Spectral Imaging Research Articles

Feasibility Study of Scanning Spectral Imaging Based on a Birefringence Flat Plate

Hyper-spectral imaging (HSI) systems can be divided into two main types as follows: a group of systems that includes a dedicated dispersion/filtering component whose role is to physically separate the different wavelengths and a group of systems that sample all wavelengths in parallel, so that the separation into wavelengths is performed by signal processing (interferometric method). There is a significant advantage to systems of the second type in terms of the integration time required to obtain a signal with a high signal-to-noise ratio since the signal-to-noise ratio of methods based on scanning interferometry (Windowing method) is better compared to methods based on dispersion. The current research deals with the feasibility study of a new concept for an HSI system that is based on scanning interferometry using the “push-broom” method. In this study, we investigated the viability of incorporating a simple birefringent plate into a scanning optical system. By exploiting the motion of the platform on which the system is mounted, we extracted the spectral information of the scanned region. This approach combines the benefits of scanning interferometry with the simplicity of the setup. According to the theory, a chirped cosine-shaped interferogram is obtained for each wavelength due to the nonlinear behavior of the optical path difference of light in the birefringent plate as a function of the angle. An algorithm converts the signal from a superposition of chirped cosine signals to a scaled interferogram such that Fourier transforming (FT) the interferogram retrieves the spectral information. This innovative idea can turn a simple monochrome camera into a hyperspectral camera by adding a relief lens and a birefringent plate.

Spectroscopy Detection and Imaging System Based on Line Array Single Photon Detectors

Abstract. Single-photon detectors, with their exceptional sensitivity, provide a reliable means for single-photon-level detection, demonstrating significant advantages in detecting weak signals in complex environments compared to traditional detectors. With the continuous advancement in semiconductor manufacturing technology, single-photon detectors based on linear array configurations have emerged and rapidly developed. This study utilizes a linear array single-photon detector in free-running mode combined with a scanning mechanism to design and implement a spectral detection and imaging system. Through the spectral scanning unit, this system successfully achieves precise spectral detection in the 890 nm to 1710 nm range, with a spectral resolution better than 2 nm. Utilizing the imaging scanning unit, the system effectively performs target spectral imaging under single and multiple wavelength conditions at 1064 nm, 1310 nm, and 1520 nm. By optimizing algorithms for data processing, the system can achieve rapid and accurate spectral detection and imaging even under low-light conditions where the average photon count per pixel is less than 3. The results of this study are expected to provide strong technical support for the application of spectral imaging technology in the field of high-speed detection and imaging.

Sparse-View Spectral CT Reconstruction Based on Tensor Decomposition and Total Generalized Variation

Spectral computed tomography (CT)-reconstructed images often exhibit severe noise and artifacts, which compromise the practical application of spectral CT imaging technology. Methods that use tensor dictionary learning (TDL) have shown superior performance, but it is difficult to obtain a high-quality pre-trained global tensor dictionary in practice. In order to resolve this problem, this paper develops an algorithm called tensor decomposition with total generalized variation (TGV) for sparse-view spectral CT reconstruction. In the process of constructing tensor volumes, the proposed algorithm utilizes the non-local similarity feature of images to construct fourth-order tensor volumes and uses Canonical Polyadic (CP) tensor decomposition instead of pre-trained tensor dictionaries to further explore the inter-channel correlation of images. Simultaneously, introducing the TGV regularization term to characterize spatial sparsity features, the use of higher-order derivatives can better adapt to different image structures and noise levels. The proposed objective minimization model has been addressed using the split-Bregman algorithm. To assess the performance of the proposed algorithm, several numerical simulations and actual preclinical mice are studied. The final results demonstrate that the proposed algorithm has an enormous improvement in the quality of spectral CT images when compared to several existing competing algorithms.

