Acquired 2D Images Research Articles

New 2D to 3D Reconstruction of Heterogeneous Porous Media via Deep Generative Adversarial Networks (GANs)

AbstractAccurately characterizing rock microstructures in three dimensions (3D) is crucial for modeling various physical phenomena and estimating rock properties. Despite advancements in 3D imaging, limitations arise from the trade‐off between sample size and resolution, particularly in heterogeneous rocks with multi‐scale features where both high resolution and a large field of view are essential. These challenges have prompted interest in accurate 3D reconstructions from high‐resolution two‐dimensional (2D) images using advanced generative models like generative adversarial networks (GANs). In this study, using scanning electron microscopy and optical microscopy, we acquired 2D images from three orthogonal sections of a Berea sandstone sample. These images were employed to train a modified SliceGAN model, a variant of GANs, for 3D reconstruction. Unlike previous studies utilizing methods for 2D to 3D reconstructions that typically incorporated 3D images in their training, our approach relies exclusively on 2D images. We propose a systematic workflow which enables us to produce 3D reconstructions that closely mirror the original 2D inputs and a 3D X‐ray tomography in terms of structural and morphological characteristics. Additionally, we highlight the importance of input data size and training our model with representative images which enable us to generate diverse reconstructions with transport properties that align with previous studies on Berea sandstone. This underscores the potential of 2D to 3D reconstructions as an effective alternative to multiple X‐ray tomographies, integral for assessing variability in heterogeneous rocks.

High frequency acoustic microscopy imaging of pellet cladding interface in nuclear fuel rods

In Pressurized Water Reactors (PWRs), fuel rods play a crucial role in generating nuclear energy. These rods consist of ceramic pellets, such as UO2 or (U,Pu)O2, enclosed in a zircaloy cladding tube, leaving an initial gap between the pellets and the cladding. As the reactor operates and the fuel undergoes irradiation, both the ceramic pellets and the zircaloy cladding experience transformations, causing the gap between them to gradually close. This phenomenon has a significant impact on the thermomechanical behavior of the fuel rod.Understanding the nature of the bonding that occurs during irradiation is essential for ensuring the safe and efficient operation of the reactor. To investigate the evolution of the contact state between the fuel pellets and the cladding during irradiation, a detailed analysis of the pellet-cladding interface after irradiation is necessary. However, traditional examination methods might be destructive or incapable of providing the desired level of precision and resolution.The Institute of Electronic and Systems at the University of Montpellier (IES – UMR CNRS 5214), in collaboration with the Alternative Energies and Atomic Energy Commission (CEA) and Electricité de France (EDF), has developed a specialized high-frequency acoustic microscope for imaging and non-destructively inspecting the pellet/cladding interface. The design of the acoustic microscope takes into account the complexity of the fuel rod's structure and the challenges associated with imaging the pellet/cladding interface by utilizing high-frequency ultrasound.In this paper, we present the ability of this acoustic microscope to acquire 2D images with controlled displacements of the sample rod along both its axial and circumferential directions thanks to a card with a high sampling frequency reaching 2 GHz. This capability is crucial because the geometrical, chemical, and mechanical properties of the fuel pellet-cladding contact are not uniform in these directions. By obtaining detailed acoustic images, we can identify specific regions where the fuel pellets and the cladding were in contact during irradiation. In this research, a resolution study is carried out to validate the microscope's ability to investigate the fuel rod and achieve the desired resolutions.Testing on real samples requires a specific configuration of the microscope, which must be adapted to the irradiation conditions. This is why, before proceeding to this stage, it is necessary to carry out tests on representative samples to validate the achievement of the desired resolution. So we’re also presenting the first acoustic images obtained on the zircaloy alloy claddings.

Three-dimensional imaging in reflection phase microscopy with minimal axial scanning.

