Line-scan Imaging Research Articles

Pigment analysis based on a line-scanning fluorescence hyperspectral imaging microscope combined with multivariate curve resolution.

A rapid and cost-effective system is vital for the detection of harmful algae that causes environmental problems in terms of water quality. The approach for algae detection was to capture images based on hyperspectral fluorescence imaging microscope by detecting specific fluorescence signatures. With the high degree of overlapping spectra of algae, the distribution of pigment in the region of interest was unknown according to a previous report. We propose an optimization method of multivariate curve resolution (MCR) to improve the performance of pigment analysis. The reconstruction image described location and concentration of the microalgae pigments. This result indicated the cyanobacterial pigment distribution and mapped the relative pigment content. In conclusion, with the advantage of acquiring two-dimensional images across a range of spectra, HSI conjoining spectral features with spatial information efficiently estimated specific features of harmful microalgae in MCR models.

Corascope: Software assisted XRF scan merging

Due to the physical constraints of X-ray Fluorescence (XRF) scanners, all but the shortest sedimentary cores are typically scanned in a piecewise fashion. Pieces are either extracted a section at a time (e.g. Russian peat corer) or are cut and sectioned after extraction (e.g. piston and gravity cores). The XRF core scanner generates an optical line-scan image, an X-ray radiograph, and a table of downcore variations in elemental composition. The subsequent processes of digitally joining the individual section scans so that they correspond to the original core is typically done in an ad-hoc manner with little visual feedback. Manual manipulation of composition tables while maintaining proper alignment with the optical scans is error prone and slow. As a result, image data are typically either discarded or used as a rough reference. This means a potentially valuable data source is normally lost. Corascope is a first-pass data reconstruction tool for the Itrax XRF scanner that aims to streamline data merging before doing any analysis. Immediate visual feedback is provided during the cropping and merging of core sections; it automatically preserves optical and elemental data alignment. It further provides a statistically-guided set of tools for adjusting overlapping core sections. These allow users to go from several overlapped cores to one merged virtual core. This virtual core then provides a reconstruction of the sedimentary record as one continuous piece. In line with the goal of being a first-pass tool, Corascope leaves all data values unperturbed and merged core data are returned in the same format as generated by the Itrax XRF scanner. This ensures that preexisting analysis software and laboratory procedures can continue to work on preprocessed data.

Structure-from-motion, multi-view stereo photogrammetry applied to line-scan sediment core images

Images of sediment cores are often acquired to preserve primary color information, before such profiles are altered by subsequent sampling and destructive analyses. In many cases, however, no post-processing of these images is undertaken to extract information, despite the fact that image processing can be used to describe and measure structures within the sample. Improvements of RGB (Red/Green/Blue) cameras and image processing algorithms now enable acquisition of high-resolution, metrically calibrated pictures called ortho-images, which have great potential. The way to obtain such ortho-images is by processing several raw images. We propose a semi-automated method that uses metrically calibrated targets to create the ortho-image, using Agisoft Photoscan software and a Python script. The method was tested on sediment cores up to 1.5 m long. It was compared to an approach without markers, one that uses only image matching. The proposed method showed better resolution and less distortion (GSD: 59 µm, RMSE: 7–18 µm). Images acquired without calibrated targets can still be used, by manually positioning points that can then be metrically calibrated. This approach is very useful for smartphone images taken in the field. There are many potential applications for such images of sediment cores, for instance as metric stratigraphic logs to facilitate description of the profile by unit, to study and measure structures (e.g. laminae, instantaneous deposits), or use of image registration or data fusion to create spatial landmarks for non-destructive sensors or destructive laboratory analyses. High-resolution metrically calibrated RGB images of sediment cores are simple to acquire and can play an important role in paleoclimate and paleoenvironmental studies.

Detection of Spinach Juice Residues on Stainless Steel Surfaces Using VNIR Hyperspectral Images

Spinach is one of the most commonly consumed fresh-cut vegetables. Hygiene and sanitation in automated processing facilities have been an important issue. This research aimed to develop a line-scan hyperspectral imaging technique for detecting spinach droplets on a stainless steel surface. The hyperspectral imaging system uses UV-A (365 nm) light sources to obtain 3D hypercube data with spatial and spectral data in the visible and near-infrared (VNIR) region ranging from 400 to 1000 nm. Freshly made 100% spinach juice and distilled water were used to prepare juice dilutions at 20%, 10%, 5%, 2%, and 1% juice. For each of the six juice concentrations, fifteen droplets were placed on a stainless steel sheet, and VNIR hyperspectral image data was collected for the 6 × 15 array of droplets on the metal sheet. To detect and classify the diluted droplets on the spectral domain, three classification models (support vector machine, partial least square discriminant analysis, and random forest) and six pre-processing methods were implemented. Among them, support vector machine (SVM) showed the best classification accuracy with A = 0.95. Besides, the classification model used to reduce the number of wavelengths and calculation time, the genetic algorithm (GA) applied to the SVM showed the most accurate result as A = 0.90 among three methods. The developed classifier demonstrated potential for detecting and classifying spinach juice droplets on the surface of stainless steel sheet metal.

