Multilayer Imaging Research Articles

Pixel-based and object-oriented approaches in segregating cocoa from forest in the Juabeso-Bia landscape of Ghana

A wall-to-wall Earth Observation (EO) data is required, as recommended by the Intergovernmental Panel on Climate Change, private sector organizations and major development partners, to allow for the implementation of forest monitoring commitments, and also to monitor commodity-led deforestation. However, a major limitation associated with the use of optical EO data in the High Forest Zone of Ghana is the presence of persistent cloud cover and the spectral limitations of segregating agroforestry cocoa (AFC) from open canopy forest (OCF) cover. The aim of the study was to investigate the synergistic use of Sentinel-1 (S1) and Sentinel-2 (S2) EO data to produce a land use/land cover map which shows AFC and OCF as different land cover classes. It was hypothesized that, a hybrid method of spectral, radar and image objects will accurately segregate the different cocoa systems from forest and other land use classes. The research was conducted in the Juaboso-Bia REDD+ Hotspot Intervention Area in the cocoa-forest mosaic landscape within the High Forest Zone of Ghana. The S1 and S2 datasets were freely acquired for the periods January to March 2018. The S1 datasets were pre-processed from backscatter intensity values to the VV and VH bands. The S2 datasets were corrected for atmospheric effects, and cloud pixels were masked and filled using a temporal gap-filling method. Six vegetation indices (VIs) were extracted, and the Multiresolution Segmentation algorithm was used to derive image objects (IOs). The S2 bands, the six VIs, the S1 VV + VH data, and the IOs were stacked into 3 different multi-layer image dataset denoted with D (i.e. D1 = S2 + VIs; D2 = S2 + VIs + S1; and D3 = S2+VIs + S1+IOs). The three datasets were classified using Random Forest and 1228 training points. Overall accuracy (OA) and kappa (k) were calculated for the classification outcome using 615 independent validation points. McNemar's test (χ) was used to access the statistically significant difference between D1, D2 and D3. The results of the study show that, D3 significantly improved the overall classification output (OA = 89.76%, k = 0.877) compared to D1 (OA = 79.02%, k = 0.748; χ = 5.56, p-value = 0.018) and D2 (OA = 80.49%, k = 0.765; χ = 5.50, p-value = 0.019). Combining spectral pixels with image objects increases overall classification accuracy and specifically, the accuracy of isolating AFC from OCF. This research is significant because it will provide an improved decision support to government-led monitoring and the private sector's commitment to halt cocoa-driven deforestation. Furthermore, and most importantly, the map shows agroforestry cocoa separated from monoculture cocoa, which provides a tremendous boost to monitoring landscape-level improvements associated with the promotion and adoption of agroforestry in cocoa landscapes, as a climate-smart practice and also for monitoring various off-reserve landscape forest restoration activities.

«El manchado lienzo de la historia»: Usos y apropiaciones del pasado medieval en la historiografía de la ilustración. El caso de Pedro el cruel

The aim of this paper is to explore the political meanings of the medieval past in the historical understanding of individuals in the 18th century. Beyond the familiar Gothic myth, I will focus on the multilayered image of the monarch Peter of Castile (1334–1369), considered cruel, lustful and passionate in the eyes of enemies and historians. The aim is to analyse the different attitudes towards and uses of this historical figure (one that was quite problematic given the cultural values of the period), ranging from the most negative of connotations to the most passionate of defences and including some more ambiguous stances. For this analysis, I will explore certain Enlightenment debates that are useful for broadening the theoretical frameworks available for understanding representations of Peter of Castile. In doing so, I will establish a link between the controversies surrounding civilization, sensibility, masculinity, patriotism and «national characters». The varied appropriations of his personality and reign, found in different chronicles, compendiums, periodicals and tributes, are understood in the framework of the controversies surrounding critical history and the reliability of witnesses and historians. Their study highlights the diversity of processes through which the past, in its different forms, played a political role in 18th-century society and culture. And, ultimately, it reveals the tensions and disagreements that arise in the process of shaping historiographical practice.