Reflection phase microscopy is a valuable tool for acquiring three-dimensional (3D) images of objects due to its capability of optical sectioning. The conventional method of constructing a 3D map is capturing 2D images at each depth with a mechanical scanning finer than the optical sectioning. This not only compromises sample stability but also slows down the acquisition process, imposing limitations on its practical applications. In this study, we utilized a reflection phase microscope to acquire 2D images at depth locations significantly spaced apart, far beyond the range of optical sectioning. By employing a numerical propagation, we successfully filled the information gap between the acquisition layers, and then constructed complete 3D maps of objects with substantially reduced number of axial scans. Our experimental results also demonstrated the effectiveness of this approach in enhancing imaging speed while maintaining the accuracy of the reconstructed 3D structures. This technique has the potential to improve the applicability of reflection phase microscopy in diverse fields such as bioimaging and material science.

Geometric Reliability of Super-Resolution Reconstructed Images from Clinical Fetal MRI in the Second Trimester

Fetal Magnetic Resonance Imaging (MRI) is an important noninvasive diagnostic tool to characterize the central nervous system (CNS) development, significantly contributing to pregnancy management. In clinical practice, fetal MRI of the brain includes the acquisition of fast anatomical sequences over different planes on which several biometric measurements are manually extracted. Recently, modern toolkits use the acquired two-dimensional (2D) images to reconstruct a Super-Resolution (SR) isotropic volume of the brain, enabling three-dimensional (3D) analysis of the fetal CNS.We analyzed 17 fetal MR exams performed in the second trimester, including orthogonal T2-weighted (T2w) Turbo Spin Echo (TSE) and balanced Fast Field Echo (b-FFE) sequences. For each subject and type of sequence, three distinct high-resolution volumes were reconstructed via NiftyMIC, MIALSRTK, and SVRTK toolkits. Fifteen biometric measurements were assessed both on the acquired 2D images and SR reconstructed volumes, and compared using Passing-Bablok regression, Bland-Altman plot analysis, and statistical tests.Results indicate that NiftyMIC and MIALSRTK provide reliable SR reconstructed volumes, suitable for biometric assessments. NiftyMIC also improves the operator intraclass correlation coefficient on the quantitative biometric measures with respect to the acquired 2D images. In addition, TSE sequences lead to more robust fetal brain reconstructions against intensity artifacts compared to b-FFE sequences, despite the latter exhibiting more defined anatomical details.Our findings strengthen the adoption of automatic toolkits for fetal brain reconstructions to perform biometry evaluations of fetal brain development over common clinical MR at an early pregnancy stage.

Denoising of the Poisson-Noise Statistics 2D Image Patterns in the Computer X-ray Diffraction Tomography

A central point of validity of computer X-ray diffraction micro tomography is to improve the digital contrast and spatial resolution of the 3D-recovered nano-scaled objects in crystals. In this respect, the denoising issue of the 2D image patterns data involved in the 3D high-resolution recovery processing has been treated. The Poisson-noise simulation of 2D image patterns data was performed; afterwards, it was employed for recovering nano-scaled crystal structures. By using the statistical average and geometric means methods of the acquired 2D image frames, we showed that the statistical average hypothesis works well, at least in the case of 2D Poisson-noise image data related to the Coulomb-type point defect in a crystal Si(111). The validation of results related to the de-noised 2D IPs data obtained was carried out by both the 3D recovery processing of the Coulomb-type point defect in a crystal Si(111) and using the peak signal-to-noise ratio (PSNR) criterion.

Self-Supervised Learning-Based High-Resolution Ultrasound Imaging for Prostate Brachytherapy