CaMKII as a therapeutic target in catecholaminergic polymorphic ventricular tachycardia type 1

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): South-Eastern Norway Regional Health Authority Background In catecholaminergic polymorphic ventricular tachycardia type 1 (CPVT1) adrenergic activation of the cardiac ryanodine receptor (RyR2) causes spontaneous calcium release and triggers arrhythmias. Calcium/calmodulin-dependent protein kinase II (CaMKII) can contribute to such arrhythmogenic calcium release and has been proposed as a therapeutic target in CPVT1. To predict the efficacy and safety of this strategy, it is necessary to know whether the mechanism for CaMKII activation is important for its arrhythmogenic effects, and if inhibition has proarrhythmic effects. We tested (1) if oxidation of CaMKII contributes to spontaneous calcium release in CPVT1 and (2) if inhibition of CaMKII in this condition can induce calcium alternans. Methods Mice with the CPVT1-causative mutation RyR2-R2474S (RyR2-RS) were crossed with mice with the CaMKII M281/282V (MMVV) mutation that prevents CaMKII M281/282 oxidation, to create double mutants (RyR2-RSxMMVV). Telemetric ECG surveillance was used to study in vivo arrhythmias following an adrenergic challenge by i.p. administration of the beta-adrenoceptor agonist isoprenaline. Confocal line-scan imaging and whole-cell calcium imaging were used to study arrhythmogenic calcium release in isolated left ventricular cardiomyocytes during stimulation with isoprenaline. Results As expected, RyR2-RS mice exhibited more arrhythmic events and spontaneous calcium release (i.e. calcium sparks and calcium waves) compared to wild-type mice. Treatment of RyR2-RS cardiomyocytes with either the CaMKII inhibitor KN-93 or the antioxidant n-acetyl-cysteine reduced spontaneous calcium release (i.e. calcium sparks and calcium waves, for KN-93 and n-acetyl-cysteine, respectively). Interestingly, CaMKII inhibition by KN-93 also increased both incidence and degree of arrhythmogenic calcium alternans in RyR2-RS cardiomyocytes. This adverse effect was a result of prolonged refractoriness of calcium release. Furthermore, to test whether the protective effect of antioxidant treatment in RyR2-RS was mediated via CaMKII oxidation, we compared arrhythmias and spontaneous calcium release (i.e. calcium waves) in RyR2-RSxMMVV with RyR2-RS. However, these two genotypes did not differ in either incidence or severity of arrhythmias, and showed similar degree of spontaneous calcium release. Conclusions Inhibition of CaMKII protects against spontaneous calcium release in CPVT1, and is a promising therapeutic strategy. However, the fact that such inhibition also induces calcium alternans needs further exploration. Antioxidative agents also attenuate arrhythmogenic calcium release in CPVT1 cardiomyocytes, but this effect does not seem to involve the M281/282 CaMKII oxidation site. Future studies should explore other oxidation sites.

Intraoperative hyperspectral label-free imaging: from system design to first-in-patient translation

Despite advances in intraoperative surgical imaging, reliable discrimination of critical tissue during surgery remains challenging. As a result, decisions with potentially life-changing consequences for patients are still based on the surgeon’s subjective visual assessment. Hyperspectral imaging (HSI) provides a promising solution for objective intraoperative tissue characterisation, with the advantages of being non-contact, non-ionising and non-invasive. However, while its potential to aid surgical decision-making has been investigated for a range of applications, to date no real-time intraoperative HSI (iHSI) system has been presented that follows critical design considerations to ensure a satisfactory integration into the surgical workflow. By establishing functional and technical requirements of an intraoperative system for surgery, we present an iHSI system design that allows for real-time wide-field HSI and responsive surgical guidance in a highly constrained operating theatre. Two systems exploiting state-of-the-art industrial HSI cameras, respectively using linescan and snapshot imaging technology, were designed and investigated by performing assessments against established design criteria and ex vivo tissue experiments. Finally, we report the use of our real-time iHSI system in a clinical feasibility case study as part of a spinal fusion surgery. Our results demonstrate seamless integration into existing surgical workflows.

