Multilayer Imaging Research Articles

Research progress of multilayer X-ray imaging optics for plasma diagnostics

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KMIT-Pathology: Digital Pathology AI Platform for Cancer Biomarkers Identification on Whole Slide Images

Analysis and identification of cancer imaging bio markers on biopsy tissues are done through optical microscope. Digital tissue scanners and Deep learning models automate this task and produce unbiased diagnostics. The digital tissue scanner is called as virtual microscopy which digitize the glass slide tissues and the digitized images are called as Whole Slide Images (WSI). They are multi-layered (level) images having high resolution, huge in size and stored as a pyramidal tiff file. As normal web browsers are unable to handle WSI, a special web imaging platform is needed to obtain, store, visualize and process WSI. This platform must provide basic facilities for uploading, viewing and annotating WSI which are the inputs to the deep learning models. The integration of deep learning models with the platform and the WSI database provides a complete solution to cancer diagnostics and detection. This paper proposes two AI deep learning models for the diagnostics and the detection of cancer imaging bio markers on breast cancer and prostate cancer WSI. Efficientnet deep learning model is used to detect ISUP (International Society of Urologic Pathologists) grading for prostate cancer which is trained and tested by 5000 prostate WSI and produces 80% accuracy with 0.6898 quadratic weighted kappa (QWK) score. R2Unet model is used to identify tubule structures for breast cancer which is a morphological component to grade breast cancer. The model is trained and tested by 17432 WSI tiles and generates f1 metric accuracy as 0.9961 with mean_io_u 0.8612. The paper also shows the complete execution of these two Deep learning models (from uploading WSI to visualize the AI detected results) on the newly developed WSI imaging web platform.

Optical Design of Athermalized Infrared Dual-Band Annular Folded Lens with Multilayer Imaging Diffractive Optical Elements

Optical Design of Athermalized Infrared Dual-Band Annular Folded Lens with Multilayer Imaging Diffractive Optical Elements

Multi-layer image decomposition-based image fusion algorithm

Multi-layer image decomposition-based image fusion algorithm

Диалог «Language School» и метареализма: поэмы Sun Майкла Палмера и «Нефть» Алексея Парщикова

In this article the author analyzes the interaction and mutual influence of such ways of speaking, which we call Language Writing and metarealism, which he reveals on the example of the poems Sun (1987) by Michael Palmer and Oil (1999) by Aleksei Parshchikov. Palmer's poem is a decentralized statement in which syntactic interruptions, complex symbolism, and an abundance of references create an embodied heteroglossia. The catastrophe of the meaning and the assembly of voices here are somehow structured by the central symbol — the Sun — from the headline of The Sun newspaper of July 22, 1986 (“A headless man walks, lives / for four hours”) to the artificial light of consumption centers. And the semantics of dying, references to Baudelaire in the poem, as well as Palmer's separate “Baudelaire Series” somehow admit that one of the many overtones of Sun poem is “The Voyage”, the final text of Fleurs du mal. Marjorie Perloff points out the difference between the approaches of Palmer and Parshchikov in the article “Russian Postmodernism: An Oxymoron?”, where, comparing their texts, she writes that the approach of the second is closer to Arthur Rimbaud, not the split subject Language Poetry. However, the similarity and differences between the approaches of Palmer and Parshchikov are similar to the similarity and differences between Charles Baudelaire and Arthur Rimbaud, when both use the mythopoetics of swimming as death, but incomplete, or rather incompleting. But if the first include it in the context of criticism of commodity fetishism and the subversive power of allegory (according to Benjamin), then the second in the context of “clairvoyance” and observation of the “black and cold” waters of capitalistic Europe. So the poem Oil by Parshchikov is close to the poem Sun by Palmer both in the depiction of the world in a frozen catastrophe, in the collapse of the language, and in the political dimension. And as in the poem Sun through heteroglossia, the symbolism of the crisis meaning, a permanent apocalypse Palmer creates political criticism of the symbolic order of the consumption society, including intellectual commodity fetishism, as in the poem Oil with a multilayered image of raw materials, including as an ideological concept, Parshchikov opens up new ways for analytical political poetry.

