Spectral Imaging Research Articles

Construction of a predictive model for gastric cancer neuroaggression and clinical validation analysis: A single-center retrospective study.

This study investigated the construction and clinical validation of a predictive model for neuroaggression in patients with gastric cancer. Gastric cancer is one of the most common malignant tumors in the world, and neuroinvasion is the key factor affecting the prognosis of patients. However, there is a lack of systematic analysis on the construction and clinical application of its prediction model. This study adopted a single-center retrospective study method, collected a large amount of clinical data, and applied statistics and machine learning technology to build and verify an effective prediction model for neuroaggression, with a view to providing scientific basis for clinical treatment decisions and improving the treatment effect and survival rate of patients with gastric cancer. To investigate the value of a model based on clinical data, spectral computed tomography (CT) parameters and image omics characteristics for the preoperative prediction of nerve invasion in patients with gastric cancer. A retrospective analysis was performed on 80 gastric cancer patients who underwent preoperative energy spectrum CT at our hospital between January 2022 and August 2023, these patients were divided into a positive group and a negative group according to their pathological results. Clinicopathological data were collected, the energy spectrum parameters of primary gastric cancer lesions were measured, and single factor analysis was performed. A total of 214 image omics features were extracted from two-phase mixed energy images, and the features were screened by single factor analysis and a support vector machine. The variables with statistically significant differences were included in logistic regression analysis to construct a prediction model, and the performance of the model was evaluated using the subject working characteristic curve. There were statistically significant differences in sex, carbohydrate antigen 199 expression, tumor thickness, Lauren classification and Borrmann classification between the two groups (all P < 0.05). Among the energy spectrum parameters, there were statistically significant differences in the single energy values (CT60-CT110 keV) at the arterial stage between the two groups (all P < 0.05) and statistically significant differences in CT values, iodide group values, standardized iodide group values and single energy values except CT80 keV at the portal vein stage between the two groups (all P < 0.05). The support vector machine model with the largest area under the curve was selected by image omics analysis, and its area under the curve, sensitivity, specificity, accuracy, P value and parameters were 0.843, 0.923, 0.714, 0.925, < 0.001, and c:g 2.64:10.56, respectively. Finally, based on the logistic regression algorithm, a clinical model, an energy spectrum CT model, an imaging model, a clinical + energy spectrum model, a clinical + imaging model, an energy spectrum + imaging model, and a clinical + energy spectrum + imaging model were established, among which the clinical + energy spectrum + imaging model had the best efficacy in diagnosing gastric cancer nerve invasion. The area under the curve, optimal threshold, Youden index, sensitivity and specificity were 0.927 (95%CI: 0.850-1.000), 0.879, 0.778, 0.778, and 1.000, respectively. The combined model based on clinical features, spectral CT parameters and imaging data has good value for the preoperative prediction of gastric cancer neuroinvasion.

Multilinear compressive learning and reconstruction of hyperspectral data with U-Net architecture for enhanced spectral imaging

Hyperspectral (HSI) data provide vast amounts of data which contain rich spatial and spectral information about each pixel of an image, such information is useful in detecting objects and identifying materials. However, the information provided by the HSI image is challenging to process. Hence, the compression of HSI makes it easier to utilize the data without the need for any heavy computation. Traditional compressive sensing techniques are adequate for lower dimensional signals, but they fail to preserve the essential spectral information provided by HSIs while performing compression. The proposed method aims to implement a Multilinear Compressive Learning (MCL) model for ideal compression of HSIs and a CNN based U-NET architecture is used for effective reconstruction of the compressed signal. The core of the algorithm involves two key blocks: (a) A multidimensional compressive sensing block for performing compression along every dimension, preserving spectral and spatial correlations. The employed sensing matrix is learned with respect to the given data for effective compression, (b) Reconstruction of the signal using a CNN based U-NET algorithm, which consists of encoders and decoders for effective reconstruction of the signal. Band selection is also performed in this technique to optimally select the most informative bands. The proposed model was implemented on the Indian Pines, Pavia University, and Salinas scenes datasets. The compression and reconstruction results were evaluated using performance metrics.