Assessment of goblet cell size and density in relation to epithelial cell (multi)layering on conjunctival impression cytology samples.

To assess goblet cell size and numbers in relation to the extent of multilayering of conjunctival impression cytology (CIC) samples as a basis for reducing variability in image selection for goblet cell density (GCD) estimates. CIC was undertaken immediately postmortem off the superior bulbar conjunctiva of healthy young adult rabbits onto Millicell-CM Biopore filter units. After fixation with buffered glutaraldehyde and Giemsa staining, two × 200 images were selected from each sample representative of either slight multilayering or substantial multilayering, projected at × 1000, an overlay of the outlines of the goblet cells was made, and their dimensions and areas were measured. From measures of 4918 goblet cells, the average value (+/- SD) for the longest dimension was 17.7 ± 6.4μm and 14.6 ± 5.3μm for the shortest dimension. The GCD values ranged from 210 to 2069/mm2, with a mean of 1074 ± 601/mm2, but was lower for slightly multilayered images (at 537 ± 239 cells/mm) compared with multilayered regions (at 1612 ± 601 cells/mm2; p < 0.001). The measured areas ranged from 72 to 491μm2, with average values from any particular image ranging from 110 to 370μm2, which were inversely correlated with the estimated GCD (Spearman's rho = - 0.722, p < 0.05). Larger goblet cells but in fewer numbers were predictably found across the filter surface where there were fewer layers of cells and vice versa. This difference could be considered in selection of images for counts of goblet cells from CIC specimens.

Application of panoramic radiography with a multilayer imaging program for detecting proximal caries: a preliminary clinical study.

The aim of this study is to introduce a novel program of panoramic radiography that shows 41 multilayer images from the buccal to lingual aspects in a region of interest, and to evaluate the effectiveness of this program for detecting proximal caries. In total, 480 premolars and molars on 30 panoramic radiographs taken with the multilayer imaging program were included in this study. The presence or absence of caries in 960 proximal surfaces was assessed by three experienced oral and maxillofacial radiologists as a consensus-based gold-standard. Two general dentists evaluated and scored proximal caries on 980 surfaces on panoramic radiographs with and without the multilayer imaging program. The two general dentists' scores were compared with the gold-standard, and were analyzed for sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic (ROC) curve with and without using the program. The area under the ROC curve was analyzed using STATA/SE 13.1. When the multilayer imaging program was used for panoramic radiography, the inter- and intraobserver agreement of the two general dentists improved. All values, including the area under the ROC curve, were higher when the multilayer imaging program was used than when it was not used. The area under the ROC curve showed a statistically significant improvement only in Observer 1, whose diagnostic ability was poorer than that of Observer 2. This multilayer imaging program might help the inexperienced dentist to improve the diagnostic accuracy of proximal caries. If further studies would be performed in various clinical application, it could be useful when intraoral radiography taking is difficult for reasons such as mouth-opening limitations and the gag reflex of the patients.

Multi-Layered Circular Dielectric Structures’ Synthetic Aperture Radar Imaging Based on Green’s Function Using Non-Uniform Measurements

The electromagnetic imaging of multi-layered circular structures can be accomplished by means of different approaches. Among these approaches, the use of synthetic aperture radar (SAR) techniques with the compensation of the Green’s function for multi-layered circular cylinders provides a trade-off between accuracy and computational complexity. Nevertheless, this approach relies on measurement points acquired uniformly. Thus, this prevents the adoption of more flexible sampling schemes, limiting the use of inaccurate positioners or manual scanners. To overcome this limitation, this paper proposes a method to handle non-uniform cylindrically acquired data, enabling multi-layered circular imaging based on non-uniformly acquired measurements. For this purpose, the presented method starts by projecting the non-uniformly measured points onto different circles with equally-spaced points. After that, the Green’s function-based circular-SAR imaging method is applied to each projection circle. Lastly, the final SAR image is obtained by the superposition of the images from the previous step. Numerical simulations and experimental results demonstrated the effectiveness and robustness of the proposed method for practical nondestructive testing (NDT) applications.