Ultrasound (US) imaging has been widely used in image-guided needle/seed placement in prostate brachytherapy. For 3D US images with large slice thickness, high frequency information in the slice direction is missing and cannot be resolved through interpolation. As an ill-posed problem, most high-resolution generation methods rely on the presence of external/training atlases to learn the transform from low resolution to high resolution images. The anatomical variations among individuals bring unavoidable uncertainties in this atlas-based learning mechanism. In this study, we propose a self-supervised learning method, which does not use any external high-resolution atlas images, yet can resolve high resolution images from the 3D images with a large slice thickness.In 3D US imaging, in-plane spatial resolution is generally much higher than through-plane resolution. Considering the natural isotropy of US imaging on biological tissue, for a certain patient, the mapping from low-resolution to high-resolution images can be established by learning the mapping from down-sampled in-plane images (low-resolution) to original in-plane US images (high-resolution), which enables the generation of high-resolution through-plane images. Two independent cycle consistent generative adversarial networks (CycleGANs) are trained with paired original in-plane US images and two sets of in-plane down-sampled US images. Finally, high-resolution 3D US images are reconstructed by combining the generated 2D images obtained via feeding through-plane images into the two CycleGAN models. A validation study was conducted to quantitatively assess the proposed method using 3D high in-/through-plane resolution breast US images of 50 breast cancer patients. The feasibility of the proposed method for obtaining high-resolution US images for prostate brachytherapy was tested using 3D US images with 2-mm slice thickness from 45 prostate cancer patients.In the breast US images validation test using a three-times spatial resolution enhancement, the proposed method achieved the mean absolute error value of 0.90 (range 0.57-1.35), the peak signal-to-noise ratio value of 37.88 (range 35.84-40.48) dB, and the visual information fidelity value of 0.69 (range 0.68-0.72), which significantly outperforms bicubic interpolation. For the prostate cases, the proposed method outperformed the bicubic interpolation for the spatial resolution enhancement factors of 5 and 10.A novel deep learning-based algorithm for reconstructing high-resolution 3D US images from sparely acquired 2D images was developed and validated. Significant improvement on through-plane resolution has been achieved by using the acquired 2D images without any external atlas images. Its self-supervision capability could accelerate high-resolution US imaging for prostate brachytherapy.

Etiology-Specific Remodeling in Ventricular Tissue of Heart Failure Patients and Its Implications for Computational Modeling of Electrical Conduction.

With an estimated 64.3 million cases worldwide, heart failure (HF) imposes an enormous burden on healthcare systems. Sudden death from arrhythmia is the major cause of mortality in HF patients. Computational modeling of the failing heart provides insights into mechanisms of arrhythmogenesis, risk stratification of patients, and clinical treatment. However, the lack of a clinically informed approach to model cardiac tissues in HF hinders progress in developing patient-specific strategies. Here, we provide a microscopy-based foundation for modeling conduction in HF tissues. We acquired 2D images of left ventricular tissues from HF patients (n = 16) and donors (n = 5). The composition and heterogeneity of fibrosis were quantified at a sub-micrometer resolution over an area of 1 mm2. From the images, we constructed computational bidomain models of tissue electrophysiology. We computed local upstroke velocities of the membrane voltage and anisotropic conduction velocities (CV). The non-myocyte volume fraction was higher in HF than donors (39.68 ± 14.23 vs. 22.09 ± 2.72%, p < 0.01), and higher in ischemic (IC) than nonischemic (NIC) cardiomyopathy (47.2 ± 16.18 vs. 32.16 ± 6.55%, p < 0.05). The heterogeneity of fibrosis within each subject was highest for IC (27.1 ± 6.03%) and lowest for donors (7.47 ± 1.37%) with NIC (15.69 ± 5.76%) in between. K-means clustering of this heterogeneity discriminated IC and NIC with an accuracy of 81.25%. The heterogeneity in CV increased from donor to NIC to IC tissues. CV decreased with increasing fibrosis for longitudinal (R2 = 0.28, p < 0.05) and transverse conduction (R2 = 0.46, p < 0.01). The tilt angle of the CV vectors increased 2.1° for longitudinal and 0.91° for transverse conduction per 1% increase in fibrosis. Our study suggests that conduction fundamentally differs in the two etiologies due to the characteristics of fibrosis. Our study highlights the importance of the etiology-specific modeling of HF tissues and integration of medical history into electrophysiology models for personalized risk stratification and treatment planning.

Self-supervised learning for accelerated 3D high-resolution ultrasound imaging.

