Spectral Scanning Research Articles

High range resolution spectral-scanning LiDAR based on optical frequency-domain reflectometry.

Spectral scanning, which utilizes the dispersive effect of light, is a simple and robust method for solid-state beam steering in light detection and ranging (LiDAR) applications. Powered by a tunable laser source, optical frequency-domain reflectometry (OFDR) is a high-precision measurement scheme that is inherently compatible with spectral scanning. Here, we propose a spectral-scanning LiDAR based on OFDR technology and demonstrate that, by connecting the measured spectral reflectivity and group delay of the targets with the dispersion equation, their cloud point data can be obtained. Moreover, compared to the spectral-scanning LiDAR based on the frequency-modulated continuous-wave (FMCW) ranging method, our proposed LiDAR scheme offers a more than tenfold improvement in range resolution with a large number of angular pixels. This enhancement enables high-resolution 3D imaging along both the angular and range axes.

Total retinal thickness is an important factor in evaluating diabetic retinal neurodegeneration

ObjectiveMacular retinal nerve fibre layer (mRNFL) and ganglion cell-inner plexiform layer thickness (GC-IPL) measurements are important markers of diabetic retinal neurodegeneration (DRN). In this cross-sectional study, we aimed to quantify...

Coronary atherosclerotic plaque burden and characterization by spectral computed tomography scan in patients with high lipoprotein a and non-hemodynamically significant stenosis

Abstract Introduction Atherosclerotic coronary disease increases morbidity and mortality worldwide. Lipoprotein A (LpA) has emerged as a cardiovascular risk marker, but little is known of its direct influence in plaque volume and composition. Spectral computed tomography (CT) technology allows to characterize coronary plaques with new promising tools. Purpose To evaluate the association of LpA with the extent and composition of non-significant coronary plaques producing stenosis <50%. Methods We prospectively recruited 109 patients referred for noninvasive CT angiography (November 2023-January 2024) with suspected coronary artery disease. 20 had totally/partially non-calcified, non-significant coronary plaques. Plaques were segmented and volume was measured. Their composition was stablished as calcified (>200 UH), lipidic (<60 UH) and fibrous (60-200 UH). Spectral data-based plaque parameters were analyzed, including radiological attenuation on polyenergetic images (CTPI), on virtual low/high monoenergetic images (CT40KeV and CT140KeV, respectively), variation in attenuation across monoenergetic levels (slope40-140 KeV) and Z-effective value. CT scan images were acquired with a 128 detectors dual-layer spectral scan with prospective-retrospective/synchronization. Intravascular iodine-based contrast was administered. LpA was stablished as high (≥25 mg/dL) or low (<25 mg/dL). Results Mean age was 60 ± 11. Cardiovascular risk factor were equally distributed between groups. We analyzed 33 plaques. Total plaque volume was significantly higher in high LpA patients (125.9 mm3 vs 62.1 mm3). There were no differences in plaque composition by LpA (Table). Additionally, spectral data showed significantly higher plaque attenuation on CT140KeV images in high LpA patients (36.07 vs 21.32 HU; p = 0.05). There were no differences for the rest of spectral parameters. Conclusions Plaque volume was higher in patients with non-significant atherosclerotic disease and high LpA, but these plaques showed similar composition in high and low LpA patients. These patients might, therefore, respond to the same conventional treatment event though there is not specific treatment for high LpA yet. Additionally, spectral CT140KeV images could add more precision differentiating these plaques since radiological attenuation in these reconstructions is higher in patients with high LpA.TableFigure

Toward the Application of Dual-Energy Computed Tomography with Virtual Non-Hydroxyapatite Color-Coded Maps to Identify Traumatic Fractures in Daily Emergency Settings