Application of magnetic data and satellite spectral imaging in identifying gold mineralization zones and its associated subsurface structures at Fawakheir-Attala area, Central Eastern Desert, Egypt

The Fawakheir-Attala gold mining zone in Egypt’s Central Eastern Desert features a diverse range of rock formations, including Precambrian crystalline rocks and Phanerozoic sedimentary formations. These formations encompass gneisses, metavolcanics, metasediments, a metagabbro-diorite complex, Dokhan volcanic rocks, and granitic rocks, alongside lower and upper Nubia sandstones. Faults and shear zones are pivotal in controlling gold mineralization within the area, indicative of substantial mineral wealth. This study aimed to map subsurface structural characteristics and investigate gold mineralization zones using aerial magnetic data and ASTER remote sensing, the latter of which played a crucial role in highlighting the surface exposure of alteration zones. Geological surveys combined with remote sensing techniques were employed to identify rock types and mineralization zones, while magnetic methods, including aeromagnetic surveys and ground-based studies, were used to reveal underlying structural properties and fault systems. Analysis of aeromagnetic data revealed a large mineralization zone running from the Fawakhir Gold mine through the Attala Gold mine in a NW–SE direction. Various structural trends and faults, including NW–SE, NE–SW, E–W, and N–S directions, were identified, strongly associated with hydrothermal alteration zones and gold mineralization. Shallow basement relief was observed in the eastern and central regions, contrasting with deeper formations and greater relief in the western section. Land magnetic surveys were utilized to identify new areas for gold mineralization, with geochemical analysis confirming gold content in quartz veins and host rocks. The integration of magnetic and remote sensing techniques effectively highlighted alteration zones indicative of potential mineralization, which could have a subsurface continuation, aiding in the identification of gold occurrences connected to faults, lineaments, and mineralization zones.

Photon-counting detector CT (PCD-CT) generated iodine maps to characterize parenchymal lung disease: A feasibility study

BackgroundWith photon-counting CT, spectral imaging is always available, and iodine maps with high spatial and spectral resolution can be generated. ObjectivesThe aim of this study was to investigate whether iodine uptake in different parenchymal patterns can be used to characterise parenchymal disease with increased lung attenuation. Methods325 patients were scanned with a photon-counting CT using four scan protocols, all with lung parenchymal contrast. Lesions were classified into three basic patterns: consolidation, ground-glass opacities (GGO), and reticular pattern. Lesion classification was performed by 2 of 3 radiologists who were blinded to the diagnosis. Classification was performed twice using a 5-point Likert scale (with and without iodine maps). In case of disagreement, a third reader was consulted, and the decision was made by consensus. Results206 lesions were found with a confirmed diagnosis (83 consolidations, 72 GGO, and 51 reticular). Diagnostic confidence improved when iodine maps were included in the evaluation. The mean Likert score increased significantly for all three basic patterns (consolidations: 3.3 vs. 3.9, GGO: 3.4 vs. 4.1, and reticular: 3.6 vs. 4.4, p < 0.001). However, the score for GGO and reticular pattern was downgraded in three and one cases, respectively. The downgrading occurred for morphologically uncertain GGO findings (3) and atelectasis (1) with inhomogeneous iodine uptake. In 29 lesions, the classification was changed when the iodine maps were included in the evaluation. ConclusionIncluding iodine maps adds contrast uptake information and improves the diagnostic confidence of radiologists in the characterization of parenchymal pathologies. Clinical ImpactIodine maps have the potential to provide complementary information for the interpretation of lung opacities with overlapping morphology.