Discriminating Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Review

Hyperspectral images (HSIs) contain subtle spectral details and rich spatial contextures of land cover that benefit from developments in spectral imaging and space technology. The classification of HSIs, which aims to allocate an optimal label for each pixel, has broad prospects in the field of remote sensing. However, due to the redundancy between bands and complex spatial structures, the effectiveness of the shallow spectral–spatial features extracted by traditional machine-learning-based methods tends to be unsatisfying. Over recent decades, various methods based on deep learning in the field of computer vision have been proposed to allow for the discrimination of spectral–spatial representations for classification. In this article, the crucial factors to discriminate spectral–spatial features are systematically summarized from the perspectives of feature extraction and feature optimization. For feature extraction, techniques to ensure the discrimination of spectral features, spatial features, and spectral–spatial features are illustrated based on the characteristics of hyperspectral data and the architecture of models. For feature optimization, techniques to adjust the feature distances between classes in the classification space are introduced in detail. Finally, the characteristics and limitations of these techniques and future challenges in facilitating the discrimination of features for HSI classification are also discussed further.

Photon Counting Detector Computed Tomography in Pediatric Cardiothoracic CT Imaging

Abstract Photon-counting-detector computed tomography (PCD-CT) is the most recent advancement in CT technology and has the potential to change clinical practice. Unlike conventional energy-integrated-detector (EID) that uses a two-step process to convert x-rays into a digital signal, PCD-CT directly converts photon energies into electronic signal. Advantages of PCD-CT over EID-CT are higher spatial resolution, electronic noise reduction, higher contrast-to-noise ratio, improved radiation dose efficiency and intrinsic spectral imaging. Successful implementation into clinical practice requires adaptations in CT protocols. In this review, we summarize basic principles of PCD-CT and technical scanning factors followed by a discussion of its clinical benefits in pediatric pulmonary and cardiovascular imaging.

Employing total internal reflection AF spectral imaging and Raman spectroscopy for margin assessment: An innovative technological approach

Employing total internal reflection AF spectral imaging and Raman spectroscopy for margin assessment: An innovative technological approach

Biosecurity risks to human food supply associated with plant-parasitic nematodes.

Biosecurity in agriculture is essential for preventing the introduction and spread of plant-parasitic nematodes (PPNs) which threaten global food security by reducing crop yields and facilitating disease spread. These risks are exacerbated by increased global trade and climate change, which may alter PPN distribution and activity, increasing their impact on agricultural systems. Addressing these challenges is vital to maintaining the integrity of the food supply chain. This review highlights significant advancements in managing PPN-related biosecurity risks within the food supply chain, particularly considering climate change's evolving influence. It discusses the PPN modes of transmission, factors increasing the risk of infestation, the impact of PPNs on food safety and security, and traditional and emerging approaches for detecting and managing these pests. Literature suggests that implementing advanced biosecurity measures could decrease PPN infestation rates by up to 70%, substantially reducing crop yield losses and bolstering food security. Notably, the adoption of modern detection and management techniques, (molecular diagnostics and integrated pest management) and emerging geospatial surveillance and analysis systems (spectral imaging, change-detection analysis) has shown greater effectiveness than traditional methods. These innovations offer promising avenues for enhancing crop health and securing the food supply chain against environmental shifts. The integration of these strategies is crucial, demonstrating the potential to transform biosecurity practices and sustain agricultural productivity in the face of changing climatic conditions. This analysis emphasizes the importance of adopting advanced measures to protect crop health and ensure food supply chain resilience, providing valuable insights for stakeholders across the agricultural sector.

Snapshot spectral imaging based on aberration model-driven deep learning

Snapshot spectral imaging based on aberration model-driven deep learning

Automated and non-destructive estimation of soluble solid content of tomatoes on the plant under variable light conditions