Vision of Victorian Ecological Disaster (based on the selected novels by Cindy Spencer Pape)

The problem of nature deterioration in the Anthropocene has become an important art concern, thus leading to analysis of ecological images in the texts of various genres. Our analysis of such imagery in a gaslight romance series is equally topical. The subject of the study is universe of the series of novels by Cindy Spencer Pape which centres around alternative nineteenth century London with highly developed steam engines and corresponding technology. Ecocritical approach is chosen as the research method of the study as the novels have powerful ecological imagery, which requires close analysis. The series covers a number of years, and so the ecological deterioration due to coal overuse can be traced through various London locales, which are described in different time frames, and the comparative reading of these descriptions provides the proof of ecological deterioration. It reveals the consistent ecological discourse within the series, its ties strengthened through steady layering of the images of nature, burdened with progressing pollution. There is social dimension to the issue, as the poorer urban residents are shown to be more effected by this problem due to their jobs, work and living conditions. The paper aims to reveal these numerous aspects of ecological imagery in the texts. The results. Close text analysis of various levels allows us to decipher the multi-layered ecological images and dwell on the message they bear. As the environment degradation takes place in the past, the series allows us to describe its results as facts and compress its progress in time and space within the span of the novels setting. This intensifies the image of the catastrophe, makes it feel more immediate and pressing, thus becoming a powerful plot tool that results in search for solutions of the problem and alleviation of the existing results of coal overuse. This research is the basis of further analysis of ecological imagery in steampunk and gaslight romance as genres, fruitful in that regard.

A wave of metaphors: image and visual metaphors in cartoons from a cognitive-discursive perspective

This paper aligns with a field of research that deals with the use of multimodal metaphors from a cognitive-discursive perspective. In this context, we aim to investigate the role played by images in the instantiation of cross-domain mappings in a particular genre. Specifically, we describe and analyze the cognitive-discursive nature and functioning of visual metaphors in political and social cartoons. This paper first explores the concepts of image schemas, image metaphors, and visual metaphors, as well as the notion of metaphoricity in discourse. We then carry out the analysis of multimodal metaphors in a corpus of editorial cartoons that depict the Covid-19 pandemic, and other related issues within social and political contexts. Some of our findings suggest that cartoons often evoke multilayered off-line frames, image metaphors and conceptual metaphors in order to enhance the persuasive power of their semiotic arrangement, especially by inviting their audience to actively participate in meaning-construction processes

A dense RGB-D SLAM algorithm based on convolutional neural network of multi-layer image invariant feature

Simultaneous localization and mapping (SLAM) is one of the key technologies used in sweepers, autonomous vehicles, virtual reality and other fields. This paper presents a dense three-channel color images composed of red, green and blue and depth images SLAM reconstruction algorithm based on convolutional neural network (CNN) of multi-layer image invariant feature transformation. The main contribution of the system lies in the construction of a CNN based on multi-layer image invariant feature, which optimized the extraction of Oriented FAST and Rotated Brief(ORB) feature points and the reconstruction effect. After the feature point matching, pose estimation, loop detection and other steps, the 3D point clouds were finally spliced to construct a complete and smooth spatial model. The system can improve the accuracy and robustness in feature point processing and pose estimation. Comparative experiments show that the optimized algorithm saves 0.093 s compared to the ordinary extraction algorithm while guaranteeing a high accuracy rate at the same time. The results of reconstruction experiments show that the spatial models have more clear details, smoother connection with no fault layers than the original ones. The reconstruction results are generally better than other common algorithms, such as Kintinuous, Elasticfusion and ORBSLAM2 dense reconstruction.

Multi-layer ultrasonic imaging of as-built Wire + Arc Additive Manufactured components