To evaluate the advantages of dual-energy computed tomography (DECT) virtual non-hydroxyapatite color mapping (VNHAP) in combination with standard bone CT (BCT) in the identification of subtle or occult traumatic fractures referred to emergency and acceptance departments (DEAs). Forty patients (22 men; mean age 83 ± 23.7 y) with suspected traumatic fractures referred to our emergency department and examined with a fast kilovoltage-switching single-source spectral CT scan between January and October 2023 were retrospectively reviewed. The BCT and VNHAP images were blindly evaluated by two radiologists with >10 years and <2 years of experience in musculoskeletal imaging. Both techniques were evaluated in terms of sensitivity (SE), specificity (SP), positive and negative predictive values (PPVs and NPVs) and accuracy for fracture detection, as confirmed at a 3-month clinical–instrumental follow-up. Inter-observer agreement and examination times were also analyzed. Fractures were confirmed in 18/40 cases. The highest values of diagnostic performance for VNHAP images were obtained in terms of SP (90.9% and 95%) and PPV (87.5% and 92.8%) and for the less experienced operator. No statistically significant differences were observed between the diagnostic accuracy of the two readers in the evaluation of VNHAP images. Inter-observer agreement was moderate (κ = 0.536) for BCT and substantial (κ = 0.680) for VNHAP. Comparing the two operators, a significantly longer examination time for BCT and no significant difference for VNHAP were registered. Our preliminary experience may encourage the employment of VNHAP maps in combination with BCT images in emergency settings. Their use could be time-saving and valuable in terms of diagnostic performance, especially for less experienced operators.

Comparison of the diagnostic efficacy between imaging features and iodine density values for predicting microvascular invasion in hepatocellular carcinoma.

In recent years, studies have confirmed the predictive capability of spectral computer tomography (CT) in determining microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Discrepancies in the predicted MVI values between conventional CT imaging features and spectral CT parameters necessitate additional validation. In this retrospective study, 105 cases of small HCC were reviewed, and participants were divided into MVI-negative (n=53, Male:48 (90.57%); mean age, 59.40 ± 11.79 years) and MVI-positive (n=52, Male:50(96.15%); mean age, 58.74 ± 9.21 years) groups using pathological results. Imaging features and iodine density (ID) obtained from three-phase enhancement spectral CT scans were gathered from all participants. The study evaluated differences in imaging features and ID values of HCC between two groups, assessing their diagnostic accuracy in predicting MVI occurrence in HCC patients. Furthermore, the diagnostic efficacy of imaging features and ID in predicting MVI was compared. Significant differences were noted in the presence of mosaic architecture, nodule-in-nodule architecture, and corona enhancement between the groups, all with p-values < 0.001. There were also notable disparities in IDs between the two groups across the arterial phase, portal phase, and delayed phases, all with p-values < 0.001. The imaging features of nodule-in-nodule architecture, corona enhancement, and nonsmooth tumor margin demonstrate significant diagnostic efficacy, with area under the curve (AUC) of 0.761, 0.742, and 0.752, respectively. In spectral CT analysis, the arterial, portal, and delayed phase IDs exhibit remarkable diagnostic accuracy in detecting MVI, with AUCs of 0.821, 0.832, and 0.802, respectively. Furthermore, the combined models of imaging features, ID, and imaging features with ID reveal substantial predictive capabilities, with AUCs of 0.846, 0.872, and 0.904, respectively. DeLong test results indicated no statistically significant differences between imaging features and IDs. Substantial differences were noted in imaging features and ID between the MVI-negative and MVI-positive groups in this study. The ID and imaging features exhibited a robust diagnostic precision in predicting MVI. Additionally, our results suggest that both imaging features and ID showed similar predictive efficacy for MVI.

Rapid quality control for recycled coarse aggregates (RCA) streams: Multi-sensor integration for advanced contaminant detection

Recycling coarse aggregates from construction and demolition waste is essential for sustainable construction practices. However, the quality of recycled coarse aggregates (RCA) often fluctuates significantly, in contrast to the more stable quality of natural aggregates. Contaminants in RCA notably compromise its quality and usability. Therefore, automating the quality control of RCA is necessary for the recycling industry. This study introduces an industry-focused, innovative, and rapid quality control system that combines Laser-Induced Breakdown Spectroscopy (LIBS) with 3D scanning technologies to enhance the detection of contaminants in RCA streams. The system involves a synchronized application of LIBS for spectral analysis and 3D scanning for the physical characterization of different materials. Results reveal that the dependability of single-shot LIBS analysis has been enhanced, thus elevating the precision of contaminant detection. This improvement is achieved by accounting for the laser shot's angle of incidence and focal length adjustments. The introduced technology holds potential for application in the real-time examination of substantial volumes of RCA, facilitating a rapid and reliable quality control method. This rapid assessment technique delivers online data about the concentration of contaminants in RCA, including recycled fine aggregates, cement paste, bricks, foam, glass, gypsum, mineral fibers, plastics, and wood. This data is both essential and sufficient for choosing a cost-effective mortar recipe and guaranteeing the performance of the final concrete product in terms of strength and durability in construction projects. The system can monitor the quality of RCA flows at throughputs of 50 tons per hour per conveyor, characterizing approximately 4000 particles in every ton of RCA, in this way signaling the most critical contaminants at levels of less than 50 parts per million. With these characteristics, the system could also become relevant for other applications, such as characterizing mining waste or solid biofuels for power plants.