In this study, a method is proposed to deal with the variable light conditions in a greenhouse to non-destructively predict the soluble solid content (SSC) of tomatoes on the plant. It was investigated how well the SSC – measured as °Brix – of tomatoes could be predicted based on spectral data in the range of 470–900 nm, where data acquired in situ (in the greenhouse) was compared to post-harvest data captured under controlled laboratory conditions. To deal with the variation in illumination in the greenhouse, a dynamic-calibration method is proposed, using a grey reference in the image. Ground-truth SSC data of the tomatoes was acquired using a refractometer. Data was collected of three different types of truss tomatoes with a wide range of SSC. Different PLS regression models were then trained on the spectral data in relation to the refractometer values. Trained and tested on all types, the in situ measurements showed a predicted coefficient of determination on the test set, Q2, of 0.95 with a Root Mean Squared Error of Prediction (RMSEP) of 0.29 °Brix using the dynamic calibration, and a Q2 of 0.93 with RMSEP of 0.35 °Brix without using the dynamic-calibration method. The post-harvest measurements resulted in a Q2 of 0.95 with RMSEP of 0.31 °Brix. The results show that spectral imaging using dynamic calibration is applicable for in situ non-destructive prediction of SSC. This method enables high-throughput and non-destructive quality estimation of fruits on the plant in commercial greenhouse conditions.

Vertebral and Femoral Bone Mineral Density (BMD) Assessment with Dual-Energy CT versus DXA Scan in Postmenopausal Females

This study aimed to demonstrate the potential role of dual-energy CT in assessing bone mineral density (BMD) using hydroxyapatite–fat material pairing in postmenopausal women. A retrospective study was conducted on 51 postmenopausal female patients who underwent DXA and DECT examinations for other clinical reasons. DECT images were acquired with spectral imaging using a 256-slice system. These images were processed and visualized using a HAP–fat material pair. Statistical analysis was performed using the Bland–Altman method to assess the agreement between DXA and DECT HAP–fat measurements. Mean BMD, vertebral, and femoral T-scores were obtained. For vertebral analysis, the Bland–Altman plot showed an inverse correlation (R2: −0.042; RMSE: 0.690) between T-scores and DECT HAP–fat values for measurements from L1 to L4, while a good linear correlation (R2: 0.341; RMSE: 0.589) was found for measurements at the femoral neck. In conclusion, we demonstrate the enhanced importance of BMD calculation through DECT, finding a statistically significant correlation only at the femoral neck where BMD results do not seem to be influenced by the overlap of the measurements on cortical and trabecular bone. This outcome could be beneficial in the future by reducing radiation exposure for patients already undergoing follow-up for chronic conditions.

Raman spectroscopy in the study of amyloid formation and phase separation.

Neurodegenerative diseases, such as Alzheimer's and Parkinson's, share a common pathological feature of amyloid structure accumulation. However, the structure-function relationship between these well-ordered, β-sheet-rich, filamentous protein deposits and disease etiology remains to be defined. Recently, an emerging hypothesis has linked phase separation, a process involved in the formation of protein condensates, to amyloid formation, suggesting that liquid protein droplets serve as loci for amyloid initiation. To elucidate how these processes contribute to disease progression, tools that can directly report on protein secondary structural changes are needed. Here, we review recent studies that have demonstrated Raman spectroscopy as a powerful vibrational technique for interrogating amyloid structures; one that offers sensitivity from the global secondary structural level to specific residues. This probe-free technique is further enhanced via coupling to a microscope, which affords structural data with spatial resolution, known as Raman spectral imaging (RSI). In vitro and in cellulo applications of RSI are discussed, highlighting studies of protein droplet aging, cellular internalization of fibrils, and Raman imaging of intracellular water. Collectively, utilization of the myriad Raman spectroscopic methods will contribute to a deeper understanding of protein conformational dynamics in the complex cellular milieu and offer potential clinical diagnostic capabilities for protein misfolding and aggregation processes in disease states.

Spectral imaging based on custom-made multi-strip filter arrays

Spectral imaging technology based on on-chip spectroscopy can find applications in areas including aerospace, industrial and consumer electronics, and so on. Since each application normally requires a different set and number of spectral bands, the development of customized spectroscopy solutions with more compact size and lower cost becomes quite important. In this paper, we demonstrate a compact, highly customizable imaging spectrometer scheme based on custom-made multi-strip filter arrays, which maintains an average high transmission of ∼85%, narrow bandwidth of ∼30nm, and high optical density of ∼OD2 in the blocking regions across the visible to near-infrared waveband. Spectral imaging experiments are conducted, and the accurate reconstruction of sparse spectral image data is demonstrated as well to prove the validity of the proposed scheme. As a result, the work reported in this paper allows researchers to develop customized spectral imaging equipment in a relatively easy way and also has a great potential to be engineered further for scalable production with a quite low cost.