Non-Destructive Evaluation (NDE) of metal Additively Manufactured (AM) components is crucial for the identification of any potential defects. Ultrasonic testing is recognised for its volumetric imaging capability in metallic components and high defect sensitivity. However, conventional ultrasonic techniques suffer from challenges when deployed on components with curved and non-planar geometries, such as those often encountered in AM builds. The body of work introduces the concept of inspection of Wire+Arc Additive Manufacture (WAAM) components from their non-planar as-built surface, eliminating the requirement for post-manufacturing machining. In-situ or post-manufacturing inspection is enabled via an autonomously deployed conformable phased array roller-probe deploying Synthetic Aperture Focusing Technique (SAFT)-surface finding and multi-layer adaptive Total Focusing Method (TFM) algorithms, for fully focussed imaging of the as-built WAAM component. The concept of the imaging approach is demonstrated by inspection, through the as-built surface, of two titanium WAAM components, one containing reference bottom-drilled holes, and the other with intentionally introduced Lack of Fusion (LoF) defects. The TFM images of the WAAM components feature sufficient Signal-to-Noise Ratio to enable defect detection along with strong agreement against reference X-Ray CT data, confirming the competency of the approach for volumetric or layer-specific inspection of as-built WAAM components.

MLMT-CNN for object detection and segmentation in multi-layer and multi-spectral images

Precisely localising solar Active Regions (AR) from multi-spectral images is a challenging but important task in understanding solar activity and its influence on space weather. A main challenge comes from each modality capturing a different location of the 3D objects, as opposed to typical multi-spectral imaging scenarios where all image bands observe the same scene. Thus, we refer to this special multi-spectral scenario as multi-layer. We present a multi-task deep learning framework that exploits the dependencies between image bands to produce 3D AR localisation (segmentation and detection) where different image bands (and physical locations) have their own set of results. Furthermore, to address the difficulty of producing dense AR annotations for training supervised machine learning (ML) algorithms, we adapt a training strategy based on weak labels (i.e. bounding boxes) in a recursive manner. We compare our detection and segmentation stages against baseline approaches for solar image analysis (multi-channel coronal hole detection, SPOCA for ARs) and state-of-the-art deep learning methods (Faster RCNN, U-Net). Additionally, both detection and segmentation stages are quantitatively validated on artificially created data of similar spatial configurations made from annotated multi-modal magnetic resonance images. Our framework achieves an average of 0.72 IoU (segmentation) and 0.90 F1 score (detection) across all modalities, comparing to the best performing baseline methods with scores of 0.53 and 0.58, respectively, on the artificial dataset, and 0.84 F1 score in the AR detection task comparing to baseline of 0.82 F1 score. Our segmentation results are qualitatively validated by an expert on real ARs.

Mapping of a multilayer panoramic radiography device.

To map the shape, location, and thickness of the focal trough of a panoramic radiography device with a multilayer imaging program. An acrylic plate (148 × 148 × 3 mm) containing 1156 holes distributed in a matrix of 34 × 34 rows was placed in the OP300 Maxio at the levels of the maxilla and mandible. 20 metal spheres (3.5 mm in diameter) were placed on the holes of the plate under 15 different arrangements and panoramic images were acquired for each arrangement at 66 kV, 8 mA, and an exposure time of 16 s. The resulting panoramic radiographs from the five image layers were exported, the horizontal and vertical dimensions of the metal spheres were measured in all images using the Image J software, and the magnification and distortion rates of the spheres were calculated. All metal spheres presenting a magnification rate lower than 30% in both vertical and horizontal dimensions and a distortion rate lower than 10% were considered to map the focal troughs of each of the five image layers. All panoramic image layers had a curved shape ranging from 39° to 51° for both dental arches and varied in position and thickness. The anterior region of maxilla was anteriorly displaced when compared to the anterior region of the mandible for all layers. Image layers are thicker at the level of the mandible than those at the level of the maxilla; also, inner layers were thinner and outer layers were thicker. All image layers in the studied panoramic radiography device had a curved shape and varied in position and thickness. The anterior region of maxilla was anteriorly displaced when compared to that of the mandible for all layers.

A multilayer network-enabled ultrasonic image series analysis approach for online cancer drug delivery monitoring

