High-quality Three-dimensional Images Research Articles

Build and operation of a custom 3D, multicolor, single-molecule localization microscope.

Single-molecule localization microscopy (SMLM) enables imaging scientists to visualize biological structures with unprecedented resolution. Particularly powerful implementations of SMLM are capable of three-dimensional, multicolor and high-throughput imaging and can yield key biological insights. However, widespread access to these technologies is limited, primarily by the cost of commercial options and complexity of de novo development of custom systems. Here we provide a comprehensive guide for interested researchers who wish to establish a high-end, custom-built SMLM setup in their laboratories. We detail the initial configuration and subsequent assembly of the SMLM, including the instructions for the alignment of all the optical pathways, the software and hardware integration, and the operation of the instrument. We describe the validation steps, including the preparation and imaging of test and biological samples with structures of well-defined geometries, and assist the user in troubleshooting and benchmarking the system's performance. Additionally, we provide a walkthrough of the reconstruction of a super-resolved dataset from acquired raw images using the Super-resolution Microscopy Analysis Platform. Depending on the instrument configuration, the cost of the components is in the range US$95,000-180,000, similar to other open-source advanced SMLMs, and substantially lower than the cost of a commercial instrument. A builder with some experience of optical systems is expected to require 4-8 months from the start of the system construction to attain high-quality three-dimensional and multicolor biological images.

Development of a Mind Map-based Predictive Nursing Protocol and its Impact on the Clarity of Images in Patients Undergoing High-concentration Contrast Three-dimensional Computed Tomography Imaging of Liver Blood Vessels.

To explore the development of a mind map-based predictive nursing protocol and assess its impact on the quality of images in patients undergoing high-concentration contrast three-dimensional computed tomography (CT) imaging of liver blood vessels. A total of 600 patients who were admitted to Beijing You an Hospital were chosen for this prospective study and underwent high-concentration contrast three-dimensional CT imaging of liver blood vessels between April 2021 and December 2021. The patients were divided into two groups using the digital table method, with 300 cases. The control group received conventional nursing intervention, while the research group was provided with a mind map-based predictive nursing protocol. We recorded the image quality of three-dimensional CT imaging of liver blood vessels, satisfaction scores regarding nurse examination guidance, and the Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) in both groups. The research group achieved a perfect rate of 100.00% for the high-quality three-dimensional CT imaging of liver blood vessels, which was noticeably higher compared to the rate of the control group of 98.67%. Patients in the research group expressed higher satisfaction levels regarding the guidance provided by nurses, including their attitude, timeliness, accuracy, and overall satisfaction, compared to the control group. Initially, the two groups had no notable differences in the SAS and SDS scores. However, after the intervention, both groups experienced a significant decrease in SAS and SDS scores, with the research group showing an even more substantial decline. Through the creation of a mind map-based predictive nursing protocol and its implementation on patients undergoing high-concentration contrast three-dimensional CT imaging of liver blood vessels, it is possible to significantly enhance the quality of CT scans, alleviate feelings of anxiety and depression, increase patient satisfaction with examination guidance by nurses, and effectively decrease the occurrences of contrast agent leakage and allergic reactions to iodine.

Artifact noise suppression of particle-field computed tomography based on lightweight residual and enhanced convergence neural network