Scanning Micro X-ray Fluorescence and Multispectral Imaging Fusion: A Case Study on Postage Stamps.

Scanning micrο X-ray fluorescence (μ-XRF) and multispectral imaging (MSI) were applied to study philately stamps, selected for their small size and intricate structures. The μ-XRF measurements were accomplished using the M6 Jetstream Bruker scanner under optimized conditions for spatial resolution, while the MSI measurements were performed employing the XpeCAM-X02 camera. The datasets were acquired asynchronously. Elemental distribution maps can be extracted from the μ-XRF dataset, while chemical distribution maps can be obtained from the analysis of the multispectral dataset. The objective of the present work is the fusion of the datasets from the two spectral imaging modalities. An algorithmic co-registration of the two datasets is applied as a first step, aiming to align the multispectral and μ-XRF images and to adapt to the pixel sizes, as small as a few tens of micrometers. The dataset fusion is accomplished by applying k-means clustering of the multispectral dataset, attributing a representative spectrum to each pixel, and defining the multispectral clusters. Subsequently, the μ-XRF dataset within a specific multispectral cluster is analyzed by evaluating the mean XRF spectrum and performing k-means sub-clustering of the μ-XRF dataset, allowing the differentiation of areas with variable elemental composition within the multispectral cluster. The data fusion approach proves its validity and strength in the context of philately stamps. We demonstrate that the fusion of two spectral imaging modalities enhances their analytical capabilities significantly. The spectral analysis of pixels within clusters can provide more information than analyzing the same pixels as part of the entire dataset.

From sensor fusion to knowledge distillation in collaborative LIBS and hyperspectral imaging for mineral identification

Multimodal spectral imaging offers a unique approach to the enhancement of the analytical capabilities of standalone spectroscopy techniques by combining information gathered from distinct sources. In this manuscript, we explore such opportunities by focusing on two well-known spectral imaging techniques, namely laser-induced breakdown spectroscopy, and hyperspectral imaging, and explore the opportunities of collaborative sensing for a case study involving mineral identification. In specific, the work builds upon two distinct approaches: a traditional sensor fusion, where we strive to increase the information gathered by including information from the two modalities; and a knowledge distillation approach, where the Laser Induced Breakdown spectroscopy is used as an autonomous supervisor for hyperspectral imaging. Our results show the potential of both approaches in enhancing the performance over a single modality sensing system, highlighting, in particular, the advantages of the knowledge distillation framework in maximizing the potential benefits of using multiple techniques to build more interpretable models and paving for industrial applications.

Fast kV-switching and dual-layer flat-panel detector enabled cone-beam CT joint spectral imaging.

Fast kV-switching (FKS) and dual-layer flat-panel detector (DL-FPD) technologies have been actively studied as promising dual-energy solutions for FPD-based cone-beam computed tomography (CBCT). However, CBCT spectral imaging is known to face challenges in obtaining accurate and robust material discrimination performance due to the limited energy separation. To further improve CBCT spectral imaging capability, this work aims to promote a source-detector joint spectral imaging solution which takes advantages of both FKS and DL-FPD, and to conduct a feasibility study on the first tabletop CBCT system with the joint spectral imaging capability developed. 
Methods: A noise performance analysis using the Cramér-Rao lower bound (CRLB) method is conducted. The CRLB for basis material after a projection-domain material decomposition is derived, followed by a set of numerical calculations of CRLBs, for the FKS, the DL-FPD and the joint solution, respectively. In this work, the first FKS and DL-FPD jointly enabled multi-energy tabletop CBCT system, to the best of our knowledge, has been developed in our laboratory. To evaluate its spectral imaging performance, a set of physics experiments are conducted, where the multi-energy and head phantoms are scanned using the 80/105/130kVp switching pairs and projection data are collected using a prototype DL-FPD. To compensate for the slightly angular mismatch between the low- and high-energy projections in FKS, a dual-domain projection completion scheme is implemented. Afterwards material decomposition is carried out by using the maximum-likelihood method, followed by reconstruction of basis material and virtual monochromatic images. 
Results: The numerical simulations show that the joint solution can lead to a significant improvement in energy separation and lower noise levels. The physics experiments confirmed the feasibility and superiority of the joint spectral imaging, whose CNRs of the multi-energy phantom were boosted by an average improvement of 21.9%, 20.4% for water and 32.8%, 62.8% for iodine when compared with that of the FKS and DL-FPD in fan-beam and cone-beam experiments, respectively. 
Conclusions: A feasibility study of the joint spectral imaging for CBCT by utilizing both the FKS and DL-FPD was conducted, with the first tabletop CBCT system having such a capability being developed, which exhibits improved spectral imaging performance as expected.