A-115 Iron assay improvement: The need for thiourea addition in Ferene/Ferrozine reagent cartridge

Abstract Background Iron assay is often ordered along with ferritin, transferrin, and iron binding capacity in patients suspected of having iron deficiency anemia or iron overload. A ferene/Ferrozine chromogen-based iron assay is more common in College of American Pathology accredited laboratories across U.S. Recently, a product recall/field safety notice was issued by a major manufacturer for several lots of ferene based iron reagent due to unacceptable positive bias in patient iron results. However, the reason(s) underlying this positive bias was not provided. In this report, we investigated the possible causes for the recall of specific lots of iron assay reagents. Methods The performance of 2 lots of recalled iron test kits was compared against 2 good lots (Sentinel Diagnostics) on the Alinity c analyzer utilizing approved calibrator lots, quality control (QC) materials (Bio-Rad Laboratories), and excess/leftover de-identified patient samples. Spectral scan (in the visible wavelength) of good and recalled lots of reagents, R1 and R2 was performed on the Tecan microplate reader. Trace elements in R1 and R2 reagents were estimated with Quantitative Inductively Coupled Plasma-Mass Spectrometry (ICP-MS). Spiking experiments in calibrator, discarded patient samples, and reagents were undertaken to further elucidate the cause and prevention of positive bias seen with recalled reagent lots. Graphs and statistical analysis were generated using Graphpad prism 10.1. Results Iron levels in QC materials and de-identified patient samples (n=42) tested with 2 recalled lot of reagents on Alinity c analyzer showed a positive bias of 15-36% compared to unimpacted lots of reagents. Calibrator and blank absorbance of recalled lots was much higher than the unimpacted lots. Percentage change in the basal absorbance of recalled vs unimpacted R2 reagent measured from 500 to 600 nm in 25 nm increments and corrected to 700 nm ranged from 150 to 250%. No such changes were noted with both R1 reagents. Interestingly, ICP-MS heavy metal panel analysis revealed that R2 reagent of the recalled lots had unanticipated presence of Cu (∼22 υg/dL) and lead (7.5 υg/L), which was not present in R1 reagents. Increased concentrations of Cu spiking (100, 200, and 400 υγ/dL) in R2 reagent from recalled lots discernibly increased the Cal Factor values as well as patient iron results relative to unspiked reagent. Further, the presence of Cu over time deteriorated the accuracy of R2 reagent reflecting on very high rise in Cal Factor and patient iron values. Interestingly, TU addition to R2 reagent from unimpacted lot prevented the Cu-induced spurious rise in Cal Factor and iron measurements in the patient samples. Further, TU exhibited sustained preventive effect on Cu-induced iron measurement changes over time. Conclusions Based on our investigation, we conclude that copper contamination of R2 reagent is the possible cause for the positive bias in the recalled iron assay reagent lots. We recommend the addition of TU to Ferene containing R2 reagent pack as a simple and effective step to prevent Cu-induced false elevation in iron values.

Transportable hyperspectral imaging setup based on fast, high-density spectral scanning for in situ quantitative biochemical mapping of fresh tissue biopsies.