Enhanced Intelligent Identification of Concrete Cracks Using Multi-Layered Image Preprocessing-Aided Convolutional Neural Networks.

Crack identification plays an essential role in the health diagnosis of various concrete structures. Among different intelligent algorithms, the convolutional neural networks (CNNs) has been demonstrated as a promising tool capable of efficiently identifying the existence and evolution of concrete cracks by adaptively recognizing crack features from a large amount of concrete surface images. However, the accuracy as well as the versatility of conventional CNNs in crack identification is largely limited, due to the influence of noise contained in the background of the concrete surface images. The noise originates from highly diverse sources, such as light spots, blurs, surface roughness/wear/stains. With the aim of enhancing the accuracy, noise immunity, and versatility of CNN-based crack identification methods, a framework of enhanced intelligent identification of concrete cracks is established in this study, based on a hybrid utilization of conventional CNNs with a multi-layered image preprocessing strategy (MLP), of which the key components are homomorphic filtering and the Otsu thresholding method. Relying on the comparison and fine-tuning of classic CNN structures, networks for detection of crack position and identification of crack type are built, trained, and tested, based on a dataset composed of a large number of concrete crack images. The effectiveness and efficiency of the proposed framework involving the MLP and the CNN in crack identification are examined by comparative studies, with and without the implementation of the MLP strategy. Crack identification accuracy subject to different sources and levels of noise influence is investigated.

Blending Bilaterally Filtered Oil Painting Expression Techniques and Stylistic Rendering of Calligraphy Strokes Combining the Background of Visual Importance

To transform real images into an oil painting style, a bilateral filtering algorithm based on the background of visual importance is proposed. Firstly, the improved bilateral filtering and mathematical morphological operations are combined to a multilayer reference image sequence with a pyramid structure according to the features of oil painting drawing. Subsequently, the stroke layout algorithm based on the visual importance diagram is proposed. The human face, visual focus detection, and other methods are used to detect the influential region. In addition, the starting point of brush drawing, the improvement of direction calculation accuracy, and the enhancement of the image texture boundaries are performed according to the information of these regions to implement the oil painting expression techniques and achieve the highlight effect of the stylistic rendering contents of calligraphy strokes. The experimental results suggest that the bilateral filtering algorithm can be used to achieve relatively good visual effects in the background of visual importance.

Pore structure characterization of oak via X-ray computed tomography

The microscopic pore structure of wood is an important factor that affects its macroscopic properties. In this study, an oak sample was used for pore structure characterization. X-ray computed tomography imaging was carried out, and the scanning results (a multi-layer two-dimensional planar image) were rendered using Avizo software (a three-dimensional stereo image). A digital image processing method was used to identify the characteristics of the three-dimensional pore structure features and to calculate the characteristic parameters, i.e., the porosity (volume/surface), pore area and volume, pore size distribution, and the connectivity. The 27 mm3 oak sample had the following characteristics: a pore size which ranged from 8.56 µm to 1262.84 µm; a pore volume of 1.01 × 1010 µm3; a pore area and volume porosity of 1.12 × 109 µm2 and 37.6%, respectively; a surface porosity range of approximately 36.1% to 39.1%; a pore diameter for axial connection ranging from approximately 164.57 µm to 1262.84 µm; and had corresponding proportions of the pore area and volume of approximately 74.4% and 67.3%, respectively. This information provided useful structural data for the construction of future models.

An End-to-End Trainable Multi-Column CNN for Scene Recognition in Extremely Changing Environment.

Scene recognition is an essential part in the vision-based robot navigation domain. The successful application of deep learning technology has triggered more extensive preliminary studies on scene recognition, which all use extracted features from networks that are trained for recognition tasks. In the paper, we interpret scene recognition as a region-based image retrieval problem and present a novel approach for scene recognition with an end-to-end trainable Multi-column convolutional neural network (MCNN) architecture. The proposed MCNN utilizes filters with receptive fields of different sizes to have Multi-level and Multi-layer image perception, and consists of three components: front-end, middle-end and back-end. The first seven layers VGG16 are taken as front-end for two-dimensional feature extraction, Inception-A is taken as the middle-end for deeper learning feature representation, and Large-Margin Softmax Loss (L-Softmax) is taken as the back-end for enhancing intra-class compactness and inter-class-separability. Extensive experiments have been conducted to evaluate the performance according to compare our proposed network to existing state-of-the-art methods. Experimental results on three popular datasets demonstrate the robustness and accuracy of our approach. To the best of our knowledge, the presented approach has not been applied for the scene recognition in literature.

