 5 min. We also used this probe for imaging parathyroid glands with hyperparathyroidism. Considering the strong correlation between fluorescence intensity and preoperative serum PTH levels in patients, we anticipate the use of this probe to distinguish normal and hyperfunctional tissues during hyperparathyroidism surgery.

Defect identification method for capacitive-inductive dual-mode sensors with lift-off interference

The capacitive-inductive dual-mode probe provides a defect detection solution that allows simultaneous or sequential eddy current detection and capacitive imaging detection, known as capacitive-inductive dual-mode detection. The dual-mode detection combines the advantages of the two detection techniques and can be used to detect multiple types of defects in “insulator-conductor” multilayer hybrid structures. However, its high sensitivity poses a challenge as the probe's responses a susceptible to interference from lift-off variations, leading to non-relevant indications in the results that is not indicative of defects or anomalies. In this study, the Lift-off Characteristics (LoC) of the hybrid structures are studied, and an effective method for identifying relevant indications such as defect and anomaly for dual-mode sensor response signals with lift-off interference by combining the testing data of both detection modes and the LoC curve is proposed. A series of validation experiments were conducted to demonstrate the effectiveness of the proposed method in defect identification and classification. The results confirmed that the method effectively identified defect from lift-off interference and significantly reduced the occurrence of false judgments, thereby enhancing the reliability of the dual mode detection. Furthermore, the feasibility of utilizing the method to identify relevant indication when the sensor is tilted was also demonstrated.

Metal Halide Perovskites for Direct X‐Ray Detection in Medical Imaging: To Higher Performance

AbstractThis review discusses the utilization of metal halide perovskite (MHP) materials in X‐ray direct imaging detectors for medical application. Compared to traditional semiconductors, MHP has multiple advantages including high linear attenuation coefficient, low exciton binding energy, long carrier diffusion length, and high carrier mobility. These ascendancies allow for low‐dose and high‐resolution medical imaging. Apartment from a historical overview of the develop‐X‐ray direct imaging detectors and their performance indicators, this review specifically focuses on the development and innovation of X‐ray direct imaging detectors using MHP materials. The article concludes by identifying existing challenges and discussing the potential application of perovskite X‐ray direct imaging detectors in the medical industry.

Recent Progress in the Application of Tau Protein Biosensors for Diagnosis of Neurodegenerative Diseases.

The microtubule-associated Tau protein is found in the central nervous system (CNS) in six major isoforms. Neurodegenerative diseases have been linked to post-translational changes of Tau, most notably phosphorylation. Tau protein's molecular diversity is highly helpful in the identification of neurodegenerative illnesses. Nonetheless, one major obstacle to the early detection of brain illness is the nanoscale identification of tau proteins. The standard methods for identifying tau protein include western blotting, polymerase chain reaction (PCR), and real-time PCR. Enzyme-linked immunosorbent assay (ELISA) is another approach used. The limited sensitivity and specificity of these detections, together with the need for sophisticated equipment, are some of their drawbacks. The development of innovative and complex methods for tau protein screening is necessary to address the aforementioned issues. Biosensors are a cutting-edge instrument that may help identify various neurodegenerative biomarkers as early as feasible. This paper provides an overview of the most recent developments in the detection of neurodegenerative diseases employing biosensors built on nanotechnology and methods for imaging, electrochemical, and optical detection of the Tau protein. Furthermore, we outline the present difficulties and suggest a possible course for biosensor-based detection and intervention in the future.

Correlation of TRPA1 RNAscope and Agonist Responses.

The TRPA1 ion channel is a sensitive detector of reactive chemicals, found primarily on sensory neurons. The phenotype exhibited by mice lacking TRPA1 suggests its potential as a target for pharmacological intervention. Antibody-based detection for distribution analysis is a standard technique. In the case of TRPA1, however, there is no antibody with a plausible validation in knockout animals or functional studies, but many that have failed in this regard. To this end we employed the single molecule in situ hybridization technique RNAscope on sensory neurons immediately after detection of calcium responses to the TRPA1 agonist allyl isothiocyanate. There is a clearly positive correlation between TRPA1 calcium imaging and RNAscope detection (R = 0.43), although less than what might have been expected. Thus, the technique of choice should be carefully considered to suit the research question. The marginal correlation between TRPV1 RNAscope and the specific agonist capsaicin indicates that such validation is advisable for every RNAscope target. Given the recent description of a long-awaited TRPA1 reporter mouse, TRPA1 RNAscope detection might still have its use cases, for detection of RNA at particular sites, for example, defined structurally or by other molecular markers.

Debut of a responsive chemiluminescent probe for butyrylcholinesterase: Application in biological imaging and pesticide residue detection

Debut of a responsive chemiluminescent probe for butyrylcholinesterase: Application in biological imaging and pesticide residue detection