Histopathological examination of surgical biopsies, such as in glioma and glioblastoma resection, is hindered in current clinical practice by the long time required for the laboratory analysis and pathological screening, typically taking several days or even weeks to be completed. We propose here a transportable, high-density, spectral scanning-based hyperspectral imaging (HSI) setup, named HyperProbe1, that can provide in situ, fast biochemical analysis, and mapping of fresh surgical tissue samples, right after excision, and without the need for fixing, staining nor compromising the integrity of the tissue properties. HyperProbe1 is based on spectral scanning via supercontinuum laser illumination filtered with acousto-optic tunable filters. Such methodology allows the user to select any number and type of wavelength bands in the visible and near-infrared range between 510 and 900nm (up to a maximum of 79) and to reconstruct 3D hypercubes composed of high-resolution (4 to ), widefield images ( ) of the surgical samples, where each pixel is associated with a complete spectrum. The HyperProbe1 setup is here presented and characterized. The system is applied to 11 fresh surgical biopsies of glioma from routine patients, including different grades of tumor classification. Quantitative analysis of the composition of the tissue is performed via fast spectral unmixing to reconstruct the mapping of major biomarkers, such as oxy-( ) and deoxyhemoglobin (HHb), as well as cytochrome-c-oxidase (CCO). We also provided a preliminary attempt to infer tumor classification based on differences in composition in the samples, suggesting the possibility of using lipid content and differential CCO concentrations to distinguish between lower and higher-grade gliomas. A proof of concept of the performances of HyperProbe1 for quantitative, biochemical mapping of surgical biopsies is demonstrated, paving the way for improving current post-surgical, histopathological practice via non-destructive, in situ streamlined screening of fresh tissue samples in a matter of minutes after excision.

The impact of deep learning image reconstruction of spectral CTU virtual non contrast images for patients with renal stones

PurposeTo compare image quality and detection accuracy of renal stones between deep learning image reconstruction (DLIR) and Adaptive Statistical Iterative Reconstruction-Veo (ASIR-V) reconstructed virtual non-contrast (VNC) images and true non-contrast (TNC) images in spectral CT Urography (CTU). MethodsA retrospective analysis was conducted on images of 70 patients who underwent abdominal-pelvic CTU in TNC phase using non-contrast scan and contrast-enhanced corticomedullary phase (CP) and excretory phase (EP) using spectral scan. The TNC scan was reconstructed using ASIR-V70 % (TNC-AR70), contrast-enhanced scans were reconstructed using AR70, DLIR medium-level (DM), and high-level (DH) to obtain CP-VNC-AR70/DM/DH and EP-VNC-AR70/DM/DH image groups, respectively. CT value, image quality and kidney stones quantification accuracy were measured and compared among groups. The subjective evaluation was independently assessed by two senior radiologists using the 5-point Likert scale for image quality and lesion visibility. ResultsDH images were superior to AR70 and DM images in objective image quality evaluation. There was no statistical difference in the liver and spleen (both P > 0.05), or within 6HU in renal and fat in CT value between VNC and TNC images. EP-VNC-DH had the lowest image noise, highest SNR, and CNR, and VNC-AR70 images had better noise and SNR performance than TNC-AR70 images (all p < 0.05). EP-VNC-DH had the highest subjective image quality, and CP-VNC-DH performed the best in lesion visibility. In stone CT value and volume measurements, there was no statistical difference between VNC and TNC (P > 0.05). ConclusionThe DLIR-reconstructed VNC images in CTU provide better image quality than the ASIR-V reconstructed TNC images and similar quantification accuracy for kidney stones for potential dose savings.The study highlights that deep learning image reconstruction (DLIR)-reconstructed virtual non-contrast (VNC) images in spectral CT Urography (CTU) offer improved image quality compared to traditional true non-contrast (TNC) images, while maintaining similar accuracy in kidney stone detection, suggesting potential dose savings in clinical practice.