Ultrasound (US) imaging has been widely used in diagnosis, image-guided intervention, and therapy, where high-quality three-dimensional (3D) images are highly desired from sparsely acquired two-dimensional (2D) images. This study aims to develop a deep learning-based algorithm to reconstruct high-resolution (HR) 3D US images only reliant on the acquired sparsely distributed 2D images. We propose a self-supervised learning framework using cycle-consistent generative adversarial network (cycleGAN), where two independent cycleGAN models are trained with paired original US images and two sets of low-resolution (LR) US images, respectively. The two sets of LR US images are obtained through down-sampling the original US images along the two axes, respectively. In US imaging, in-plane spatial resolution is generally much higher than through-plane resolution. By learning the mapping from down-sampled in-plane LR images to original HR US images, cycleGAN can generate through-plane HR images from original sparely distributed 2D images. Finally, HR 3D US images are reconstructed by combining the generated 2D images from the two cycleGAN models. The proposed method was assessed on two different datasets. One is automatic breast ultrasound (ABUS) images from 70 breast cancer patients, the other is collected from 45 prostate cancer patients. By applying a spatial resolution enhancement factor of 3 to the breast cases, our proposed method achieved the mean absolute error (MAE) value of 0.90±0.15, the peak signal-to-noise ratio (PSNR) value of 37.88±0.88dB, and the visual information fidelity (VIF) value of 0.69±0.01, which significantly outperforms bicubic interpolation. Similar performances have been achieved using the enhancement factor of 5 in these breast cases and using the enhancement factors of 5 and 10 in the prostate cases. We have proposed and investigated a new deep learning-based algorithm for reconstructing HR 3D US images from sparely acquired 2D images. Significant improvement on through-plane resolution has been achieved by only using the acquired 2D images without any external atlas images. Its self-supervision capability could accelerate HR US imaging.

3D Image Generation From Single Image Using Color Filtered Aperture and 2.1D Sketch-A Computational 3D Imaging System and Qualitative Analysis

A new method for generating 3D images using both disparity parameters due to the color filtered aperture (CFA) and depth alignment of the multi-plane images for an acquired 2D image using a novel computational imaging system is presented. A CFA is created using add-on red, green, and blue color filters in front of the objective lens for a conventional 2D camera system. The multiple plane images are computed using various image regions identified by 2.1D sketch and the corresponding inter-region depth information with the aid of color disparities caused in the acquired 2D image. Our emphasis is to handle possible occlusions in single 2D image regions due to various object features for the acquired scene using ordered multiple plane images and to generate a 3D image by exploiting the order existing in the computed multiple plane images and compositing the region with aligned inter-image region depth information. The simulation and analysis demonstrate the elegance of the 3D image generation. Ultimately, the proposed computational imaging system is not only cost-effective but also an alternative to the stereo-based imaging system, which is not suited for ophthalmic or endoscopic imaging applications.

Statistical analysis and comparative study of multi-scale 2D and 3D shape features for unbound granular geomaterials

Morphological analysis is a prerequisite step for studying the mechanical properties of unbound granular geomaterials through both laboratory experiments and numerical simulations. In this study, two typical geomaterials, e.g., ballast and cobble particles, were sampled for study. First, a systematic framework was established to acquire two-dimensional (2D) particle outline and three-dimensional (3D) morphology for statistical shape analysis. The photographic-based system was set up to acquire 2D images of particle projections from multiple surrounding directions, then the images obtained were used to reconstruct 3D particle morphology based on close-range photogrammetry. Second, in order to quantify particle shape at three different geometric scales, 6 well-acknowledged 2D and 3D geometry descriptors, i.e., aspect ratio, sphericity, convexity, roundness, regularity, and roughness were computed. Third, the probability distribution function (PDF) of shape descriptors were fitted with different functions. Finally, 2D and 3D shape descriptors were statistically compared and the relationships between them were revealed by analyzing their cumulative distribution functions (CDFs).