Diagnosis of acute pulmonary embolism: when photon-counting-detector CT replaces energy-integrating-detector CT in daily routine.

To compare the diagnostic approach of acute pulmonary embolism (PE) with photon-counting-detector CT (PCD-CT) and energy-integrating-detector CT (EID-CT). Two cohorts underwent CT angiographic examinations with EID-CT (Group 1; n = 158) and PCD-CT (Group 2; n = 172), (b) with two options in Group 1, dual energy (Group 1a) or single energy (Group 1b) and a single option in Group 2 (spectral imaging with single source). In Group 2, all patients benefited from spectral imaging, only accessible to 105 patients (66.5%) in Group 1, with a mean acquisition time significantly shorter (0.9 ± 0.1 s vs 4.0 ± 0 .3 s; p < 0.001) and mean values of CTDIvol and DLP reduced by 46.3% and 47.7%, respectively. Comparing the quality of 70 keV (Group 2) and averaged (Group 1a) images: (a) the mean attenuation within pulmonary arteries did not differ (p = 0.13); (b) the image noise was significantly higher (p < 0.001) in Group 2 with no difference in subjective image noise (p = 0.29); and (c) 89% of examinations were devoid of artifacts in Group 2 vs 28.6% in Group 1a. The percentage of diagnostic examinations was 95.2% (100/105; Group 1a), 100% (53/53; Group 1b), and 95.3% (164/172; Group 2). There were 4.8% (5/105; Group 1a) and 4.7% (8/172; Group 2) of non-diagnostic examinations, mainly due to the suboptimal quality of vascular opacification with the restoration of a diagnostic image quality on low-energy images. Compared to EID-CT, morphology and perfusion imaging were available in all patients scanned with PCD-CT, with the radiation dose reduced by 48%. PCD-CT enables scanning patients with the advantages of both spectral imaging, including high-quality morphologic imaging and lung perfusion for all patients, and fast scanning-a combination that is not simultaneously accessible with EID-CT while reducing the radiation dose by almost 50%.

Identifying frequency de-correlated dust residuals in B-mode maps by exploiting the spectral capability of bolometric interferometry

Astrophysical polarized foregrounds represent the most critical challenge in cosmic microwave background (CMB) $B$-mode experiments, requiring multifrequency observations to constrain astrophysical foregrounds and isolate the CMB signal. However, recent observations indicate that foreground emission may be more complex than anticipated. Not properly accounting for these complexities during component separation can lead to a bias in the recovered tensor-to-scalar ratio. In this paper we investigate how the increased spectral resolution provided by band-splitting in bolometric interferometry (BI) through a technique called spectral imaging can help control the foreground contamination in the case of an unaccounted-for Galactic dust frequency de-correlation along the line of sight (LOS). We focused on the next-generation ground-based CMB experiment CMB-S4 and compared its anticipated sensitivity, frequency, and sky coverage with a hypothetical version of the same experiment based on BI (CMB-S4/BI). We performed a Monte Carlo analysis based on parametric component separation methods (FGBuster and Commander) and computed the likelihood of the recovered tensor-to-scalar ratio, $r$. The main result is that spectral imaging allows us to detect systematic uncertainties on $r$ from frequency de-correlation when this effect is not accounted for in the component separation. Conversely, an imager such as CMB-S4 would detect a biased value of $r$ and would be unable to spot the presence of a systematic effect. We find a similar result in the reconstruction of the dust spectral index, and we show that with BI we can more precisely measure the dust spectral index when frequency de-correlation is present and not accounted for in the component separation. The in-band frequency resolution provided by BI allows us to identify dust LOS frequency de-correlation residuals where an imager with a similar level of performance would fail. This creates the possibility of exploiting this potential in the context of future CMB polarization experiments that will be challenged by complex foregrounds in their quest for $B$-mode detection.