Influence of tree age on the wood color and compounds involved in the “golden-thread” wood color formation in Phoebe zhennan

Wood color plays a key role in the quality and commercial value of wood products. However, the mechanism underlying wood color formation has yet to be fully elucidated. The principal objective of this study was to determine the effects of tree age on the wood color and identify the key metabolites correlated with the golden-thread wood color formation in Phoebe zhennan. The results indicated that the golden-thread wood color of P. zhennan was significantly affected by tree age. Four polar organic solvents and water were tested for the extraction of the metabolites, and ethyl acetate was determined to be the most suitable solvent for the extraction of the golden-thread wood color-related metabolites. Based on the experimental data from the UVVIS-NIR spectral scanning and comparative metabolomics, 66 differentially accumulated metabolites were significantly enriched in older wood, and among these, 46 metabolites (e.g., prenol lipids, carboxylic acids and their derivatives, steroids and their derivatives, and benzene and its substituted derivatives) were closely related to the golden-thread wood color formation (r = 0.526–0.944, P < 0.05). In addition, compared to younger wood (10 years old), older wood (30 and 80 years old) had greater durability and a stronger fragrance because of the enrichment of sesquiterpenoids in older wood. In this study, the effects of tree age on P. zhennan wood color, fragrance, and durability were determined through the alterations in the composition and contents of the related secondary metabolites. These findings contribute to a better understanding of the special wood color formation and can be used for a wide range of applications in the wood industry, from quality control to marketing and product development.

Recent improvements in the novel approach to the fast detection of surface contaminations

Here, we present the idea behind a novel measurement system and the recent improvements in detecting surface contaminations using a tunable, very fast three-core quantum cascade laser (QCL) system. It is developed as a potential replacement for the old established double-wheel sampling system currently used to detect surface threats caused by hazardous, non-volatile chemical substances. The novel system design is based on an optical measurement principle, allowing standoff detection and identification, thus rendering ground contact unnecessary. One of the leading military requirements for a CBRN reconnaissance vehicle is a high marching speed; hence, this new system is designed to have an ultra-short acquisition time and an adequate spectral bandwidth for measurements in the mid-infrared (MIR). This innovative approach may replace the more cumbersome indirect detection techniques. We discuss the main advantages and evaluate possible application scenarios that we expect from this setup. Active IR backscatter spectroscopy generally permits the contactless identification of hazardous chemical substances on surfaces. Here, the design was optimized around an approximate measuring distance of decimeters. Using a monoaxial setup, the autofocus is less demanding than alternative approaches and even allows for rapid measurements of rough surfaces. We built the improved measurement system based on novel QCL modules that integrate micro-opto-electro-mechanical grating scanners (MOEMS) in an external cavity. These elaborate laser modules provide almost one kilohertz (kHz) spectral scan speeds and typically cover a spectral range of 200–300 cm-1, respectively. Furthermore, such modules achieve measurement times of as short as one millisecond per spectrum. The full spectral coverage of the system is realized by coupling several of such modules.

Towards an ultrafast 3D imaging scanning LiDAR system: a review

Light detection and ranging (LiDAR), as a hot imaging technology in both industry and academia, has undergone rapid innovation and evolution. The current mainstream direction is towards system miniaturization and integration. There are many metrics that can be used to evaluate the performance of a LiDAR system, such as lateral resolution, ranging accuracy, stability, size, and price. Until recently, with the continuous enrichment of LiDAR application scenarios, the pursuit of imaging speed has attracted tremendous research interest. Particularly, for autonomous vehicles running on motorways or industrial automation applications, the imaging speed of LiDAR systems is a critical bottleneck. In this review, we will focus on discussing the upper speed limit of the LiDAR system. Based on the working mechanism, the limitation of optical parts on the maximum imaging speed is analyzed. The beam scanner has the greatest impact on imaging speed. We provide the working principle of current popular beam scanners used in LiDAR systems and summarize the main constraints on the scanning speed. Especially, we highlight the spectral scanning LiDAR as a new paradigm of ultrafast LiDAR. Additionally, to further improve the imaging speed, we then review the parallel detection methods, which include multiple-detector schemes and multiplexing technologies. Furthermore, we summarize the LiDAR systems with the fastest point acquisition rate reported nowadays. In the outlook, we address the current technical challenges for ultrafast LiDAR systems from different aspects and give a brief analysis of the feasibility of different approaches.

Recent Recall of Iron Reagent-Investigation of Potential Reagent Contamination and Assay Improvement Strategy.