Detection of kernels in maize forage using hyperspectral imaging

In this study, the use of hyperspectral imaging was examined to enhance the kernel processing assessment of maize forage, which is crucial for optimizing the production of dairy cow feed and biogas production. As the visual contrast between the kernels and other crop particles is quite limited, spectral imaging could provide better kernel classification. A pushbroom hyperspectral imaging system was used, scanning samples collected during maize harvesting in the 400–1000 nm range. A PLSDA model for pixel classification was developed to distinguish kernel spectra from the other particles in the forage, achieving a pixel-level classification accuracy of 95.2 %. Next, the most important wavelengths were identified by means of a stepwise procedure using the Wilks Lambda criterion. A Pixel classification using the top five discriminating wavelengths achieved nearly the same accuracy as the full spectrum wavelength model (95.2 % compared to 93.5 %) and did considerably better than the RGB classifier’s 86.3 % accuracy. Finally, these top 5 discriminating wavebands were applied in an object detection deep learning model, more specifically a Faster R-CNN model. While object detection based on these 5 wavebands stilled outperformed object detection based on RGB wavebands, detection performance, as measured by AP50, was rather low. This weak performance resulted from the low resolution of the hyperspectral imaging camera.

A multiscale dilated attention network for hyperspectral image classification

Hyperspectral imaging is an image obtained by combining spectral detection technology and imaging technology, which can collect electromagnetic spectra in the wavelength range of visible light to near-infrared. It is an important research content in the field of ground observation in hyperspectral remote sensing. However, hyperspectral image face significant challenges in classification task due to their high spectral dimensions, lack of labeled samples, and strong correlation between bands. In order to fully extract features from both spectral and spatial dimensions and improve classification accuracy in the case of limited training samples, a multiscale dilated attention network is proposed for hyperspectral image classification. First, a three-dimensional convolutional layer is used to extract the shallow features of the image. Then, a multiscale dilated attention module is proposed by combining dilated convolution and channel attention. Using ordinary convolution and dilated convolution to form different receptive fields. Channel attention is used to remodel the obtained multiscale features, enhancing the inter-channel correlation. After that, a multiscale spatial-spectral attention module is constructed using multiple asymmetric convolutions to obtain spatial and spectral attention features at different positions, further enhancing important feature suppression over non-important features. Finally, using softmax to classify the obtained features. Using Indian Pines, Pavia University, KSC and University of Houston as experimental datasets, the overall classification accuracy of this paper’s method achieved 98.97%, 99.14%, 99.45%, and 98.56% respectively, using only 5%, 1%, 10%, and 10% of training samples per class. Compared with seven advanced classification methods, the experimental results show that the proposed method can achieve the highest classification accuracy with limited training samples.

X-ray dark-field via spectral propagation-based imaging

Dark-field X-ray imaging visualizes scattering from unresolved microstructure. Most dark-field imaging techniques rely on crystals or structured illumination, but recent work has shown that dark-field effects are observable in straightforward propagation-based imaging (PBI). Based on the single-material X-ray Fokker–Planck equation with an a priori dark-field energy dependence, we propose an algorithm to extract phase and dark-field effects from dual-energy PBI images. We successfully apply the dark-field retrieval algorithm to simulated and experimental dual-energy data, and show that by accounting for dark-field effects, projected thickness reconstruction is improved compared to the classic Paganin algorithm. With the emergence of spectral detectors, the method could enable single-exposure dark-field imaging of dynamic and living samples.

Optimal Spectral Performance on Pediatric Photon-Counting CT: Investigating Phantom-Based Size-Dependent kV Selection for Spectral Body Imaging.

