High Spectral Accuracy Research Articles

Impact of scatter radiation on spectral quantification performance of first- and second-generation dual-layer spectral computed tomography.

To assess the impact of scatter radiation on quantitative performance of first and second-generation dual-layer spectral computed tomography (DLCT) systems. A phantom with two iodine inserts (1 and 2mg/mL) configured to intentionally introduce high scattering conditions was scanned with a first- and second-generation DLCT. Collimation widths (maximum of 4cm for first generation and 8cm for second generation) and radiation dose levels were varied. To evaluate the performance of both systems, the mean CT numbers of virtual monoenergetic images (MonoEs) at different energies were calculated and compared to expected values. MonoEs at 50 versus 150keV were plotted to assess material characterization of both DLCTs. Additionally, iodine concentrations were determined, plotted, and compared against expected values. For each experimental scenario, absolute errors were reported. An experimental setup, including a phantom design, was successfully implemented to simulate high scatter radiation imaging conditions. Both CT scanners illustrated high spectral accuracy for small collimation widths (1 and 2cm). With increased collimation (4cm), the second-generation DLCT outperformed the earlier DLCT system. Further, the spectral performance of the second-generation DLCT at an 8cm collimation width was comparable to a 4cm collimation on the first-generation DLCT. A comparison of the absolute errors between both systems at lower energy MonoEs illustrates that, for the same acquisition parameters, the second-generation DLCT generated results with decreased errors. Similarly, the maximum error in iodine quantification was less with second-generation DLCT (0.45 and 0.33mg/mL for the first and second-generation DLCT, respectively). The implementation of a two-dimensional anti-scatter grid in the second-generation DLCT improves the spectral quantification performance. In the clinical routine, this improvement may enable additional clinical benefits, for example, in lung imaging.

Single-pixel p-graded-n junction spectrometers.

Ultra-compact spectrometers are becoming increasingly popular for their promising applications in biomedical analysis, environmental monitoring, and food safety. In this work, we report a single-pixel-photodetector spectrometer with a spectral range from 480 nm to 820 nm, based on the AlGaAs/GaAs p-graded-n junction with a voltage-tunable optical response. To reconstruct the optical spectrum, we propose a tailored method called Neural Spectral Fields (NSF) that leverages the unique wavelength and bias-dependent responsivity matrix. Our spectrometer achieves a high spectral wavelength accuracy of up to 0.30 nm and a spectral resolution of up to 10 nm. Additionally, we demonstrate the high spectral imaging performance of the device. The compatibility of our demonstration with the standard III-V process greatly accelerates the commercialization of miniaturized spectrometers.

Direct-detected spectroscopy based on a plasmonic Schottky photodetector and a deep neural network.

Computational algorithms have facilitated the miniaturization of spectrometers, which is essential for on-chip and portable applications. A plasmonic Schottky photodetector provides a filter-free and CMOS-compatible scheme for spectral measurement. In this study, we report on a direct-detected spectral analysis based on an integrated vertically coupled plasmonic nanostructure Schottky photodetector. We demonstrate that the plasmonic Schottky photodetector has a fast response with a -3 dB bandwidth of 600 kHz and a high peak detectivity of 8.65 × 1010 Jones. By designing a deep neural network (DNN), we demonstrate the reconstruction of the unknown spectrum with a mean square error (MSE) of 1.57 × 10-4 at a broad operating wave band of 450-950 nm, using only 20 distinct devices. Moreover, the spectral resolution of the 20 devices can reach to 7 nm. These findings provide a promising route for the development of chip-integrated spectrometers with high spectral accuracy and optical performance.

The EnMAP imaging spectroscopy mission towards operations

EnMAP (Environmental Mapping and Analysis Program) is a high-resolution imaging spectroscopy remote sensing mission that was successfully launched on April 1st, 2022. Equipped with a prism-based dual-spectrometer, EnMAP performs observations in the spectral range between 418.2nm and 2445.5nm with 224 bands and a high radiometric and spectral accuracy and stability. EnMAP products, with a ground instantaneous field-of-view of 30m×30m at a swath width of 30km, allow for the qualitative and quantitative analysis of surface variables from frequently and consistently acquired observations on a global scale. This article presents the EnMAP mission and details the activities and results of the Launch and Early Orbit and Commissioning Phases until November 1st, 2022. The mission capabilities and expected performances for the operational Routine Phase are provided for existing and future EnMAP users.