Non-Destructive Detection Pilot Study of Vegetable Organic Residues Using VNIR Hyperspectral Imaging and Deep Learning Techniques

Contamination is a critical issue that affects food consumption adversely. Therefore, efficient detection and classification of food contaminants are essential to ensure food safety. This study applied a visible and near-infrared (VNIR) hyperspectral imaging technique to detect and classify organic residues on the metallic surfaces of food processing machinery. The experimental analysis was performed by diluting both potato and spinach juices to six different concentration levels using distilled water. The 3D hypercube data were acquired in the range of 400–1000 nm using a line-scan VNIR hyperspectral imaging system. Each diluted residue in the spectral domain was detected and classified using six classification methods, including a 1D convolutional neural network (CNN-1D) and five pre-processing methods. Among them, CNN-1D exhibited the highest classification accuracy, with a 0.99 and 0.98 calibration result and a 0.94 validation result for both spinach and potato residues. Therefore, in comparison with the validation accuracy of the support vector machine classifier (0.9 and 0.92 for spinach and potato, respectively), the CNN-1D technique demonstrated improved performance. Hence, the VNIR hyperspectral imaging technique with deep learning can potentially afford rapid and non-destructive detection and classification of organic residues in food facilities.

Extended aperture line-scanning Hartmann wavefront sensor.

We first propose a line-scanning Hartmann wavefront sensor (LS-HWS) with extended aperture. In the LS-HWS, a line-scanning imaging sensor was driven by a motor and scanning behind a large-area Hartman mask. Compared to the traditional Hartman wavefront sensor with two-dimensional imaging sensors, our method can significantly enlarge the aperture because of the larger imaging area with line-scanning imaging sensors. Cross correlation registration was adopted to reduce the scanning error. Experiments on two single spherical lenses and a free-form lens were performed to demonstrate the capability of the LS-HWS method. The results show that our method can achieve an aperture of 17.5×37.5mm2 with the prototype system, which could be further extended easily and is limited only by the size of the line-scanning imaging sensor and the scanning range of the motor. We believe that the LS-HWS method is promising for many wavefront sensing applications where a large aperture is preferred.

Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm.

Fluvial remote sensing has been used to monitor diverse riverine properties through processes such as river bathymetry and visual detection of suspended sediment, algal blooms, and bed materials more efficiently than laborious and expensive in-situ measurements. Red–green–blue (RGB) optical sensors have been widely used in traditional fluvial remote sensing. However, owing to their three confined bands, they rely on visual inspection for qualitative assessments and are limited to performing quantitative and accurate monitoring. Recent advances in hyperspectral imaging in the fluvial domain have enabled hyperspectral images to be geared with more than 150 spectral bands. Thus, various riverine properties can be quantitatively characterized using sensors in low-altitude unmanned aerial vehicles (UAVs) with a high spatial resolution. Many efforts are ongoing to take full advantage of hyperspectral band information in fluvial research. Although geo-referenced hyperspectral images can be acquired for satellites and manned airplanes, few attempts have been made using UAVs. This is mainly because the synthesis of line-scanned images on top of image registration using UAVs is more difficult owing to the highly sensitive and heavy image driven by dense spatial resolution. Therefore, in this study, we propose a practical technique for achieving high spatial accuracy in UAV-based fluvial hyperspectral imaging through efficient image registration using an optical flow algorithm. Template matching algorithms are the most common image registration technique in RGB-based remote sensing; however, they require many calculations and can be error-prone depending on the user, as decisions regarding various parameters are required. Furthermore, the spatial accuracy of this technique needs to be verified, as it has not been widely applied to hyperspectral imagery. The proposed technique resulted in an average reduction of spatial errors by 91.9%, compared to the case where the image registration technique was not applied, and by 78.7% compared to template matching.

Fast, multiplane line-scan confocal microscopy using axially distributed slits.

The inherent constraints on resolution, speed and field of view have hindered the development of high-speed, three-dimensional microscopy techniques over large scales. Here, we present a multiplane line-scan imaging strategy, which uses a series of axially distributed reflecting slits to probe different depths within a sample volume. Our technique enables the simultaneous imaging of an optically sectioned image stack with a single camera at frame rates of hundreds of hertz, without the need for axial scanning. We demonstrate the applicability of our system to monitor fast dynamics in biological samples by performing calcium imaging of neuronal activity in mouse brains and voltage imaging of cardiomyocytes in cardiac samples.