The comprehensive evaluation of kV selection on photon-counting computed tomography (PCCT) has yet to be performed. The aim of the study is to evaluate and determine the optimal kV options for variable pediatric body sizes on the PCCT unit. In this study, 4 phantoms of variable sizes were utilized to represent abdomens of newborn, 5-year-old, 10-year-old, and adult-sized pediatric patients. One solid water and 4 solid iodine inserts with known concentrations (2, 5, 10, and 15 mg I/mL) were inserted into phantoms. Each phantom setting was scanned on a PCCT system (Siemens Alpha) with 4 kV options (70 and 90 kV under Quantum Mode, 120 and 140 kV under QuantumPlus Mode) and clinical dual-source (3.0 pitch) protocol. For each phantom setting, radiation dose (CTDIvol) was determined by clinical dose settings and matched for all kV acquisitions. Sixty percent clinical dose images were also acquired. Reconstruction was matched across all acquisitions using Qr40 kernel and QIR level 3. Virtual monoenergetic images (VMIs) between 40 and 80 keV with 10 keV interval were generated on the scanner. Low-energy and high-energy images were reconstructed from each scan and subsequently used to generate an iodine map (IM) using an image-based 2-material decomposition method. Image noise of VMIs from each kV acquisition was calculated and compared between kV options. Absolute percent error (APE) of iodine CT number accuracy in VMIs was calculated and compared. Root mean square error (RMSE) and bias of iodine quantification from IMs were compared across kV options. At the newborn size and 50 keV VMI, noise is lower at low kV acquisitions (70 kV: 10.5 HU, 90 kV: 10.4 HU), compared with high kV acquisitions (120 kV: 13.8 HU, 140 kV: 13.9 HU). At the newborn size and 70 keV VMI, the image noise from different kV options is comparable (9.4 HU for 70 kV, 8.9 HU for 90 kV, 9.7 HU for 120 kV, 10.2 HU for 140 kV). For APE of VMI, high kV (120 or 140 kV) performed overall better than low kV (70 or 90 kV). At the 5-year-old size, APE of 90 kV (median: 3.6%) is significantly higher (P < 0.001, Kruskal-Wallis rank sum test with Bonferroni correction) than 140 kV (median: 1.6%). At adult size, APE of 70 kV (median: 18.0%) is significantly higher (P < 0.0001, Kruskal-Wallis rank sum test with Bonferroni correction) than 120 kV (median: 1.4%) or 140 kV (median: 0.8%). The high kV also demonstrated lower RMSE and bias than the low kV across all controlled conditions. At 10-year-old size, RMSE and bias of 120 kV are 1.4 and 0.2 mg I/mL, whereas those from 70 kV are 1.9 and 0.8 mg I/mL. The high kV options (120 or 140 kV) on the PCCT unit demonstrated overall better performance than the low kV options (70 or 90 kV), in terms of image quality of VMIs and IMs. Our results recommend the use of high kV for general body imaging on the PCCT.

Validation of Timepix4 energy calibration procedures with synchrotron X-ray beams

The Timepix4 is an application-specific integrated circuit developed by the Medipix4 collaboration. The chip is a 448 × 512 matrix that can be connected, via bump-bonding, to pixelated sensors of various materials and thicknesses, forming application specific detection systems. Among the Timepix4 operating modes, the data-driven Time-of-Arrival - Time-Over-Threshold is a particularly promising mode for spectral imaging applications: data packets are generated every time the charge created by the incoming radiation goes above a pre-defined threshold, allowing the count of the detected particles and their energy measurement. This article investigates the spectroscopic characteristics of a Timepix4 assembly equipped with a 300 μm Si sensor, using monochromatic X-ray beams in the energy range 8.5 keV–40 keV working at the SYRMEP beamline of the Elettra synchrotron. The accuracy and the energy resolution of the reconstructed energy spectra, together with an energy resolution model, will be presented.

Nutritional Quality Analysis and Classification Detection of Buckwheat in Different Harvest Periods.