The objective of this study is to develop an effective data-driven methodology for the online monitoring of cancer drug delivery guided by the ultrasonic images. To achieve this goal, effective image quantification and accurate feature extraction play a critical role on image-guided drug delivery (IGDD) monitoring. However, the existing image-guided approaches in such area are mainly focused on the analysis for individual images rather than the image series. In fact, the temporal patterns between consecutive images may contain critical information and it is necessary to be considered in the monitoring analysis. In addition, the conventional approaches, such as the pure intensity-based method, also do not sufficiently consider the effects of noise in the ultrasonic images, which also limits the monitoring sensitivity and accuracy. To address the challenges, this paper proposed a novel multilayer network-enabled IGDD (MNE-IGDD) monitoring approach. The contributions of the proposed method can be summarized into three aspects: (1) formulate the sequential ultrasound images to a multilayer network by the proposed spatial-regularized distance; (2) detect drug delivery area based on community detection algorithm of multilayer network; and (3) quantify the drug delivery progress by incorporating the image intensity-based features with the detected community. Both the detected communities and feature increment percentages are applied as the evaluation metric for validation. A simulation study was conducted and this method was also applied to a real-world mouse colon tumor treatment case study under three temperature conditions. Both simulation and the real-world case studies demonstrated that the proposed method is promising to achieve satisfactory monitoring performance in clinical trials.

Beam's-Eye-View Imaging of Liver SBRT With a Novel Multi-Layer Imager

Beams-eye view (BEV) imaging during radiation therapy can be used for real-time localization of the treatment target. However, clinical implementation is limited by the poor performance of current electronic portal imaging devices (EPIDs). A novel multilayer imager (MLI), consisting of four stacked conventional flat-panel imagers, was recently shown to have 5.7 times the detective quantum efficiency and nearly 2 times the contrast-to-noise ratio of a conventional EPID. In the current study, we compare the MLI against a single layer imager for the application of fiducial tracking during liver SBRT procedures. The hypothesis is that the MLI will provide more accurate and efficient fiducial tracking.The prototype MLI was installed in the standard MV imager housing on a clinical LINAC, replacing the conventional EPID. The MV imager arm was extended during volumetric modulated arc therapy (VMAT) SBRT treatments for passive data acquisition. Six patients with liver metastases receiving SBRT in 3-5 fractions (900-1200 cGy/fx) were studied. Data was acquired for two fractions of each treatment course, one fraction with the MLI using all 4 layers and one with the MLI using the top layer only. Automatic fiducial localization was performed using a modification of a previously published algorithm. Truth data was derived using external surrogate respiratory traces combined with manual tracking. Results for 4- and 1-layer modes were compared against truth data to determine tracking accuracy and efficiency. Tracking and noise improvements were assessed for any correlation.Between 2771 to 6505 image frames were acquired for each patient, with roughly half of each corresponding to acquisition in 1-layer mode and the other half 4-layer mode. Tracking accuracy with the 1-layer imager returned a root mean square error (RMSE) of 2.1 mm compared to 4-layer RMSE of 1.5 mm, a statistically significant (P < 0.001) improvement of 0.6 mm. Successful tracking efficiency with 4 layers improved to 82.8% versus 58.4% for the 1-layer mode, a relative improvement of 41.8% (P < 0.001). There was a strong correlation (r = 0.913) between the increase in successfully tracked frames and reduction in noise when using the 4-layer mode.Noise reduction and increased MV photon detection efficiency achieved by utilizing a multi-layer MV imager results in improved fiducial tracking for liver SBRT treatments. Future BEV clinical applications may be improved by pursuing similar noise reduction and photon detection enhancement strategies.

Quantitative and anatomical imaging of dermal angiopathy by noninvasive photoacoustic microscopic biopsy.

The ability to noninvasively acquire the fine structure of deep tissues is highly valuable but remains a challenge. Here, a photoacoustic microscopic biopsy (PAMB) combined switchable spatial-scale optical excitation with single-element depth-resolved acoustic detection mode was developed, which effectively coordinated the spatial resolution and the penetration depth for visualizations of skin delamination and chromophore structures up to reticular dermis depth, with the lateral resolution from 1.5 to 104 μm and the axial resolution from 34 to 57 μm. The PAMB obtained anatomical imaging of the pigment distribution within the epidermis and the vascular patterns of the deep dermal tissue, enabling quantification of morphological abnormalities of angiopathy without the need for exogenous contrast agents. The features of healthy skin and scar skin, and the abnormal alteration of dermal vasculature in port wine stains (PWS) skin were first precisely displayed by PAMB-shown multi-layered imaging. Moreover, the quantitative vascular parameters evaluation of PWS were carried out by the detailed clinical PAMB data on 174 patients, which reveals distinct differences among different skin types. PAMB captured the PWS changes in capillary-loop depth, diameter, and vascular volume, making it possible to perform an objective clinical evaluation on the severity of PWS. All the results demonstrated the PAMB can provide vascular biopsy and new indexes deep into the dermal skin noninvasively, which should be meaningful to timely evaluate the pathological types and treatment response of skin diseases. This opens up a new perspective for label-free and non-invasive biopsies of dermal angiopathy.