Animal feed formulation: Rapid and non-destructive measurement of components from waste by-products

The major cost (60–80%) of animal production is attributed to feed but most growers are yet to accept and adopt alternative materials like by-products due to their vast variations in nutrient components. Feed and animal production methods are currently considered as unsustainable -with environmental issues related to by-products disposal. Rapid and non-destructive models for quantifying sugars, organic acids, amino acids and other nutrients in alternative materials and a model for precision animal feed production were developed. Consequently, we investigated the nutrient components of by-products using line-scan hyperspectral imaging (HSI) technique. Hyperspectral images of by-products were acquired in the spectral range of 1000–2500 nm. The spectral data were extracted and preprocessed to develop a prediction model using partial least square regression (PLSR) analysis. The PLSR models developed resulted in the following acceptable prediction accuracies (R2p); sugars (0.76–0.94), organic acids (0.72–0.75), amino acids (0.55–0.84), and other nutrients content (0.69–0.96). The root means square error of predictions (RMSEP) obtained were sugars (0.076–0.524 mg/mL), organic acids (0.360–0.626 mg/mL), amino acids (0.007–0.052 mg/mL), and other nutrients content (0.403–1.035 %). The results obtained from the PLSR models showed reliable performance for quantifying chemical components of different by-products. Further, the generated PLSR-based chemical-mapped images facilitated the visual assessment of the chemical concentration and distribution in by-products. Thus, based on the results, the application of HSI in combination with multivariate analysis method of PLSR in a commercial setting may be feasible. This can ultimately enable cost-saving in breeding by curtailing overfeeding and post-production losses and significantly mitigate environmental issues related to by-products disposal.

High-throughput optical sectioning via line-scanning imaging with digital structured modulation.

Optical sectioning with high-throughput, a high signal-to-noise ratio (SNR), and submicrometer resolution is crucial, but challenging, to three-dimensional visualization of large biological tissue samples. Here we propose line-scanning imaging with digital structured modulation for optical sectioning. Our method generates images with a significantly improved SNR, compared to wide-field structured illumination microscopy (WF-SIM), without residual modulation artifacts. We image a 14.5mm×11.5mm horizontal view of mouse brain tissue at a pixel resolution of 0.32µm×0.32µm in 101 s, which, compared to WF-SIM, represents a significant improvement on imaging throughput. These results provide development opportunities for high-throughput, high-resolution large-area optical imaging methods.

Light Sheet Fluorescence Microscope Based on Line-Scanning Imaging

Light Sheet Fluorescence Microscope Based on Line-Scanning Imaging

Line-scanning confocal microscopic imaging based on virtual structured modulation

The predecessor of China Optics was China Optics and Applied Optics Digest, which was founded in 1985. At that time, it was the only retrieval journal in the field of optics in China. At the end of 2008, China Optics and Applied Optics Digest was renamed

Three-Dimensional Measurement Based on Line-Scan Image Fourier and Grayscale Matching Algorithm

Three-Dimensional Measurement Based on Line-Scan Image Fourier and Grayscale Matching Algorithm

Rapid elemental composition measurement of commercial pellets using line-scan hyperspectral imaging analysis

The use of biomass pellets as a renewable energy source is increasing, leading to the need for rapid assessment of biofuel pellet quality for production monitoring. The purpose of this work was to use line-scan near-infrared (NIR) hyperspectral image technology coupled with chemometric tools to assess the elemental components of biomass pellets. The parameters influencing model performance were investigated, i.e. wavelength and spectral pretreatment technique. Either full wavelength or partial wavelength selected using interval successive projections algorithm (iSPA) and interval genetic algorithm (iGA) were investigated. Either raw spectra or pretreated spectra were used for model development. The models were developed using partial least squares regression (PLSR). The most effective model for the prediction of carbon (C), hydrogen (H), and nitrogen (N) content was developed using iGA wavelength selection and standard normal variate (SNV) spectral pretreatment and provided the highest accuracy with a coefficient of determination of prediction set (r2p) and standard error of prediction (SEP) of 0.83 and 1.33%; 0.84 and 0.17%; and 0.90 and 0.098%, respectively. The model could be used for quality assurance. The S content model was poor and not recommended. The relationship between pellet chemical parameters and reflectance characteristics could be used for predicting C, H, and N of biomass pellets.

Line-scan compressive Raman imaging with spatiospectral encoding.

We report a line-scanning imaging modality of compressive Raman technology with a single-pixel detector. The spatial information along the illumination line is encoded onto one axis of a digital micromirror device, while spectral coding masks are applied along the orthogonal direction. We demonstrate imaging and classification of three different chemical species.

Real-time determination and visualization of two independent quantities during a manufacturing process of pharmaceutical tablets by near-infrared hyperspectral imaging combined with multivariate analysis.