Ca2+ imaging of neurons in freely moving rats with automatic post hoc histological identification

BackgroundCognitive neuroscientists aim to understand behavior often based on the underlying activity of individual neurons. Recently developed miniaturized epifluorescence microscopes allow recording of cellular calcium transients, resembling neuronal activity, of individual neurons even in deep brain areas in freely behaving animals. At the same time, molecular markers allow the characterization of diverse neuronal subtypes by post hoc immunohistochemical labeling. Combining both methods would allow researchers to increase insights into how individual neuronal activity and entities contribute to behavior. New methodHere, we present a novel method for identifying the same neurons, recorded with calcium imaging using a miniaturized epifluorescence microscope, post hoc in fixed histological sections. This allows immunohistochemical investigations to detect the molecular signature of in vivo recorded neurons. Our method utilizes the structure of blood vessels for aligning in vivo acquired 2D images with a reconstructed 3D histological model. ResultsWe automatically matched, 60 % of all in vivo recorded cells post hoc in histology. Across all animals, we successfully matched 43 % to 89 % of the recorded neurons. We provide a measure for the confidence of matched cells and validated our method by multiple simulation studies. Comparison with existing methodsTo our knowledge, we present the first method for matching cells, recorded with a miniaturized epifluorescence microscope in freely moving animals, post hoc in histological sections. ConclusionsOur method allows a comprehensive analysis of how cortical circuits relate to freely moving animal behavior by combining functional activity of individual neurons with their underlying histological profiles.

3D-Focused ion beam tomography and quantitative porosity evaluation of ZrO2-SiO2 composite coating; amorphous SiO2 as a porosity tailoring agent

The current study is focused on refining effect of amorphous SiO2 on porosity and interlayer formation of sintered ZrO2-SiO2 composite coatings produced on anodic oxidized 316L substrate. The SiO2 improved particle interaction with the substrate surface and increased thickness of the interlayer formed at the coating/substrate interface. For porosity evaluation, the coatings were sliced and imaged layer by layer using focused ion beam-scanning electron microscopy and 3D visualizations reconstructed by stacking the acquired 2D images. Volume fraction, dimension, distribution and connectivity of pores as a function of coating composition were analyzed. The results showed that SiO2 also densifies the coating and decreases volume fraction of connected pores. Corrosion performance of ZrO2-SiO2 coatings was improved with increasing SiO2 content due to the higher barrier effect resulted by increasing the interlayer thickness as well as the lower permeability supported by lower percent of connected pores.

Finger-mounted quantitative micro-elastography.

We present a finger-mounted quantitative micro-elastography (QME) probe, capable of measuring the elasticity of biological tissue in a format that avails of the dexterity of the human finger. Finger-mounted QME represents the first demonstration of a wearable elastography probe. The approach realizes optical coherence tomography-based elastography by focusing the optical beam into the sample via a single-mode fiber that is fused to a length of graded-index fiber. The fiber is rigidly affixed to a 3D-printed thimble that is mounted on the finger. Analogous to manual palpation, the probe compresses the tissue through the force exerted by the finger. The resulting deformation is measured using optical coherence tomography. Elasticity is estimated as the ratio of local stress at the sample surface, measured using a compliant layer, to the local strain in the sample. We describe the probe fabrication method and the signal processing developed to achieve accurate elasticity measurements in the presence of motion artifact. We demonstrate the probe's performance in motion-mode scans performed on homogeneous, bi-layer and inclusion phantoms and its ability to measure a thermally-induced increase in elasticity in ex vivo muscle tissue. In addition, we demonstrate the ability to acquire 2D images with the finger-mounted probe where lateral scanning is achieved by swiping the probe across the sample surface.