Art as Advocacy: Protecting the U.S.-Mexico Border Environment in Amanda Keller Konya's "Specimens"

Amanda Keller-Konya's multilayered photographic image construction in "Specimens" from North America's Most Polluted River" helps the viewer perceive the magnitude of the damage sustained by US-Mexico's border area residents in California's Imperial Valley and the effort necessary to clean the area's New River. The river originates in Mexicali city, capital of Baja California, northwestern Mexico, crosses the border, flows north through California's Imperial Valley, and empties into the Salton Sea, the state's largest and most contaminated lake. A sustainable environment is crucial to the health of the valley's residents and farms that supply most of the nation's winter produce. Recovery of the area's ecosystem requires solving the impact of agricultural runoff and toxic dust as less water flows into the Salton Sea, a body of water vital for the wellbeing of Southern Californians, the fish, and the migratory birds that depend on it for survival.

A Deep-learning Approach for Modeling Phase-change Metasurface in the Mid-infrared

Reconfigurable metasurface constitutes an important block for future adaptive and smart nanophotonic applications. In this work we introduce a new modeling approach for the fast design of tunable and reconfigurable metasurface structures using convolutional deep learning network. The metasurface structure is modeled as a multilayer image tensor to model the material properties as image maps. The dimensionality mismatch problem is avoided by using the operating wavelength as an input to the network, so the model is used as single-point solver. As a case study, we model the response of a reconfigurable absorber employing phase transition of vanadium dioxide in the mid-infrared. The results show that our model provides accurate prediction of the metasurface response using small training dataset.

Improvements in beam’s eye view fiducial tracking using a novel multilayer imager

Purpose. Electronic portal image devices (EPIDs) have been investigated previously for beams-eye view (BEV) applications such as tumor tracking but are limited by low contrast-to-noise ratio and detective quantum efficiency. A novel multilayer imager (MLI), consisting of four stacked flat-panels was used to measure improvements in fiducial tracking during liver stereotactic body radiation therapy (SBRT) procedures compared to a single layer EPID. Methods. The prototype MLI was installed on a clinical TrueBeam linac in place of the conventional DMI single-layer EPID. The panel was extended during volumetric modulated arc therapy SBRT treatments in order to passively acquire data during therapy. Images were acquired for six patients receiving SBRT to liver metastases over two fractions each, one with the MLI using all 4 layers and one with the MLI using the top layer only, representing a standard EPID. The acquired frames were processed by a previously published tracking algorithm modified to identify implanted radiopaque fiducials. Truth data was determined using respiratory traces combined with partial manual tracking. Results for 4- and 1-layer mode were compared against truth data for tracking accuracy and efficiency. Tracking and noise improvements as a function of gantry angle were determined. Results. Tracking efficiency with 4-layers improved to 82.8% versus 58.4% for the 1-layer mode, a relative improvement of 41.7%. Fiducial tracking with 1-layer returned a root mean square error (RMSE) of 2.1 mm compared to 4-layer RMSE of 1.5 mm, a statistically significant (p < 0.001) improvement of 0.6 mm. The reduction in noise correlated with an increase in successfully tracked frames (r = 0.913) and with increased tracking accuracy (0.927). Conclusion. Increases in MV photon detection efficiency by utilization of a MLI results in improved fiducial tracking for liver SBRT treatments. Future clinical applications utilizing BEV imaging may be enhanced by including similar noise reduction strategies.