For buckwheat, the optimal harvest period is difficult to determine-too early or too late a harvest affects the nutritional quality of buckwheat. In this paper, physical and chemical tests are combined with a method using near-infrared spectroscopy nondestructive testing technology to study buckwheat harvest and determine the optimal harvest period. Physical and chemical tests to determine the growth cycle were performed at 83 days, 90 days, 93 days, 96 days, 99 days, and 102 days, in which the buckwheat grain starch, fat, protein, total flavonoid, and total phenol contents were assessed. Spectral images of buckwheat in six different harvest periods were collected using a near-infrared spectral imaging system. Four preprocessing methods (SNV, S-G, DWT, and the normaliz function) and three dimensionality reduction algorithms (IVSO, VCPA, VISSA) were used to process the raw buckwheat spectral data, and the full and eigen spectra were established as a random forest (RF). Random forest (RF) and Least Squares Support Vector Machine (LS-SVM) classification models were used to determine the full and eigen spectra, respectively, and the optimal model for the buckwheat single harvest period was determined and validated. Through physical and chemical tests, it was concluded that the 90-day harvest buckwheat grain protein, fat, and starch contents were the highest, and that the total flavonoid and total phenolic contents were also high. The SNV preprocessing method was the most effective, and the feature bands extracted using the IVSO algorithm were more representative. The IVSO-RF model was the best discriminative model for the classification of buckwheat in different harvest periods, with the correct rates of the training and prediction sets reaching 100% and 96.67%, respectively. When applying the IVSO-RF model to the buckwheat single harvest period to verify the classification, the correct rate of the training set for each harvest period reached 96%, and that of the prediction set reached 100%. Near-infrared spectroscopy combined with the IVSO-RF modeling method for buckwheat harvest period detection is a rapid, nondestructive classification method. When this was combined with physical and chemical analyses, it was determined that a growth cycle of 90 days is the best harvest period for buckwheat. The results of this study can not only improve the quality of buckwheat crops but also be applied to other crops to determine their optimal harvest period.

Performance Characterization of an Illumination-Based Low-Cost Multispectral Camera.

Spectral imaging has many applications, from methane detection using satellites to disease detection on crops. However, spectral cameras remain a costly solution ranging from 10 thousand to 100 thousand euros for the hardware alone. Here, we present a low-cost multispectral camera (LC-MSC) with 64 LEDs in eight different colors and a monochrome camera with a hardware cost of 340 euros. Our prototype reproduces spectra accurately when compared to a reference spectrometer to within the spectral width of the LEDs used and the ±1σ variation over the surface of ceramic reference tiles. The mean absolute difference in reflectance is an overestimate of 0.03 for the LC-MSC as compared to a spectrometer, due to the spectral shape of the tiles. In environmental light levels of 0.5 W m-2 (bright artificial indoor lighting) our approach shows an increase in noise, but still faithfully reproduces discrete reflectance spectra over 400 nm-1000 nm. Our approach is limited in its application by LED bandwidth and availability of specific LED wavelengths. However, unlike with conventional spectral cameras, the pixel pitch of the camera itself is not limited, providing higher image resolution than typical high-end multi- and hyperspectral cameras. For sample conditions where LED illumination bands provide suitable spectral information, our LC-MSC is an interesting low-cost alternative approach to spectral imaging.

Electrochemical, Structural, and Hyperspectral Imaging Investigation of Bifunctional Zn-Air Battery Gas-Diffusion Electrodes