Multi-layered full-field phase imaging using continuous-wave terahertz ptychography.

Due to the unique properties of terahertz (THz) waves, THz phase imaging has been widely investigated to retrieve the absorption and phase modulation of dielectric two-dimensional thin samples, as well as multiple stacked samples. In this Letter, we apply the three-dimensional ptychographic iterative engine algorithm for continuous-wave THz full-field multi-layered phase imaging. The complex-valued transmission function of two-layered polypropylene thin plates and the corresponding probe function are reconstructed, respectively, which are immune to crosstalk of different layers. The phenomenon of the field-of-view enlargement at the second object layer is observed. This lensless compact imaging method can be potentially used for THz three-dimensional imaging.

Holographic capture and projection system of real object based on tunable zoom lens

In this paper, we propose a holographic capture and projection system of real objects based on tunable zoom lenses. Different from the traditional holographic system, a liquid lens-based zoom camera and a digital conical lens are used as key parts to reach the functions of holographic capture and projection, respectively. The zoom camera is produced by combing liquid lenses and solid lenses, which has the advantages of fast response and light weight. By electrically controlling the curvature of the liquid-liquid surface, the focal length of the zoom camera can be changed easily. As another tunable zoom lens, the digital conical lens has a large focal depth and the optical property is perfectly used in the holographic system for adaptive projection, especially for multilayer imaging. By loading the phase of the conical lens on the spatial light modulator, the reconstructed image can be projected with large depths. With the proposed system, holographic zoom capture and color reproduction of real objects can be achieved based on a simple structure. Experimental results verify the feasibility of the proposed system. The proposed system is expected to be applied to micro-projection and three-dimensional display technology.

Ivan Vyshensky’s Polemical Tractate “Porada” as a Synthetic Text

Polemical prose is a crucial and interesting part of Ukrainian literature of the 16th–17th centuries. It represents not only social realities of that time (the Union of Brest), but also the philosophical and ethical views of Ukrainian polemicists. These texts have significant multilayer symbolism, images, and Biblical intertext. The article highlights the short history of the study of the heritage of Ukrainian polemicist Ivan Vyshensky in general and the “Porada” in particular from the end of the 19th century (I. Franko, M. Hrushevskyi) to the end of the 20th century (P. Yaremenko, S. Pinchuk). Works of the polemicist are studied in historical, cultural, and philosophical terms. The author’s attention is concentrated on structural and semiotic readings of the third chapter of the polemicist’s magnum opus (“Knyzhka”). A structure is presented as an artificial scheme to organize all textual material into one discourse (according to Umberto Ecco). It is shown that some specifics of the composition’s structure influence the entire meaning of the “Porada”. Its construction, formed by such key elements as “frames” and a semantically meaningful center, is correlated with ancient religious beliefs (Mircea Eliade) as well as the Christian antithesis of “spirit and body”. Singled out are main codes, symbols, and images of the antithesis of the tractate. The code is a semiotic system and the text is a code-generator. The main codes of the “Porada” are its language code (liturgy), the code of mainting Christian customs (in the church as well as at home) and the code of faith. Loci (church, house) and monk’s clothes (hat, hair, belt, shoes) are symbols that represent the objectivity of image and depths of meaning (S. Averintsev). Images of antitheses reveal leading dichotomies: sacral – profane, monastic – secular, spiritual – sinful.