Modelling farmland topography for suitable site selection of dam construction using unmanned aerial vehicle (UAV) photogrammetry

UAV photogrammetry and 3D mapping are gaining fast and wide applications around the world majorly due to the relatively low-cost advantage it offers in the acquisition of high resolution 3D topographic models when compared to LiDAR. This research seeks to demonstrate the applicability of UAV photogrammetry and Geographic Information System (GIS) in modelling the topography of a farm land located in Kwandere community, Lafia, Nasarawa state, Nigeria, for the selection of a suitable site for the construction of an earth-fill dam. The image data was acquired with the aid of a DJI Phantom 2 UAV at 120 m flying height, with a 3.61 mm camera focal length and at 65% overlap. The acquired images were processed using Agisoft Photoscan digital photogrammetric software and 20 GCPs were used for the georeferencing and check points for accuracy assessment. The Digital Elevation Model (DEM) of the area was generated from the acquired 2D image sequences using Structure from Motion (SfM) techniques, at a horizontal and vertical accuracy that falls within the desirable threshold according to NSSDA standard. Using the hydrologic tool of ArcGIS 10.3.1, the produced DEM together with the contour map of the area was finally used to determine the suitable location for the siting of dam using topographic constraints. The results obtained demonstrate the robustness of UAS products and their usefulness for planning purposes in construction and engineering applications.

Model-Interpolated Gating for Magnetic Resonance Image–Guided Radiation Therapy

To develop and validate a technique for radiation therapy gating using slow (≤1 frame per second) magnetic resonance imaging (MRI) and a motion model. Proposed uses of the technique include radiation therapy gating using T2-weighted images and conducting additional imaging studies during gated treatments. The technique uses a physiologically guided breathing motion model to interpolate deformed target position between 2-dimensional (2D) MRI images acquired every 1 to 3 seconds. The model is parameterized by a 1-dimensional respiratory bellows surrogate and is continuously updated with the most recently acquired 2D images. A phantom and 8 volunteers were imaged with a 0.35T MRI-guided radiation therapy system. A balanced steady-state free precession sequence with a 2D frame rate of 3 frames per second was used to evaluate the technique. The accuracy and beam-on positive predictive value (PPV) of the model-based gating decisions were evaluated using the gating decisions derived from imaging as a ground truth. A T2-weighted gating offline proof-of-concept study using a half-Fourier, single-shot, turbo-spin echo sequence is reported. Model-interpolated gating accuracy, beam-on PPV, and median absolute distances between model and image-tracked target centroids were, on average, 98.3%, 98.4%, and 0.33 mm, respectively, in the balanced steady-state free precession phantom studies and 93.7%, 92.1%, and 0.86 mm, respectively, in the volunteer studies. T2 model-interpolated gating in 6 volunteers yielded an average accuracy and PPV of 94.3% and 92.5%, respectively, and the mean absolute median distance between modeled and imaged target centroids was 0.86 mm. This work demonstrates the concept of model-interpolated gating for MRI-guided radiation therapy. The technique was found to be potentially sufficiently accurate for clinical use. Further development is needed to accommodate out-of-plane motion and the use of an internal MR-based respiratory surrogate.

Viewpoint projection based deep feature learning for single and dyadic action recognition

Usage of depth data in various computer vision applications gains popularity with cheap depth sensors available on the market. In this study, a method that utilizes Deep Learning is proposed for single human actions and dyadic actions. Depth data of the actions are used to construct a three-dimensional (3D) template. These templates are rotated in different directions and two-dimensional (2D) views from different angles are stored. Acquired 2D images are used in deep feature extraction. AlexNet (Krizhevsky, Sutskever, & Hinton, 2012) pre-trained convolutional neural network is used for deep feature extraction. For all viewpoints, deep features are extracted and concatenated. After that random forest classifier is used for action recognition. The contributions of this paper are as follows. First, a 3D isosurface models is constructed to figure actions sequence. Second, we project 3D shapes into a 2D feature space by taking its snapshots from different views and giving them to a pre-trained CNN as input for feature extraction. We explore more information about the actions by extracting features from different layers of the deep neural network. We compare the results of the deep features extracted from different layers of the pre-trained CNN. Various classifiers are trained with extracted deep features. Because of the complex structure of the 3D shapes, there are a limited number of feature extraction methods. Our method obtains close to state-of-the-art performance on various single and two-person action datasets.