Rechargeable Zinc-air batteries (RZAB) emerge as highly promising solutions for electrochemical energy storage (EES) applications. Nevertheless, a noteworthy drawback in the existing generation of these batteries lies in the air gas-diffusion electrode (GDE). This component grapples with issues related to both efficiency and durability. In response to this challenge, our research is concentrated on the creation of a novel GDE, accompanied by an in-depth examination of its electrochemical, structural, and compositional characteristics. The primary objective is to comprehensively assess the physico-chemical factors that exert an influence on its efficiency and durability. In the case of the present research, two different catalysts were chosen for the ORR (discharge) and OER (charge) electrocatalysis, namely α-MnO2 nanorods (NR) and Ni nanoparticles (NP), respectively. The GDEs were produced by applying a water-based catalyst ink, augmented with PTFE and C-black, through spray-coating onto a carbon paper support, which serves as the gas diffusion layer (GDL). Initially, the ideal proportion of OER/ORR catalyst was determined by conducting electrochemical tests on both the individual material (via RRDE studies) and the gas diffusion electrode using a rapid screening protocol. The material extracted from the GDEs, in pristine state and after ageing under operating conditions, was then characterized with electrochemical methods, SEM microscopy, HRTEM/SAED microscopy/electron diffraction, as well as with direct- and Fourier-space soft x-ray transmission microscopy (STXM) at the Mn, Ni and Zn L-edges. The electrochemical tests conducted in this study demonstrated a progressive improvement in performance as the nickel nanoparticle content increased, until a plateau is reached after 23% wt. Specifically, lower overpotentials were observed alongside increased cyclic stability for the combined ORR/OER processes. These assessments were conducted in two distinct electrolyte environments: pure KOH and KOH with the inclusion of Zn2+. The inclusion of Zn2+ aimed to replicate and understand the potential impact of anode chemistry on the cathode's overall performance. The investigation employed HRTEM and SAED analyses to meticulously monitor alterations in the crystal structure of the α-MnO2 electrocatalyst under diverse aging conditions. Notably, during ORR cycling, the manganese nanorods underwent amorphization. However, cyclic processes between oxygen OER and ORR revealed the recovering of the original crystalline phase. The introduction of Zn2+ into the system resulted in the formation of a partially amorphous Zn-Mn mixed oxide. To delve further into the nuanced changes, soft-X ray hyperspectral imaging, both in direct and Fourier space was employed. By spectral imaging at the Mn and Ni L-edges, we could assess the space-dependent valence alterations of Manganese and Nickel at different stages of aging with space resolution down to a few tens of nanometers. The integration of diverse analytical techniques, including electrochemical assessments, STXM, HRTEM/ SAED imaging, along with space-resolved structural analyses, has provided a comprehensive, molecular-level understanding of the performance and degradation of bifunctional ORR/OER electrocatalysts within the realistic context of gas diffusion electrodes. Of particular significance is the capability of this combined approach to meticulously trace and correlate the changes in the chemical state of the manganese ORR electrocatalyst. This shift, notably away from the optimal 3+/4+ mixed valence state, was observed in response to aging and Zn2+ contamination. The correlation of this chemical state drift with electrochemical conditions and the evolution of crystal structure adds a nuanced layer of insight into the intricate interplay of factors influencing the performance and degradation of these electrocatalysts. In particular, the control of the oxidative stress under OER, enabled by addition of Ni NPs, was correlated with preservation of α-MnO2 crystallinity, GDE operation in the presence of Zn2+ was proved to give rise to the formation of ZnMn2O4, poisoning the electrocatalyst. Lastly, the role of hydrophobicity of the active layer of the GDE was proven to be of crucial importance in the stability of the Mn-based catalyst, as PTFE-surrounded clusters were observed to be less affected by cycling with respect to others which clearly underwent electrolyte flooding. Figure 1

Freeform-surface-based optical design of a broadband compressive spectral imager with co-aperture coding

Compressive spectral imaging (CSI) is an advanced computational imaging approach to reconstruct the three-dimensional (3D) spatio-spectral data cube of a target scene through a single or a few snapshots. However, limited by the response range of the image detector, the existing CSI systems mostly work within narrow spectral bands, such as the visible or shortwave-infrared (SWIR) spectral band. The work band of the CSI system constrains the detection capacity for the targets under complex environments (such as rain, snow, haze, etc.). In addition, most of the current CSI prototypes lack engineering design for practical applications. This paper develops a novel, to the best of our knowledge, optical design scheme of a broadband CSI system with co-aperture coding to simultaneously realize visible multi-spectral imaging (10 channels) and SWIR super-resolution imaging (16×). The freeform surfaces are used to design the front-end reflective objective lens, thus significantly improving the image quality and spatial modulation precision of the system. By means of performance evaluation and tolerance analysis, excellent image quality and manufacturability of the proposed system are demonstrated.