Dual-camera compressive hyperspectral imaging based on deep image prior and a guided filter.

Coded aperture snapshot spectral imaging (CASSI) aims to capture the high-dimensional (usually 3D) data cube using a 2D sensor in a single snapshot. Due to the ill-posed snapshot, the reconstruction results are not ideal. One feasible solution is to utilize additional information such as the panchromatic measurement in CASSI. In this paper, we propose a dual-camera hyperspectral reconstruction method based on the deep image prior (DIP) and a guided filter. In particular, the panchromatic measurements are used to estimate spatial detail, and spectral details are provided using CASSI measurements. These measurements are used as a priori learning by the self-supervised network. Using iteration combined with DIP, the hyperspectral reconstruction is continuously updated iteratively. Finally, the panchromatic measurement is used as the guidance image, and the reconstruction result is optimized by guide filtering. A large number of experimental results demonstrate that our method without training data can reconstruct spectral data with both high spectral accuracy and spatial resolution.

Hyperspectral image reconstruction based on the fusion of diffracted rotation blurred and clear images

To overcome the problems of imaging speed and bulky volume of the traditional hyperspectral imaging systems, the recently proposed compact, snapshot hyperspectral imaging system with diffracted rotation has attracted a lot of interest. Due to the severe degradation of the diffracted rotation blurred image, the restored hyperspectral image (HSI) suffers from a lack of spatial detail information and spectral accuracy. To improve the quality of the reconstructed HSI, we present a joint imaging system of diffractive imaging and clear imaging as well as a convolutional neural network (CNN) based method with two input branches for HSI reconstruction. In the reconstruction network, we develop a feature extraction block (FEB) to extract the features of the two input images, respectively. Subsequently, a double residual block (DRB) is designed to fuse and reconstruct the extracted features. Experimental results show that HSI with high spatial resolution and spectral accuracy can be reconstructed. Our method outperforms the state-of-the-art methods in terms of quantitative metrics and visual quality.

Analysis Model of Image Colour Data Elements Based on Deep Neural Network.

At present, the classification method used in image colour element analysis in China is still based on subjective visual evaluation. Because the evaluation process will inevitably be disturbed by human factors, it will not only have low efficiency but also produce large errors. To solve the above problems, this paper proposes an image colour data element analysis model based on depth neural network. Firstly, intelligent analysis of image colour data elements based on tensorflow is constructed, and the isomerized tensorflow framework is designed with the idea of Docker cluster to improve the efficiency of image element analysis. Secondly, considering the time error and spatial error diffusion model in the process of image analysis, the quantization modified error diffusion model is replaced by the original model for more accurate colour management. Image colour management is an important link in the process of image reproduction; the rotating principal component analysis method is used to correct and analyze the image colour error. Finally, using the properties of transfer learning and convolution neural network, an image colour element analysis model based on depth neural network is established. Large-scale image data is collected, and the effectiveness and reliability of the algorithm are verified from different angles. The results show that the new image colour analysis method can not only reveal the true colour components of the target image; furthermore, the real colour component of the target image also has high spectral data reconstruction accuracy, and the analysis results have strong adaptability.

Adopting Gram-Schmidt and Brovey Methods for Estimating Land Use and Land Cover Using Remote Sensing and Satellite Images

The production of Land Use and Land Cover thematic maps using remote sensing data is one of the things that must be dealt with carefully to obtain accurate results, data is obtained from sensors of different characteristics. It is not possible to obtain high spatial and spectral accuracy in one image, so we used a fusion image (multispectral image with a low spatial resolution with a panchromatic image with high spatial resolution), which achieved high efficiency in improving the methods of producing Land Use and Land Cover maps. In this study, we used Landsat-8 multispectral and panchromatic images. The study aims to investigate the effectiveness of panchromatic images in improving the methods of producing Land Use and Land Cover maps for the city of Karbala, Iraq. The Support Vector Machine was used to classify the fusion images using the Brovey method and Gram-Schmidt sharpening algorithms. The appropriate methodology for producing Land Use and Land Cover maps was suggested by comparing classifying results and the classification accuracy was evaluated through the confusion matrix. Where the results showed that the method of classifying the fused image by Gram-Schmidt and classified by Support Vector Machine is the best way to produce Land use and Land cover maps for the study area and achieved the highest results for overall accuracy and kappa coefficient of 97.81% and 0.95, respectively.