Recently, a major manufacturer recalled several lots of iron assay reagent due to positive bias of roughly 15%-30% and the cause remains unknown. This study investigated the root cause of this positive bias and evaluated a simple practical approach to improve the assay. Performance comparison of recalled and unimpacted iron assay kits was done utilizing calibrators, quality control (QC) materials, and 42 remnant patient samples. Spectral scan and trace elements analysis of R1 and R2 reagents was performed. Copper (Cu) and thiourea (TU) spiking experiments were utilized to elucidate the cause and prevention of positive bias seen with recalled lots. Iron measurements in QC materials and patient samples using recalled reagents generated a positive bias of 17.5% and 21%, respectively. Correspondingly, the recalled R2 reagents, but not R1, showed a rise in basal absorbance along with an unanticipated presence of Cu (22.7 µg/dL) and lead (7.5 µg/L). Cu spiking to recalled and unimpacted R2 reagent intensified the reagent color besides falsely increasing its absorbance, calibration factor, and patient iron measurements. Interestingly, addition of TU (65 mmol/L) to R2 reagent from unimpacted lot prevented the short-term and prolonged Cu-induced spurious rise in calibration factor and patient iron estimations. We conclude that accidental copper contamination of R2 reagent during manufacturing could be a reason underlying the positive bias in the recalled iron reagent lots. Addition of TU in ferene-containing R2 reagent is a simple and effective means to prevent Cu-induced false elevation in iron values.

Mid-infrared Doppler-free saturation absorption spectroscopy of the Q branch of CH4ν3 = 1 band using a rapid-scanning continuous-wave optical parametric oscillator.

We have developed a mid-infrared Doppler-free saturation absorption spectroscopy apparatus that employs a commercial continuous-wave optical parametric oscillator (CW OPO), complemented by a home-built automation and wavelength scanning system. Here, we report a comprehensive spectral scan of the Q branch transitions of the ν3 = 1 band of methane (CH4) with an average linewidth (FWHM) of 4.5 MHz. The absolute frequency calibration was achieved using previously reported transition frequencies determined using optical frequency combs, while a Fabry-Perot etalon was used for the relative frequency calibration. We report 15 transitions with improved accuracies of 1.13 MHz (3.76 × 10-5 cm-1).

Unveiling the True Nature of Plasma Dynamics from the Reference Frame of a Superpenumbral Fibril

The magnetic geometry of the solar atmosphere, combined with projection effects, makes it difficult to accurately map the propagation of ubiquitous waves in fibrillar structures. These waves are of interest due to their ability to carry energy into the chromosphere and deposit it through damping and dissipation mechanisms. To this end, the Interferometric Bidimensional Spectrometer at the Dunn Solar Telescope was employed to capture high-resolution Hα spectral scans of a sunspot, with the transverse oscillations of a prominent superpenumbral fibril examined in depth. The oscillations are reprojected from the helioprojective Cartesian frame to a new frame of reference oriented along the average fibril axis through nonlinear force-free field extrapolations. The fibril was found to be carrying an elliptically polarized, propagating kink oscillation with a period of 430 s and a phase velocity of 69 ± 4 km s−1. The oscillation is damped as it propagates away from the sunspot with a damping length of approximately 9.2 Mm, resulting in the energy flux decreasing at a rate on the order of 460 W m−2/Mm. The Hα line width is examined and found to increase with distance from the sunspot, a potential sign of a temperature increase. Different linear and nonlinear mechanisms are investigated for the damping of the wave energy flux, but a first-order approximation of their combined effects is insufficient to recreate the observed damping length by a factor of at least 3. It is anticipated that the reprojection methodology demonstrated in this study will aid with future studies of transverse waves within fibrillar structures.

Generation of virtual monoenergetic images at 40 keV of the upper abdomen and image quality evaluation based on generative adversarial networks