Studying on the in vivo pathological evolution of spinal cord injury with the rat model by the method of integrated multispectral imaging and Raman spectroscopy

Spinal cord injury (SCI) is a debilitating neurological and pathological condition that results in significant impairments in motor, sensory, and autonomic functions. By integrating multispectral imaging (MSI) with Raman spectroscopy, a label-free optical methodology was developed for achieving a non-invasive in vivo understanding on the pathological features of SCI evolution. Under the guidance of captured the spectral imaging data cube with a rigid endoscope based MSI system, a special designed fiber probe passed through the instrumental channel for acquiring the finger-print spectral information from compression rat SCI models. After identifying the main visual features of injured spinal cord tissue in all Sham, 0-, 3- and 7-days post injury (0 DPI, 3 DPI, and 7 DPI) groups, the blood volume and oxygen content were visualized to describe hemorrhage, hypoxia and inflammatory state after acute injury. The averaged reflectance spectra, which were deduced from MSI data cubes, were utilized for describing oxygen saturation and hemoglobin concentration in living tissue. The results of Raman spectroscopy addressed complex compositional and conformational phenomena during SCI progression, correlated with the well-known event like neuronal apoptosis, hemorrhage, demyelination, and even the upregulation of chondroitin sulfate proteoglycans (CSPGs). A principal component analysis and linear discriminate algorithm (PCA-LDA) based discriminate model was introduced for categorizing spectral features in different injury stages, which was applicable for intraoperative interpretations on the complex pathological courses of SCI and therapeutic outcomes.

Compressive single-pixel spectral imaging with spatial-spectral modulation optimization via coherence minimization

Compressive spectral imaging (CSI) obtains spatial-spectral information from under-sampled measurements, which solves the contradiction between imaging speed and resolution in conventional scanning-based spectral imaging systems. The spatial-spectral modulation and the corresponding sparse prior in reconstruction algorithm jointly determine the reconstruction quality of CSI systems. In this paper, a compressive single-pixel spectral imaging system with the spatial-spectral modulation and the sparse prior simultaneously optimized by coherence minimization is proposed. By formulating the modulation of the single-pixel spectral imaging system and the sensing coherence in the differentiable matrix notation, the gradients of the modulation and the sparse prior with respect to the sensing coherence are derived for both spatial and spectral dimensions. The spatial-spectral modulation and the sparse prior are optimized via gradient descent to minimize the sensing coherence, for both the decoupled CSI systems with one dimension completely sampled and the coupled CSI system with both spatial-spectral dimensions compressively sampled. For the optimized spatial-spectral modulation, high relative mutual differences in the spectral dimension as well as low redundancy patterns in the spatial dimension are achieved, and by combining with the optimized spatial-spectral sparse prior, the enhancement of 5.7 dB for PSNR compared to the unoptimized system is realized.

Video‐Rate Spectral Imaging Based on Diffractive‐Refractive Hybrid Optics

Video‐Rate Spectral Imaging Based on Diffractive‐Refractive Hybrid Optics

Fine-Tuning of Sub-Annual Resolution Spectral Index Time Series from Eifel Maar Sediments, Western Germany, to the NGRIP δ18O Chronology, 26–60 ka

Recent technological advancements in spectral imaging core-scanning techniques have proved to be a promising tool to study lake sediments at extremely high resolution. We used this novel analytical approach to scan core AU3 of the Pleistocene Auel maar, Western Germany. The resulting ultra-high-resolution RABD670 spectral index, a proxy for the lake’s primary production, shows an almost complete succession of Greenland Interstadials of the NGRIP ice core chronology back to around 60,000 years. Using the ELSA-20 chronology and its anchor points to the NGRIP record as a stratigraphic basis, we were able to compare and fine-tune prominent climate signals occurring in both regions. This in-depth correlation yields strong evidence that the climates of Greenland and Central Europe were not only strongly coupled on timescales of stadials and interstadials but even on multidecadal scales, showing prominent climate cycles between 20 and 125 years. As climate changes in these regions were ultimately driven by variations in the North Atlantic meridional heat transport, their strong coupling becomes most apparent during cold and arid intervals. In contrast, longer-lasting warmer and more humid phases caused the activation of various regional feedback mechanisms (e.g., soil formation, forest growth), resulting in more complex patterns in the proxy records.