FT-ICR Mass Spectrometry Imaging at Extreme Mass Resolving Power Using a Dynamically Harmonized ICR Cell with 1ω or 2ω Detection.

MALDI mass spectrometry imaging (MALDI MSI) is a powerful analytical method for achieving 2D localization of compounds from thin sections of typically but not exclusively biological samples. The dynamically harmonized ICR cell (ParaCell) was recently introduced to achieve extreme spectral resolution capable of providing the isotopic fine structure of ions detected in complex samples. The latest improvement in the ICR technology also includes 2ω detection, which significantly reduces the transient time while preserving the nominal mass resolving power of the ICR cell. High-resolution MS images acquired on FT-ICR instruments equipped with 7T and 9.4T superconducting magnets and the dynamically harmonized ICR cell operating at suboptimal parameters suffered severely from the pixel-to-pixel shifting of m/z peaks due to space-charge effects. The resulting profile average mass spectra have depreciated mass measurement accuracy and mass resolving power under the instrument specifications that affect the confidence level of the identified ions. Here, we propose an analytical workflow based on the monitoring of the total ion current to restrain the pixel-to-pixel m/z shift. Adjustment of the laser parameters is proposed to maintain high spectral resolution and mass accuracy measurement within the instrument specifications during MSI analyses. The optimized method has been successfully employed in replicates to perform high-quality MALDI MS images at resolving power (FWHM) above 1,000,000 in the lipid mass range across the whole image for superconducting magnets of 7T and 9.4T using 1 and 2ω detection. Our data also compare favorably with MALDI MSI experiments performed on higher-magnetic-field superconducting magnets, including the 21T MALDI FT-ICR prototype instrument of the NHMFL group at Tallahassee, Florida.

Channeled imaging spectropolarimeter reconstruction by neural networks.

Snapshot channeled imaging spectropolarimetry (SCISP), which can achieve spectral and polarization imaging without scanning (a single exposure), is a promising optical technique. As Fourier transform is used to reconstruct information, SCISP has its inherent limitations such as channel crosstalk, resolution and accuracy drop, the complex phase calibration, et al. To overcome these drawbacks, a nonlinear technique based on neural networks (NNs) is introduced to replace the role of Fourier reconstruction. Herein, abundant spectral and polarization datasets were built through specially designed generators. The established NNs can effectively learn the forward conversion procedure through minimizing a loss function, subsequently enabling a stable output containing spectral, polarization, and spatial information. The utility and reliability of the proposed technique is confirmed by experiments, which are proved to maintain high spectral and polarization accuracy.

A novel photoluminescence hyperspectral camera for the study of artworks

We present the application of a novel hyperspectral camera, based on the Fourier-transform approach, to study the photoluminescence emission from artworks at different spatial scales and emission timescales. The hyperspectral system relies on an innovative wide-field, compact and ultra-stable interferometer coupled to different excitation and detection methods. Here, we describe and illustrate the potentialities and limitations of its use when coupled with excitation at variable fluence and with time-gated detection. The developed methods allow an in-depth characterization of the optical emission from luminescent materials in cultural heritage and provide information on the nature of the recombination pathways in crystalline pigments. Indeed, one of the main difficulties in the interpretation of the optical emission from artworks is the presence of multiple emitting compounds with spectra characterized by broad emission bands. The photoluminescence imaging methods here proposed allow to partially solve this issue, by separating emission from different materials on the basis of their different timescales and spectral emission properties, thus providing important information to support material identification. Furthermore, the high spectral accuracy achievable with a hyperspectral camera, such as the one proposed in this paper, allows the collection of highly resolved spectral datacubes, which can then be post-processed with computational and multivariate statistical analysis methods to better assess material identification and mapping.