Enhancement in the kV Portal Image Contrast Using Depth Normalization for Accurate Patient Localization

The X-ray imaging modality has played an important role in the daily verification of a patient’s position for accurate radiation treatment (RT), especially in modern RT techniques that require highly precise patient localization. Although cone-beam computer tomography (CBCT) has already been introduced to the RT field, two-dimensional (2D) X-ray portal imagers have been more widely used than CBCT for the daily check of patient localization owing to the lower patient exposure and time consumption. However, the 2D imager practically provide a lower bone-tissue contrast than CBCT. Thus, we propose a method that enhances the image contrast of daily acquired 2D images by using just one CBCT scan during an entire RT course. This method operates on the basis of the depth normalization (DN) technique and requires no artificial data manipulation such as image post-processing filters. We implemented the algorithm for the portal images of a cylindrical phantom and three patients. From DN results, the image contrast for the phantom and the patients increased by factors of 26.3 and 13.4 on average, and their contrast-to-noise ratios were maintained at differences of 8.6% and 7.7% on average, respectively. Moreover, the DN method provided a stronger contrast enhancement at lower doses that could suppress the imaging exposure and thus improve the patient’s safety. Therefore, the verification of RT patient localization is expected to be performed more efficiently and accurately by using the DN method.

Three-dimensional surface microtopography recovery from a multifocus image sequence using an omnidirectional modified Laplacian operator with adaptive window size.

Optical microscopy enables the observation of highly magnified objects and material structures on microsurfaces, but it can only acquire 2D images. In order to observe areal features more accurately and intuitively, 3D surface microtopography recovery has been applied to form a 3D surface model of an object from its 2D image sequence. In the 3D reconstruction of the focus evaluation operator, we have the gray variance operator, the gray-scale difference absolute sum operator, the Roberts gradient operator, the Tenengrad gradient operator, the improved Laplace operator, etc. There are two problems with these operators: one is that there is no difference between (x,y) and the gray scale of the pixel in the diagonal direction in the field and the other is that the window size of the focus evaluation operator is fixed, e.g., 3×3, 5×5, etc. Thus, the size of the window for each pixel in the image is the same, and the small window may not cover enough field information while being vulnerable to noise. Large windows can cover more information, but they may result in a smoothing phenomenon, which affects the accuracy of the model. Different pixels around the field have different pixel colors when the size of the window is not the same. Therefore, this paper proposes a modified omnidirectional Laplacian operator with an adaptive window to automatically adjust the size of the window according to the color difference within the window. This also takes into consideration the pixels in the diagonal direction. In addition, very comprehensive verification experiments proved the conclusions.

Three-Dimensional Investigation of Cycling-Induced Microstructural Changes in Lithium-Ion Battery Cathodes