Adaptive image enhancement method using contrast limitation based on multiple layers BOHE

Multiple layers block overlapped histogram equalization (MLBOHE) is a classic image enhancement method. However, median filter is used in it to reduce noises which cause the degeneration of the local information. Moreover, the hidden details are not revealed effectively during the image fusion processes. To solve these drawbacks, an adaptive image enhancement method using contrast limitation is proposed in this paper. Based on MLBOHE, the proposed method employs a contrast limited method to suppress noises before BOHE is performed in each layer sub-blocks. Then an improved image fusion mode is applied to adaptively merge the multilayered BOHE images. The way to obtain the fusion weights by this fusion mode is according to the entropy value of each layer sub-blocks. In addition, four Image Quality Measures (IQMs), namely peak signal-to-noise ratio (PSNR), image clarity, contrast measure (EME) and feature similarity index metric (FSIM), are used to analyze the effectiveness of the proposed method. Simulation results show that the proposed method has high performance in suppressing noises and displaying more trustworthy details. Besides, this method outperforms the existing methods in weakening the excessive enhancement for low illumination and foggy images.

A scanning and image processing system with integrated design for automated micronucleus scoring

Purpose: Our aim was to design a compact and cost-effective optical microscopic system for automated non-fluorescent micronucleus (MN) scoring whose performance can reach the accuracy of visual scoring with the help of minimal user interaction and also gives an option for fully automatic scoring with an accuracy suitable for triage purposes.Materials and methods: The concept of Radometer MN-Series (RS-MN) microscopic system designed by Radosys was to develop hardware and software layers in parallel in order to optimize the performance in automated MN scoring. A MN assay slide is automatically scanned by the RS-MN then the binucleated cells and micronuclei are automatically identified. Processing 1000 cells takes 10–60 minutes by automatic scoring (scanning plus image processing) depending on sample quality and required accuracy. The manual revision of the cell gallery takes an extra 10 minutes per sample. Dose response curves are determined for manual, automatic and semi-automatic scoring methods.Results: The combination of object-sensitive autofocusing method and the multi-layer image acquisition is able to reduce the minimum resolvable dose by 14%. The MN yields obtained from the manual, semi-automatic and automatic scoring methods are well correlating (Pearson’s correlation coefficients are between 0.977 and 0.998). In order to compare the reliability of the results of visual and automatic scoring, an extended analysis on uncertainty contributors was performed. For a dose of 1 Gy, the estimated relative uncertainty from the Poisson characteristics of MN yield is 17–19% for the manual and 20–38% for the automated scoring. Other uncertainty factors (differences in donor radiosensitivity, scorer performance, and sample preparations) can contribute to this error fall within a similar range: 3–16%. Taking into account all of the possible uncertainties, the minimum resolvable dose for the manual (0.48 Gy) is the two-thirds of that of the automatic scoring (0.61 Gy).Conclusions: The results verify that the fully automatic mode of RS-MN is suitable for triage purposes. The performance of the user interacted semi-automatic mode is comparable with the reference manual scoring. Its performance reaches up to other non-fluorescent automatic systems and offers a compact and cost-effective alternative.

Multiplane displays based on liquid crystals for AR applications

AbstractMultiplane displays are capable of displaying 3D scenes with correct focus cues by creating multilayer 2D images in the display volume. Hence, such a 3D display technique could effectively address the accommodation‐vergence conflict (AVC) problem, which is prevalent in augmented reality (AR) displays. In this paper, we review the recent progress on multiplane AR displays based on liquid crystals (LCs) for AR applications. The working principle of multiplane AR displays is illustrated, the electro‐optical properties of the tunable LC devices are investigated and display prototypes are demonstrated. Finally, we discuss the prospects and challenges of multiplane AR displays based on LCs.

Low-dose megavoltage cone-beam computed tomography using a novel multi-layer imager (MLI).