Multi-objective topology optimization of pin-fin heat exchangers using spectral and finite-element methods

Forced convective pin-fin heat exchangers, due to the high wet surface area per volume and the hindered thermal boundary layers, feature low thermal resistances. However, the considerable coolant pressure drop, particularly for densely packed fin arrays, imposes operational costs for pumping power supply. This paper develops a multi-objective topology optimization approach to optimize sink geometries in order to minimize thermal resistance and pressure loss, concurrently. In accordance to the pin-fin geometrical characteristics, a dedicated pseudo-3D conjugate heat transfer model is utilized, by assuming periodic flow and fin design pattern, to reasonably reduce the high cost of full-3D model optimization. For the solution of flow part, a pseudo-spectral scheme is used, which is intrinsically periodic and features a high spectral accuracy, and the finite element method for the non-periodic conjugate heat transfer model. Exact partial derivatives of the discrete solutions are obtained analytically by hand-differentiation. This task is rather convenient for the flow part, due to the simplicity of the pseudo-spectral implementation; however, the MATLAB symbolic toolbox is selectively utilized for the finite element code to ensure an error-free implementation. Finally, the sensitivities are computed directly or via a discrete adjoint method, for the flow and heat models, respectively. To examine the present approach, two approaches are used for optimization of a practical cooling task: constrained and unconstrained multi-objective formulations, where in all cases more emphasis is placed on thermal resistance minimization. A series of optimized heat sink geometries via constrained or unconstrained multi-objective optimizations are obtained to examine practical utility of the present approach. The optimized topologies demonstrated superior cooling performances at lower costs of pressure losses compared to conventional (circular) in-line and staggered fins, and confirmed the supremacy of topology over pure sizing optimization.

Variable-order optimal implicit finite-difference schemes for explicit time-marching solutions to wave equations

The time-space-domain finite-difference method (FDM) is widely used in forward modeling of wave equations. Conventional explicit FDMs (EFDMs) (with high order in space and the second order in time) would result in apparent temporal and spatial dispersion for high frequencies and large time steps. Moreover, the saturation effect of Taylor expansion seriously restricts the improvement of bandwidth coverage and efficiency of EFDMs. We have developed a variable-order optimization scheme of the implicit FDM (IFDM) to improve the computational efficiency and numerical accuracy of forward modeling. Then, we applied time-dispersion transforms (TDTs) to filter out the temporal dispersion generated by the second-order temporal approximations. Our method greatly alleviates the saturation effect of the high-order spatial finite-difference (FD) operators. Dispersion analysis indicates that the optimized coefficients of the IFDM based on the Remez algorithm can achieve the widest bandwidth (close to the Nyquist wavenumber), which corresponds to the shortest length of the spatial FD operator under a given error threshold. Our method has great potential to approach the highest spectral accuracy but with minimal increase in computational cost. Numerical experiments indicate that the combination of the variable-order optimization of IFDM and TDTs can significantly reduce numerical dispersion. Compared with traditional methods, our scheme is more advantageous for numerical simulation of large-scale geologic models because it has the least amount of calculation burden under the same accuracy requirements.

Numerical simulation of a shock–helium bubble interaction

We solve the Euler equations to compute the interaction of a Mach 1.22 shock wave with a helium bubble contaminated by 28% air by mass. A ninth-order upwind scheme is used to calculate the left and the right states of the primitive variables as required by the AUSMD algorithm. The low numerical dissipation of the AUSMD algorithm makes it an ideal choice for computing long-time behavior of the bubble after the shock passes over it. The algorithm combines well with the high spectral accuracy of the ninth-order upwind scheme. The basic trends in the evolving bubble match with earlier experimental and numerical observations. Effect of numerical schemes and grid sizes is also observed for this study. The Euler solver captures a large number of small-scale rolled-up vortices originally generated by the baroclinic torque term in the vorticity transport equation and later enhanced by the Kelvin–Helmholtz instability. Numerical schlieren, mass fraction, and vorticity contour plots are used to visualize the turbulent mixing zone that is a key feature of the translating and deforming bubble.