Spectral line confocal sensor signal analysis and correction: Unlocking reflectance difference sample measurements

Spectral line confocal imaging (LCI) technology enables high-resolution, rapid 3D surface morphology analysis of transparent materials and other samples, positioning it as a leading optical measurement tool. Nevertheless, where the sensor measures samples with significant variations in surface reflectance, the peak signals of the acquired images exhibit low signal-to-noise ratios or become distorted, hampering the peak extraction algorithm from accurately locating their peak positions. Consequently, it's arduous to reconstruct their precise surface topography. To enhance the sensor's dynamic measurement range and adaptability, this paper introduces an adaptive correction approach. It utilizes a regularized non-negative matrix to decompose images with various grey levels. This is followed by weighted frequency filtering and Gaussian enhancement of all the grey layers. Eventually, these layers are fused into one image with high contrast and uniformity. Using this approach, the sensor is capable of achieving precise 3D reconstruction with high-quality peak signals. Additionally, this study experimentally demonstrates the efficacy of the suggested approach by measuring the interdigital electrode and printed circuit boards (PCB). It should be noted that the technique presented in this paper is not limited to spectral line confocal instruments and is also effective for line laser and line structured light instruments.

Incoherent partial superposition modeling for single-shot angle-resolved ellipsometry measurement of thin films on transparent substrates

Ellipsometric measurement of transparent samples suffers from substrate backside reflection challenges, including incoherent and partial superposition issues. The recently developed angle-resolved ellipsometry (ARE) can naturally eliminate the backside reflections of substrates with a micro-spot equivalent thickness or thicker; however, for thinner substrates, ARE working with general incoherent backside reflection models shows significant inaccuracy or measurement failure. In this paper, an incoherent partial superposition (IPS) model is proposed to characterize the optical superposition effect between the frontside and uncertain backside reflections from an unknown substrate. IPS introduces a cosine-like correction of the backside reflection, corresponding to the overlapping-area change of backside and frontside reflections along with incident angles. Benefiting from ARE’s wide-angle spectral imaging capability, IPS achieves single-shot measurement of thin film thicknesses on transparent substrates of unknown thickness. An ARE system was built and calibrated regarding the linear relationship between the cosine-corrected angular frequencies and substrate thicknesses. Then, commercial ITO films on glasses of different thicknesses ranging from 200 to 1000 µm were measured. Experimental results show that IPS-ARE results in a root-mean-square accuracy error of ∼1 nm in film thickness measurement and provides a ∼77% error reduction from general incoherent backside reflection models.

Development of a large volume line scanning, high spectral range and resolution 3D hyperspectral photoluminescence imaging microscope for diamond and other high refractive index materials

Hyperspectral photoluminescence (PL) imaging is a powerful technique that can be used to understand the spatial distribution of emitting species in many materials. Volumetric hyperspectral imaging of weakly emitting color centers often necessitates considerable data collection times when using commercial systems. We report the development of a line-scanning hyperspectral imaging microscope capable of measuring the luminescence emission spectra for diamond volumes up to 2.20 × 30.00 × 6.30 mm with a high lateral spatial resolution of 1–3 µm. In an single X-λ measurement, spectra covering a 711 nm range, in a band from 400–1100 nm, with a spectral resolution up to 0.25 nm can be acquired. Data sets can be acquired with 723 (X) × 643 (Y) × 1172 (λ) pixels at a rate of 6 minutes/planar image slice, allowing for volumetric hyperspectral imaging with high sampling. This instrument demonstrates the ability to detect emission from several different color centers in diamond both at the surface and internally, providing a non-destructive method to probe their 3D spatial distribution, and is currently not achievable with any other commonly used system or technique.