The feasibility of low-dose megavoltage cone-beam acquisition (MVCBCT) using a novel, high detective quantum efficiency (DQE) multi-layer imager (MLI) was investigated. The aim of this work was to reconstruct MVCBCT images using the MLI at different total dose levels, and assess Hounsfield Unit (HU) accuracy, noise and contrast-to-noise ratio (CNR) for low-dose megavoltage cone-beam acquisition. The MLI has four stacked layers; each layer contains a combination of copper filter/converter, gadolinium oxysulfide (GOS) scintillator and a-Si detector array. In total, 720 projections of a CATPHAN® phantom were acquired over 360° at 2.5, 6, and 6MV flattening filter free (FFF) beam energies on a Varian TrueBeam LINAC. The dose per projection was 0.01, 0.0167, and 0.05MU for 2.5, 6, and 6MV FFF, respectively. MVCBCT images were reconstructed with varying numbers of projections to provide a range of doses for evaluation. Hounsfield Unit uniformity, accuracy, noise and CNR were estimated. Improvements were quantified relative to the standard AS1200 single-layer imager. Average HU uniformity for the MLI reconstructions was within a range of 95%-99% for all of the energies studied. Relative electron density estimation from HU values was within 0.4%±1.8% from nominal values. The CNR for MVCBCT based on MLI projections was 2-4× greater than from AS1200 projections. The 2.5MV beam acquisition with the MLI exhibited the lowest noise and the best balance between CNR and dose for low-dose reconstructions. Megavoltage cone-beam acquisition imaging with a novel MLI prototype mounted on a clinical linear accelerator demonstrated substantial improvement over the standard AS1200 EPID. Further optimization of MVCBCT reconstruction, particularly for 2.5MV acquisitions, will improve image metrics. Overall, the MLI improves CNR at substantially lower doses than currently required by conventional detectors. This new high DQE detector could provide high-quality MVCBCT at clinically acceptable doses.

P1782 Left ventricular deformation proprieties in aortic coarctation: insights from multilayer strain imaging

Abstract Background Patients with coarctation of the aorta (CoA) have proven impaired left ventricular (LV) myocardial deformation that will persist even after successful transcatheter intervention compared to normal controls. However, little is known about layer specific contraction in this clinical setting. Purpose To investigate multi-layer longitudinal strain in patients with repaired CoA, compared with a control group of healthy subjects. Methods In a case-control study, 13 CoA patients (F/M = 9/4, age = 15.1 ± 4.4 years) and 13 healthy age- and sex-matched controls, underwent a complete echocardiogram, including speckle tracking assessment for quantification of LV transmural global longitudinal strain (GLS), subendocardial longitudinal strain (LSsubendo), subepicardial longitudinal strain (LSsubepi), and myocardial longitudinal strain gradient (LSsubendo - LSsubpepi). Results CoA patients had similar blood pressure, heart rate, and body surface area in comparison with healthy controls. The two groups did not differ for ejection fraction and LV diastolic indices. Increased maximal gradient (&amp;gt;20 mmHg) in descending aorta was found in 9 (69.2%) CoA patients. In CoA population, 5 (38.5%) had LV concentric remodeling (RWT &amp;gt; 0.42) and 2 (15.4%) LV hypertrophy (Z score &amp;gt;2.0). LV mass index (92.4 ± 31.1 vs. 60.9 ± 12.5 g/m^2.7, p = 0.002), septal wall thickness (8.7 ± 2.0 vs 6.6 ± 0.9 mm p = 0.002), posterior wall thickness (8.2 ± 1.7 vs. 6.6 ± 1.4 mm, p = 0.02) and RWT (0.38 ± 0.06 vs. 0.27 ± 0.08, p &amp;lt; 0.001) were greater in CoA patients. By multi-layer deformation analysis, GLS (21.2 ± 1.9 vs. 22.9 ± 1.4%, p &amp;lt; 0.01), LSsubepi (19.3 ± 1.6 vs. 21.0 ± 1.6%, p &amp;lt; 0.01), LSsubendo (22.9 ± 1.8 vs. 25.4 ± 1.9%, p = 0.003) and longitudinal strain gradient (3.6 ± 1.1 vs. 4.7 ± 1.1, p &amp;lt; 0.02) were lower in CoA patients than in healthy controls. Separate sub-analyses in patients without LV hypertrophy, substantially confirmed the same results: GLS 20.9 ± 1.6 vs. 22.8 ± 0.9%, p = 0.004; LSsubepi 19.0 ± 1.4 vs. 21.1 ± 1.3%, p = 0.004; and LSsubendo 22.4 ± 1.2 vs. 25.3 ± 1.4%, p &amp;lt; 0.0001; strain gradient 3.4 ± 1.0 vs. 4.6 ± 1.1, p = 0.02. Notably, LSsubepi (18.6 ± 1.4 vs. 20.6 ± 1.0%, p = 0.03) and, with greater significance, LSsubendo (22.1 ± 1.1 vs. 24.7 ± 1.8%, p &amp;lt; 0.0001) were lower in CoA patients with increased aortic gradient versus those without significant gradient increase. Conclusions In CoA patients, layer specific strain imaging highlights an impairment of LV longitudinal deformation, which mainly involves LSsubendo and causes reduction of longitudinal strain gradient. This preferential impairment of subendocardium is particularly evident in patients with residual aortic gradient and is independent of LV hypertrophy. Abstract P1782 Figure. Multilayer strain in CoA vs. controls