FORUM: Unique Far-Infrared Satellite Observations to Better Understand How Earth Radiates Energy to Space

AbstractThe outgoing longwave radiation (OLR) emitted to space is a fundamental component of the Earth’s energy budget. There are numerous, entangled physical processes that contribute to OLR and that are responsible for driving, and responding to, climate change. Spectrally resolved observations can disentangle these processes, but technical limitations have precluded accurate space-based spectral measurements covering the far infrared (FIR) from 100 to 667 cm−1(wavelengths between 15 and 100µm). The Earth’s FIR spectrum is thus essentially unmeasured even though at least half of the OLR arises from this spectral range. The region is strongly influenced by upper-tropospheric–lower-stratospheric water vapor, temperature lapse rate, ice cloud distribution, and microphysics, all critical parameters in the climate system that are highly variable and still poorly observed and understood. To cover this uncharted territory in Earth observations, the Far-Infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission has recently been selected as ESA’s ninth Earth Explorer mission for launch in 2026. The primary goal of FORUM is to measure, with high absolute accuracy, the FIR component of the spectrally resolved OLR for the first time with high spectral resolution and radiometric accuracy. The mission will provide a benchmark dataset of global observations which will significantly enhance our understanding of key forcing and feedback processes of the Earth’s atmosphere to enable more stringent evaluation of climate models. This paper describes the motivation for the mission, highlighting the scientific advances that are expected from the new measurements.

Fabrication of a Smartphone-Based Spectrophotometer and Its Application in Monitoring Concentrations of Organic Dyes.

In this study, an in-house constructed paper-based spectrophotometer is presented and demonstrated for detecting three organic dyes, namely, methylene blue, malachite green, and rhodamine B, and monitoring the efficiency of their removal from a wastewater sample with Sistan sand as a costless adsorbent. The compact design and light weight of this simple spectrophotometer delivered portability, with materials costing less than a dollar. Spectral analysis of the captured images was performed using free downloadable software from the Google Play store. The main experimental parameters affecting the efficiency of dye adsorption including pH, sorbent dosage, initial dye concentration, and contact time were investigated and optimized using the Taguchi design experimental method. Validation experiments were performed using a standard commercial bench-top spectrophotometer, and results were compared in terms of analytical performance, speed, and cost of analysis. The smartphone-based spectrometer was able to measure accurately, as confirmed using the commercial spectrometer, with enhanced sensitivity for methylene blue and rhodamine B. The combination of the high spectral accuracy of the paper-based spectrophotometer, together with sand as a readily accessible sorbent, enabled us to develop a powerful yet simple approach and tool for the removal and monitoring of dyes within wastewater samples, which is potentially available to everybody who owns a smartphone.

A feasible strategy to improve confident elemental composition determination of compounds in complex organic mixture such as natural organic matter by FTICR-MS without internal calibration

Confident elemental composition determination of compounds in complex samples such as natural organic matter (NOM) by ultrahigh resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) is challenging due to the interference between multiple components in these samples during detection. Here the performance of Solarix 15T-FTICR-MS in terms of accurate relative natural isotope abundance (RIA) and mass measurements for elemental composition determination of compounds in complex samples such as NOM was systematically evaluated. The optimal sweep excitation power values ranging from 20% to 22% was found to significantly diminish the underestimation of RIA measurement for 13C1 peaks of NOM components by FTICR-MS. Random error was found to be one of the main sources for the RIA errors of 13C1 peaks with S/N ratios <25. The mean averaged RIA errors of less than 10% could be obtained by averaging the measured RIAs of each 13C1 peaks in five replicated runs. By adjusting the total ion abundance of NOM complex sample between 3.8-E7 and 1.4-E8 which was simultaneously similar to that of external calibrant during detection, mass errors of lower than 1 ppm for NOM components with m/z lower than 700 Da could be obtained without internal calibration. Meanwhile, a linear correlation between mass errors of ions in NOM complex sample and their m/z values could be obtained. The mass error deviation derived from the linearity was firstly used as new criterion to reduce the number of false formula candidates. A novel strategy of combination of high mass accuracy, high spectral accuracy, and mass error deviation for elemental composition determination of unknown compounds in complex sample such as NOM by FTICR-MS was proposed and applied for different complex samples. Compared to the traditional method, about one fold increasement in the number of the unique formula assignments for measured ions was obtained by using our strategy.

Raman Probes for In Situ Molecular Analyses of Peripheral Nerve Myelination.