Lithium ion batteries are one of the most promising energy storage systems that have been widely used in portable electronic devices and electric vehicles. Nevertheless, advanced automotive electrification requires greater capacity, longer cycle life and reduced cost. With respect to cycle life, the mechanisms of capacity degradation are not yet to be fully understood. What is known is that solid electrolyte interface (SEI) formation [1] and structural distortion of active materials adversely impacts battery performance.[2,3] Furthermore, next generation negative electrodes are known to suffer huge volume changes during Li+ intercalation/deintercalation upon cycling, which induce local stresses and lead to microstructure degradation of the electrode.[4] While the smaller volume changes of the cathode materials are less well recognized, they may also turn out to be significant for battery performance and life.[5] In the present work, the structural evolution of the LiNixMnyCo1-x-yO2 (NMC) and Li1.2Ni0.13Mn0.54Co0.13O2 (HE-NMC) cathodes in lithium-ion batteries following electrochemical cycling were studied three-dimensionally.[6] The cathodes were successfully reconstructed in 3D using state–of-the-art focus ion beam-scanning electron microscope (FIB-SEM) serial sectioning and imaging techniques. The acquired 2D images were digitally segmenting into three distinct phases before reconstruction. In this study, a segmentation algorithm was developed to apply refined threshold values and gradient analysis to the 2D images, thereby avoiding the necessity of epoxy infiltration to the sample [7] and protect the integrity of the small features of the phases. As shown in Figures 1a-1b, each of the three phases: active material (AM), carbon-doped binder (CB), and pore spaces (which are filled with electrolyte during cycling) were clearly resolved after the image processing step. In order to evaluate the evolution of the relative positions of the important phases within the cathode, the segmented 3D structure was quantified with a “neighbor counting” method, by which the connectivity between CB and the AM was analyzed for cathodes that underwent different number of cycles. This approach provides statistical analysis-based assessment of the degree of connectivity between the surface of active particles and the binder matrix as a function of depth into the cathode (i.e., in the direction perpendicular to the separator and current collector). The probability of carbon-doped binder being adjacent to the active particles (PBA) of cycled NMC cathode is shown in Figure 1c. Each data point represents the probability of CB being in direct contact with the active particle surface of each slice perpendicular to the depth of the 3D structure in units of pixels, each pixel corresponding to the slice thickness (35 nm), as shown in the schematic diagram in Figure 1c. It is shown that the average PBA of the pristine sample decreases from 0.54 to 0.48 after 20 cycles, and further drops to 0.43 after 50 cycles. The value of the average PBA, e.g., 0.54, means that 54% of the AM surface is in contact with the CB phase within the 3D volume. The results therefore indicate that the connectivity between AM and CB decreases with the increased number of cycles, where a cycling-induced detachment between the two phases was observed. Similar detachment phenomenon is also observed in the HE-NMC cathode after 50 cycles, for which a 22.5% loss of AM and CB contact was detected. Since the CB phase contains the carbon black additive for the enhancement of electrical conductivity of the cathode, our results thus suggest that the detachment between the conductive CB phase and the active particles will reduce the efficiency of electron transport from the active particles (where Li+ intercalation/deintercalation happens) to the current collector. The loss of electrical conductivity of the cycled cathode will have an adverse effect on the cyclic performance as a consequence of the detachment between the conductive binder matrix and active particles. [1] P. Verma, P. Maire, P. Novak, Electrochem. Acta. 2010, 55, 6332-6341. [2] F. Lin, I.M. Markus, D. Nordlund, et al. Nat. Commun. 2014, 5, 3529. [3] A. Boulineau, L Simonin, J.F. Colin, et al. Nano Lett. 2013, 13, 3857-3863. [4] D. Larcher, S. Beattie, M. Morcrette, et al. J. Mater. Chem. 2007, 17, 3759–3772. [5] D.S. Eastwood, V. Yufit, J. Gelb, et al. Adv. Energy Mater. 2014, 4, 1300506. [6] H. Liu, J. M. Foster, A. Gully, et al. J. Power Sources. 2016, 306, 300-308. [7] Z. Liu, J. Scott Cronin, Y.K. Chen-Wiegart, et al. J. Power Sources. 2013, 227, 267–274. Figure 1

On-the-fly geometrical calibration fine-tuning of a mobile C-arm CBCT system

Abstract C-arm cone-beam computed tomography (CBCT) offers CT-like 3D imaging capabilities while being appropriate for interventional suites. 3D images are reconstructed based on 2D projections gathered during the rotation of the C-arm around the object under investigation. It is mandatory to provide accurate geometrical projection parameters for each acquired 2D image, otherwise significant CT artifacts may be induced in the reconstructed 3D volume. Usually, a pre-calibration (offline calibration) using an X-ray phantom is preformed under the assumption that the C-arm motion is reproducible. Thereby, stochastic misalignments due to the open design of mobile C-arm CBCT systems are not considered. In this article we introduce a novel online calibration algorithm to compensate stochastic mechanical inaccuracies. The performance of the proposed method is demonstrated on simulated and real data.