Spectroscopic evidence of cool plasma in quiet Sun small-scale brightenings detected by HRIEUV on board Solar Orbiter

Context. A large number of small and short-lived EUV brightenings have been detected in the quiet Sun (QS) over the past three years by the High Resolution Imager of the Extreme Ultraviolet Imager (HRIEUV) on board Solar Orbiter. It remains uncertain whether these events reach coronal temperatures and, thus, whether they directly contribute to coronal heating. Aims. In this work, we evaluate the maximum temperature of 11 EUV brightenings in the QS through plasma diagnostics with UV/EUV spectroscopy and imaging. Methods. We used three QS observations coordinated alongside those of HRIEUV: Spectral Imaging of the Coronal Environment (SPICE/Solar Orbiter), EUV Imaging Spectrometer (EIS/Hinode), and Atmospheric Imaging Assembly (AIA/SDO). We detected events in HRIEUV, ranging from 0.8 to 6.2 Mm in length. We then identified nine of them in SPICE and AIA, as well as three in EIS. We investigated their temporal evolution using their light curves and applied temperature diagnostics, such as the LOCI emission measure (EM) and the differential EM (DEM). We also estimated the electron density of one event identified in EIS. Results. These events are dominated by emission of plasma at chromospheric and TR temperatures and they barely reach temperatures above 1 MK. As such, we concluded that their contribution to coronal heating is not dominant. The estimated density of one of the event is ne = (1.8 ± 1.3)×1010 cm−3.

To Reconstruct or Discard: A Comparison of Additive and Subtractive Charge Sharing Correction Algorithms at High and Low X-ray Fluxes.

Effective X-ray photon-counting spectral imaging (x-CSI) detector design involves the optimisation of a wide range of parameters both regarding the sensor (e.g., material, thickness and pixel pitch) and electronics (e.g., signal-processing chain and count-triggering scheme). Our previous publications have looked at the role of pixel pitch, sensor thickness and a range of additive charge sharing correction algorithms (CSCAs), and in this work, we compare additive and subtractive CSCAs to identify the advantages and disadvantages. These CSCAs differ in their approach to dealing with charge sharing: additive approaches attempt to reconstruct the original event, whilst subtractive approaches discard the shared events. Each approach was simulated on data from a wide range of x-CSI detector designs (pixel pitches 100-600 µm, sensor thickness 1.5 mm) and X-ray fluxes (106-109 photons mm-2 s-1), and their performance was characterised in terms of absolute detection efficiency (ADE), absolute photopeak efficiency (APE), relative coincidence counts (RCC) and binned spectral efficiency (BSE). Differences between the two approaches were explained mechanistically in terms of the CSCA's effect on both charge sharing and pule pileup. At low X-ray fluxes, the two approaches perform similarly, but at higher fluxes, they differ in complex ways. Generally, additive CSCAs perform better on absolute metrics (ADE and APE), and subtractive CSCAs perform better on relative metrics (RCC and BSE). Which approach to use will, thus, depend on the expected operating flux and whether dose efficiency or spectral efficiency is more important for the application in mind.

DeepQR: single-molecule QR codes for optical gene-expression analysis

Abstract Optical imaging and single-molecule imaging, in particular, utilize fluorescent tags in order to differentiate observed species by color. The degree of color multiplexing is dependent on the available spectral detection window and the ability to distinguish between fluorophores of different colors within this window. Consequently, most single-molecule imaging techniques rely on two to four colors for multiplexing. DeepQR combines compact spectral imaging with deep learning to enable 4 color acquisition with only 3 spectral detection windows. It allows rapid high-throughput acquisition and decoding of hundreds of unique single-molecule color combinations applied here to tag native RNA targets. We validate our method with clinical samples analyzed with the NanoString gene-expression inflammation panel side by side with the commercially available NanoString nCounter system. We demonstrate high concordance with “gold-standard” filter-based imaging and over a four-fold decrease in acquisition time by applying a single snapshot to record four-color barcodes. The new approach paves the path for extreme single-molecule multiplexing.