BackgroundAbdominal CT scans are vital for diagnosing abdominal diseases but have limitations in tissue analysis and soft tissue detection. Dual-energy CT (DECT) can improve these issues by offering low keV virtual monoenergetic images (VMI), enhancing lesion detection and tissue characterization. However, its cost limits widespread use.PurposeTo develop a model that converts conventional images (CI) into generative virtual monoenergetic images at 40 keV (Gen-VMI40keV) of the upper abdomen CT scan.MethodsTotally 444 patients who underwent upper abdominal spectral contrast-enhanced CT were enrolled and assigned to the training and validation datasets (7:3). Then, 40-keV portal-vein virtual monoenergetic (VMI40keV) and CI, generated from spectral CT scans, served as target and source images. These images were employed to build and train a CI-VMI40keV model. Indexes such as Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity (SSIM) were utilized to determine the best generator mode. An additional 198 cases were divided into three test groups, including Group 1 (58 cases with visible abnormalities), Group 2 (40 cases with hepatocellular carcinoma [HCC]) and Group 3 (100 cases from a publicly available HCC dataset). Both subjective and objective evaluations were performed. Comparisons, correlation analyses and Bland-Altman plot analyses were performed.ResultsThe 192nd iteration produced the best generator mode (lower MAE and highest PSNR and SSIM). In the Test groups (1 and 2), both VMI40keV and Gen-VMI40keV significantly improved CT values, as well as SNR and CNR, for all organs compared to CI. Significant positive correlations for objective indexes were found between Gen-VMI40keV and VMI40keV in various organs and lesions. Bland-Altman analysis showed that the differences between both imaging types mostly fell within the 95% confidence interval. Pearson’s and Spearman’s correlation coefficients for objective scores between Gen-VMI40keV and VMI40keV in Groups 1 and 2 ranged from 0.645 to 0.980. In Group 3, Gen-VMI40keV yielded significantly higher CT values for HCC (220.5HU vs. 109.1HU) and liver (220.0HU vs. 112.8HU) compared to CI (p < 0.01). The CNR for HCC/liver was also significantly higher in Gen-VMI40keV (2.0 vs. 1.2) than in CI (p < 0.01). Additionally, Gen-VMI40keV was subjectively evaluated to have a higher image quality compared to CI.ConclusionCI-VMI40keV model can generate Gen-VMI40keV from conventional CT scan, closely resembling VMI40keV.

Deep SBP+ 2.0: a physics-driven generation capability enhanced framework to reconstruct a space-bandwidth product expanded image from two image shots

The space-bandwidth product (SBP) limitation makes it difficult to obtain an image with both a high spatial resolution and a large field of view (FoV) through commonly used optical imaging systems. Although FoV and spectrum stitch provide solutions for SBP expansion, they rely on spatial and spectral scanning, which lead to massive image captures and a low processing speed. To solve the problem, we previously reported a physics-driven deep SBP-expanded framework (Deep SBP+) [J. Opt. Soc. Am. A 40, 833 (2023)JOAOD60740-323210.1364/JOSAA.480920]. Deep SBP+ can reconstruct an image with both high spatial resolution and a large FoV from a low-spatial-resolution image in a large FoV and several high-spatial-resolution images in sub-FoVs. In physics, Deep SBP+ reconstructs the convolution kernel between the low- and high-spatial-resolution images and improves the spatial resolution through deconvolution. But Deep SBP+ needs multiple high-spatial-resolution images in different sub-FoVs, inevitably complicating the operations. To further reduce the image captures, we report an updated version of Deep SBP+ 2.0, which can reconstruct an SBP expanded image from a low-spatial-resolution image in a large FoV and another high-spatial-resolution image in a sub-FoV. Different from Deep SBP+, the assumption that the convolution kernel is a Gaussian distribution is added to Deep SBP+ 2.0 to make the kernel calculation simple and in line with physics. Moreover, improved deep neural networks have been developed to enhance the generation capability. Proven by simulations and experiments, the receptive field is analyzed to prove that a high-spatial-resolution image in the sub-FoV can also guide the generation of the entire FoV. Furthermore, we also discuss the requirement of the sub-FoV image to obtain an SBP-expanded image of high quality. Considering its SBP expansion capability and convenient operation, the updated Deep SBP+ 2.0 can be a useful tool to pursue images with both high spatial resolution and a large FoV.