During pharmaceutical manufacturing, line-scan hyperspectral imaging enables us to collect several electromagnetic spectra at each pixel in a two-dimensional plane for each tablet. The present study quantitatively determines two independent values of the active pharmaceutical ingredient (API) content in a tablet and the amount of coating on a surface of the same tablet simultaneously; the process is visualized by means of a near-infrared hyperspectral imaging (NIR-HSI) system combined with multivariate data analysis at a typical manufacturing speed of 4,000 tablets per minute. The API content and the amount of coating were controlled to be in the range 80-120% and 0-7mg, respectively. The results of the cross validation of regression models demonstrated a coefficient of determination (R2) of 0.942, a root-mean-square error of cross validation (RMSECV) of 3.48% for the API content, an R2 of 0.939, and an RMSECV of 0.46mg for the amount of coating. These results demonstrated that the API content in a tablet as well as the amount of coating on the surface of the same tablet can be simultaneously determined with sufficient accuracy. This technique is practically applicable to process analytical technology in pharmaceutical manufacturing.

Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil.

We describe a method for two-dimensional (2D) visualization and quantification of the distribution of labile (i.e., reversibly adsorbed) inorganic nutrient (e.g., P, Fe, Mn) and contaminant (e.g., As, Cd, Pb) solute species in the soil adjacent to plant roots (the 'rhizosphere') at sub-millimeter (~100 µm) spatial resolution. The method combines sink-based solute sampling by the diffusive gradients in thin films (DGT) technique with spatially resolved chemical analysis by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The DGT technique is based on thin hydrogels with homogeneously distributed analyte-selective binding phases. The variety of available binding phases allows for the preparation of different DGT gel types following simple gel fabrication procedures. For DGT gel deployment in the rhizosphere, plants are grown in flat, transparent growth containers (rhizotrons), which enable minimal invasive access to a soil-grown root system. After a pre-growth period, DGT gels are applied to selected regions of interest for in situ solute sampling in the rhizosphere. Afterwards, DGT gels are retrieved and prepared for subsequent chemical analysis of the bound solutes using LA-ICP-MS line-scan imaging. Application of internal normalization using 13C and external calibration using matrix-matched gel standards further allows for the quantification of the 2D solute fluxes. This method is unique in its capability to generate quantitative, sub-mm scale 2D images of multi-element solute fluxes in soil-plant environments, exceeding the achievable spatial resolution of other methods for measuring solute gradients in the rhizosphere substantially. We present the application and evaluation of the method for imaging multiple cationic and anionic solute species in the rhizosphere of terrestrial plants and highlight the possibility of combining this method with complementary solute imaging techniques.

Abstract 2961: Olaparib mediated DNA damage response and its resistant mechanism in cervical cancer cells

Abstract Cervical cancer is still the most common gynecologic cancer in developing countries. Olaparib is a poly-(ADR-ribose)-polymerase 1 (PARP1) inhibitor targeting at DNA strand break repair. However its efficacy in cervical cancer is unclear. This study aimed to evaluate the therapeutic potential of olaparib in cervical cancer. Olaparib remarkably reduced the PARylation activity and arrested cervical cancer cells at G2/M phase, and was associated with reduced level of a mitosis marker, phosphor-histone H3 as indicated by propidium iodide DNA staining and immunofluorescence. Cytotoxicity of olaparib against cervical cancer cells was determined by XTT assay with IC50 ranged from 35.49µM in C4I to 87.17µM in SiHa. The difference in olaparib sensitivity between C4I and SiHa was consistently demonstrated in annexin V based apoptotic assay, where a higher level of apoptotic response was found in C4I (21.12%) compared to SiHa (6.37%). Immunofluorescent staining and western blot for gH2AX clearly showed DNA double-strand break accumulation in both cell lines upon PARP1 blockade. Lower expression level of RAD51, a homologous recombination (HR) repair factor, was also observed in C4I, when compared to SiHa. Besides, gH2AX/RAD51 foci co-immunofluorescent line scan image analysis showed that there was reduced level of DNA damage recognition by RAD51 (26.80%), compared to SiHa (65.31%). These findings suggested that there was a reduced HR activity in C4I and might explain the higher sensitivity to olaparib compared to SiHa. We also demonstrated the therapeutic potential of olaparib in cervical cancer, and the rationale of using RAD51 as a predictive biomarker for olaparib sensitivity. Citation Format: Ka Yu Tse, Stephanie Si Liu, Horace Hok Yeung Lee, Philip Pun Ching Ip, Kin Tak Chan. Olaparib mediated DNA damage response and its resistant mechanism in cervical cancer cells [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2961.