The myelinating activity of living Schwann cells in coculture with neuronal cells was examined in situ in a Raman microprobe spectroscope. The Raman label-free approach revealed vibrational fingerprints directly related to the activity of Schwann cells' metabolites and identified molecular species peculiar to myelinating cells. The identified chemical species included antioxidants, such as hypotaurine and glutathione, and compartmentalized water, in addition to sphingolipids, phospholipids, and nucleoside triphosphates also present in neuronal and nonmyelinating Schwann cells. Raman maps at specific frequencies could be collected, which clearly visualized the myelinating action of Schwann cells and located the demyelinated ones. An important finding was the spectroscopic visualization of confined water in the myelin structure, which exhibited a quite pronounced Raman signal at ∼3470 cm-1. This peculiar signal, whose spatial location precisely corresponded to a low-frequency fingerprint of hypotaurine, was absent in unmyelinating cells and in bulk water. Raman enhancement was attributed to frustration in the hydrogen-bond network as induced by interactions with lipids in the myelin sheaths. According to a generally accepted morphological model of myelin, an explanation was offered of the peculiar Raman scattering of water confined in intraperiod lines, according to an ordered hydrogen bonding structure. The possibility of concurrently mapping antioxidant molecules and compartmentalized water structure with high spectral accuracy and microscopic spatial resolution enables probing myelinating activity and might play a key-role in future studies of neuronal pathologies. Compatible with life, Raman microprobe spectroscopy with the newly discovered probes could be suitable for developing advanced strategies in the reconstruction of injured nerves and nerve terminals at neuromuscular junctions.

Spatial resolution as a trade-space for low-light imaging of sensitive cultural heritage documents

Abstract A 14th-century manuscript was imaged at high image quality with an HSI detector, using a low-illumination dose of 57 lx h, significantly less than one day’s worth of museum display (≈ 400 lx h). Initial work digitally downsampled spatial resolution from 333 pixels per inch (ppi) to 161 and 80 ppi, and increased the signal-to-noise ratio (SNR) by a factor of 2.00 and 2.79. Despite downsampling, pan-sharpened images were up to 1.5 times spatially sharper than the reference HSI image. Relatively high spectral accuracy was maintained, with spectral angle mapper (SAM) measurements of 0.0527–0.0963. The process was then repeated with physical adjustments to imaging height replacing the digital downsampling process, decreasing resolution from 403 ppi to 111 ppi, increasing SNR from 27.1 to 95.4, and sharpness by 60% using pan-sharpening, while maintaining SAM values of 0.110. An overall image quality metric is proposed, composed of fundamental spatial, spectral, and SNR metrics. This metric highlights efficient lighting and processing methods for increased image quality while limiting total illumination dose on the target, and can be tailored based on one’s imaging constraints.

The refined spatiotemporal representation of soil organic matter based on remote images fusion of Sentinel-2 and Sentinel-3

Remote sensing technology is important for soil organic matter (SOM) estimation, but existing studies have mainly relied on a single data source. This limitation makes it difficult to simultaneously ensure high spatial resolution, high spectral accuracy and refined temporal granularity simultaneously, which cannot meet the requirements of the spatiotemporal dynamics representation. This study aimed to introduce a new remote sensing image source into SOM modeling and spatiotemporal estimation generated by fusing together Sentinel-2 and Sentinel-3 remote sensing images that have a 5-day revisit cycle; 10 m spatial resolution; and 21 different bands in blue, green, red and NIR spectral ranges. According to the image fusion process, a total of 52 available images were acquired between November 2016 and December 2018 in Donghai County, China. The fused images were used for SOM estimation model associated with 107 field samples. The results indicated that, first, the optimal model consisted of the band reflectivity (B20) and RVI (B18/B9), which were derived from the fused images, and the R2 approached 0.7 in the two phases of the synchronized data. Second, the modeling accuracy was influenced to some extent by the actual SOM content. The R2 values exceeded 0.75 when the SOM content was higher than 24 g/kg, while the R2 was even lower than 0.35 when the SOM content was lower. Third, the averaged SOM contents remained stable in general, while the seasonal variances can also be found during the two-year interval. The SOM contents maintained a low level during autumn and winter, while higher SOM levels were found in the spring and summer. Finally, the spatial variations could be described as ‘low in the west and high in the east’. In summary, the spatiotemporal dynamics of SOM highlighted the necessity of modeling with fused remote sensing images, and more effective modeling could be expected with the continued increase in SOM in future.