Copper-doped zinc tartrate (Cu-ZnT) nanocrystals: Exploring their physical and antimicrobial properties

This study investigates the growth and properties of pure zinc tartrate (ZnT) and copper-doped zinc tartrate (Cu-ZnT) nanocrystals synthesized via a sol-gel technique using double-distilled water, sodium metasilicate, and tartaric acid. Controlled pH gels were prepared with the subsequent addition of zinc chloride and copper chloride solutions to facilitate nanocrystal formation. The synthesized nanocrystals were characterized using various techniques, including Energy-Dispersive X-ray analysis, Fourier Transform Infrared Spectroscopy, Thermogravimetric analysis, UV–visible-near infrared spectral analysis, X-ray diffraction (XRD), and Field Emission Scanning Electron Microscopy. EDAX confirmed the elemental composition of Zn, Cu, C, and O in the crystals. FTIR spectra revealed the presence of functional groups corresponding to water of crystallization, tartaric acid moieties, and potentially other compounds. TGA analysis indicated a two-stage decomposition process for ZnT and the formation of zinc copper oxide in Cu-ZnT. UV–vis-NIR analysis yielded a low cut-off wavelength of 285 nm for both crystals, suggesting electronic transitions below 350 nm. Both ZnT and Cu-ZnT nanocrystals are transparent in visible and near-infrared light, absorbing strongly in the ultraviolet. Copper doping enhances Cu-ZnT light absorption. While both act as insulators, Cu-ZnT exhibits significantly higher conductivity due to copper doping. XRD analysis estimated an average crystalline size of 42 nm, while FE-SEM images revealed a quasi-spherical morphology. Refractive index, extinction coefficient, and optical bandgap were further determined, highlighting the potential suitability of these nanocrystals for optoelectronic applications. Antibacterial and antifungal assays demonstrated moderate effectiveness against tested microorganisms compared to standard drugs.

Automated identification of pesticide mixtures via machine learning analysis of TLC-SERS spectra

Identification of components in pesticide mixtures has been a major challenge in spectral analysis. In this paper, we assembled monolayer Ag nanoparticles on Thin-layer chromatography (TLC) plates to prepare TLC-Ag substrates with mixture separation and surface-enhanced Raman scattering (SERS) detection. Spectral scans were performed along the longitudinal direction of the TLC-Ag substrate to generate SERS spectra of all target analytes on the TLC plate. Convolutional neural network classification and spectral angle similarity machine learning algorithms were used to identify pesticide information from the TLC-SERS spectra. It was shown that the proposed automated spectral analysis method successfully classified five categories, including four pesticides (thiram, triadimefon, benzimidazole, thiamethoxam) as well as a blank TLC-Ag data control. The location of each pesticide on the TLC plate was determined by the intersection of the information curves of the two algorithms with 100 % accuracy. Therefore, this method is expected to help regulators understand the residues of mixed pesticides in agricultural products and reduce the potential risk of agricultural products to human health and the environment.

Development of a Preoperative Prediction Model Based on Spectral CT to Evaluate Axillary Lymph Node With Macrometastases in Clinical T1/2N0 Invasive Breast Cancer

ObjectivesTo develop a prediction model based on spectral computed tomography (CT) to evaluate axillary lymph node (ALN) with macrometastases in clinical T1/2N0 invasive breast cancer. MethodsA total of 217 clinical T1/2N0 invasive breast cancer patients who underwent spectral CT scans were retrospectively enrolled and categorized into a training cohort (n = 151) and validation cohort (n = 66). These patients were classified into ALN nonmacrometastases (stage pN0 or pN0 [i+] or pN1mi) and ALN macrometastases (stage pN1-3) subgroups. The morphologic criteria and quantitative spectral CT parameters of the most suspicious ALN were measured and compared. Least absolute shrinkage and selection operator (Lasso) was used to screen predictive indicators to build a logistic model. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the models. ResultsThe combined arterial-venous phase spectral CT model yielded the best diagnostic performance in discrimination of ALN nonmacrometastases and ALN macrometastases with the highest AUC (0.963 in the training cohort and 0.945 in validation cohorts). Among single phase spectral CT models, the venous phase spectral CT model showed the best performance (AUC = 0.960 in the training cohort and 0.940 in validation cohorts). There was no significant difference in AUCs among the 3 models (DeLong test, P > .05 for each comparison). ConclusionA Lasso-logistic model that combined morphologic features and quantitative spectral CT parameters based on contrast-enhanced spectral imaging potentially be used as a noninvasive tool for individual preoperative prediction of ALN status in clinical T1/2N0 invasive breast cancers.