Full-field optical coherence tomography using tunable-path-difference source for three-dimensional multi-layered imaging with enhanced signal-to-noise ratio

We demonstrate a novel scheme for a full-field optical coherence tomography (FF-OCT) system using a tunable-path-difference source (TPDS). Recently, the comb-spacing-swept source (CSWS) method, including TPDS, has been successfully applied to improve the stability of three-dimensional (3D) time-domain (TD)-FF-OCT imaging by eliminating the mechanical scanning of the reference path, which has been the main source of undesirable noise in general TD-OCT. However, CSWS-OCT has the drawback of a degraded signal-to-noise ratio (SNR) from a duplicated interferometer structure. In this study, we improve the configuration of the TPDS so that it can be connected directly to the measurement interferometer, including the sample and reference paths of the OCT system. In our novel TPDS-OCT, a propagating optical path through only a single interferometer was critically helpful in enhancing the SNR while retaining the benefits of CSWS-OCT, and a free-space full-field interferometer of dual-input Mach–Zehnder type also has an even higher imaging performance owing to the simultaneous exposure of optical intensity into the camera and the short image acquisition time laterally. We theoretically and experimentally demonstrated the benefits of TPDS-OCT compared with CSWS-OCT in terms of SNR. We also demonstrated 3D multi-layered imaging, which CSWS-OCT cannot achieve owing to its limited SNR.

Abbreviated on-treatment CBCT using roughness penalized mono-energization of kV-MV data and a multi-layer MV imager

Simultaneous acquisition of cone beam CT (CBCT) projections using both the kV and MV imagers of an image guided radiotherapy system reduces set-up scan times—a benefit to lung cancer radiation oncology patients—but increases noise in the 3D reconstruction. In this article, we present a kV‐MV scan time reduction technique that uses two noise-reducing measures to achieve superior performance. The first is a high-DQE multi-layer MV imager prototype. The second is a beam hardening correction algorithm which combines poly-energetic modeling with edge-preserving, regularized smoothing of the projections. Performance was tested in real acquisitions of the Catphan 604 and a thorax phantom. Percent noise was quantified from voxel values in a soft tissue volume of interest (VOI) while edge blur was quantified from a VOI straddling a boundary between air and soft material. Comparisons in noise/resolution performance trade-off were made between our proposed approach, a dose-equivalent kV-only scan, and a kV‐MV reconstruction technique previously published by Yin et al (2005 Med. Phys. 32 9). The proposed technique demonstrated lower noise as a function of spatial resolution than the baseline kV‐MV method, notably a 50% noise reduction at typical edge blur levels. Our proposed method also exhibited fainter non-uniformity artifacts and in some cases superior contrast. Overall, we find that the combination of a multi-layer MV imager, acquiring at a LINAC source energy of 2.5 MV, and a denoised beam hardening correction algorithm enables noise, resolution, and dose performance comparable to standard kV-imager only set-up CBCT, but with nearly half the gantry rotation time.

Multi-layer color image encryption using random matrix affine cipher, RP2DFrHT and 2D Arnold map

Confidentiality, integrity, authenticity, non-repudiation and storing and transmitting images over the unsecured channel has become a challenging task nowadays. In this scenario, a robust image encryption technique over open network has grasped a great deal of attention. In this paper to meet this challenge, we have established a new multi-layer robust color image encryption using random matrix affine cipher (RMAC), reality preserving two dimensional discrete fractional Hartley transform (RP2DFrHT) and two dimensional Arnold map. The first stage of encryption is designed through RMAC. RMAC provides security in co-ordinate domain as well as in geometrical domain. So if a hacker has knowledge about all the pixels of an image, but has no information about the mechanism of co-ordinate domain he/she cannot steal any information. The second stage of encryption is obtained incorporating the concept of RP2DFrHT. The reality preserving transform eliminates the complex-valued coefficients and provides the real-valued coefficients of encrypted image. The real-valuedness of data provides convenient platform for display, storage and transmission in digital domain. The third stage of encryption is done using 2D Arnold map, which not only enhances the security but also enlarges key space. Therefore, the proposed technique provides security in geometrical, co-ordinate, frequency and time domains simultaneously. The security of our proposed technique depends upon the secret keys as well as their correct arrangements. Simulation analysis provides the complete visual results of all stages of encrypted and decrypted images. Sensitivity analysis validates that our proposed technique is highly sensitive towards its secret keys and their arrangements. Statistical analysis such as histogram analysis, MSE, PSNR, correlation coefficient, entropy analysis and resistivity of classical attacks validates the effectiveness and feasibility of our proposed work. Moreover, comparison analysis testifies that our proposed technique functions significantly well as compared to existing similar techniques.