Design and Optimisation of a Three Layers Thermal Neutron, Fast Neutron and Gamma-Ray Imaging System

The design and configuration of a multi-layered imaging system with the ability to detect thermal neutrons, fast neutrons and gamma rays has been developed and its efficacy demonstrated. The work presented here numerically determines the systems efficiency and spatial resolution, using 252Cf and 137Cs as a case study. The novelty of this detection system lies in the use of small form factor detectors in a three-layer design, which utilises neutron elastic scattering and Compton scattering simultaneously. The current configuration consists of 10 mm thick natural lithium glass (GS10) scintillator integrated with a 20 mm thick plastic scintillator (EJ-204) in the first layer, a 15 mm thick lithium glass (GS10) scintillator in the second and a 30 mm thick CsI(Tl) scintillator forming the final layer. Each of these layers is backed with an 8 x 8 silicon photomultiplier diode (SiPM) array. The overall size of the imaging system is 27 mm x 27 mm x 135 mm. MCNPv6.1 and Geant4-10.04 were alternatively used to optimise the overall configuration and to investigate detection modalities. Results show promising performance with high precision source localisation and characterization abilities. Measurements were virtually obtained of two gamma-ray sources within steel enclosures at angles of 15°, 30° and 50° separation in order to test spatial resolution ability of the system. With the current active size of the system and the 8x8 SiPM configuration, the results estimate the spatial resolution to be close to 30°. The ability of the system to characterise and identify sources based on the type and energy of the radiation emitted, has been investigated and results show that for all radiation types the system can identify the source energy within the energy range of typical reported sources in literature.

Cancer cell-targeted nanoprobe for multilayer imaging of diverse biomarkers and precise photodynamic therapy.

A novel multifunctional nanoprobe was designed for cancer cell targeted multilayer imaging of two cancer biomarkers. Based on the proposed method, in situ imaging of membrane MUC1 mucin and cytoplasmic microRNA miR-21 coupled with precise photodynamic therapy was achieved.

A polydopamine-based biomimetic multifunctional nanoplatform for multilayer imaging of cancer biomarkers carried by extracellular vesicles.

Cancer-derived extracellular vesicles (EVs) have attracted considerable attention for clinical diagnosis. However, a limiting factor in current EV assays is the ability to detect various EV cancer biomarkers expressed at different locations. Here, we report a biomimetic multifunctional nanoplatform for multilayer imaging of cancer biomarkers from the EV surface to the interior without complex pretreatment. Constructed from polydopamine-wrapped gold nanoparticles modified with multiple functional molecules, this nanoplatform can capture EVs from complex samples and target different EV cancer biomarkers for imaging analysis at the single-vesicle level. Combined with 96-well plates, this assay can distinguish cancer cell-derived EVs from normal ones in a high-throughput manner. Using serum samples, EVs from hepatocellular carcinoma (HCC) patients can be distinguished from healthy controls. This convenient workflow represents a promising tool for EV-based cancer diagnosis.