<sec>The realization of fast and high-quality three-dimensional particle-field image characterization is always highly desired in the areas, such as experimental fluid mechanics and biomedicine, for the micro-particle distribution status in a flow-field can characterize the field properties well. In the particle-field image reconstruction and characterization, a wildly used approach at present is the computed tomography. The great advantage of the computed tomography for particle-field image reconstruction lies in the fact that the full particle spatial distribution can be obtained and presented due to multi-angle sampling.</sec><sec>Recently, with the development and application of deep learning technique in computed tomography, the image quality has been greatly improved by the powerful learning ability of a deep learning network. In addition, the deep learning application also makes it possible to speed up the computed tomographic imaging process from sparse-sampling due to the ability of the network to strongly extract image feature. However, sparse-sampling will lead to insufficient acquirement of the object information during sampling for the computed tomography. Therefore, a sort of artifact noise will emerge and be accompanied with the reconstructed images, and thus severely affecting the image quality. As there is no universal network approach that can be applied to all types of objects in the suppression of artifact noise, it is still a challenge in removing the sparse-sampling-induced artifact noise in the computed tomography now.</sec><sec>Therefore, we propose and develop a specific lightweight residual and enhanced convergence neural network (LREC-net) approach for suppressing the artifact noise in the particle-field computed tomography. In this method, the network input dataset is also optimized in signal-to-noise ratio (SNR) in order to reduce the input noise and ensure the effective particle image feature extraction of the network in the imaging process.</sec><sec>In the design of LREC-net architecture, a five-layer lightweight and dual-residual down-sampling is constructed on the basis of typical U-net and Resnet50, making the LREC-net more suitable for the particle-field image reconstruction. Moreover, a fast feature convergence module for rapid particle-field feature acquirement is added to up-sampling process of the network to further promote the network processing efficiency. Apart from the design of LREC-net network itself, the optimization of network input dataset in SNR of images is achieved by finding a fit image reconstruction algorithm that can produce higher-SNR particle images in the computed tomography. This achievement reduces the input noise as much as possible and ensures effective particle-field feature extraction by the network.</sec><sec>The simulation analysis and experimental test demonstrate the effectiveness of the proposed LREC-net method, which involves the evaluations of SNR changes of the input-output images through the network, the proportion of residual artifact noise as ghost-particles (GPP) in the reconstructed images, and the valid-particle loss proportion (PLP). In contrast to the performances of U-net and Resnet50 under the same imaging conditions, all the data in SNR, GPP and PLP show the great improvement of the image quality due to the application of LREC-net method. Meanwhile, the designed LREC-net method also enhances the network running efficiency to a large extent due to the remarkable reduction of training time. Therefore, this work provides a new and effective approach for developing sparse-sampling-based fast and high-quality particle-field computed tomography.</sec>

Non-iterative reconstruction of interferenceless coded aperture correlation holography enabled high quality three-dimensional imaging

Interferenceless coded aperture correlation holography (I-COACH) is a rapidly developing indirect three-dimensional (3D) imaging technique. However the ideal point spread function of imaging system is not obtained in most cases, the object hologram is usually correlated with the system response of a limited-size pinhole during the reconstruction process. As a result, the quality of reconstructed images is contaminated by noise. In this paper, the system response of a limited-size pinhole is regarded as the convolution of the pinhole (as an object) intensity and the ideal point spread function of I-COACH, a mathematical analysis of the background noise is conducted and a general high-quality reconstruction mathematic model of I-COACH system is derived when a limited-size pinhole is used to record point spread hologram, in which the reconstructions and noise is separated. Then a non-iterative cross-correlation reconstruction algorithm has been developed here to achieve high-quality 3D reconstruction. The proposed method has been implemented on a regular I-COACH system in both transmission as well as reflection illumination modes to demonstrate the imaging ability of our method. The experimental results show that high quality reconstructions can be achieved by our proposal with a single-exposure object hologram.

RECOMMENDED TECHNOLOGY FOR TOMOGRAPHIC INSPECTION IN THE LABORATORY

Paper presents a detailed technology of tomographic inspection in the laboratory. Tomographic inspection is a powerful tool for obtaining high-quality three-dimensional images of the internal structure of objects. The advantages of the method are considered, which include the non-destructive nature of the control, high resolution, measurement accuracy, and the ability to detect defects invisible to the human eye. The main attention is paid to the description of the main stages of tomographic inspection, which are recommended to achieve maximum efficiency. Possible errors that may occur during tomographic inspection in the laboratory are highlighted, and ways to eliminate them are provided. The optimal parameters that should be taken into account during tomographic inspection are considered, including the correct choice of tomograph parameters, setting of lighting conditions, and correct positioning of the object under inspection. The process of processing the obtained inspection data is described in detail, which helps to ensure high-quality information analysis. The authors also analyze the feasibility of the control stages, comparing the results obtained at different stages and considering their impact on the final result. This work is a valuable source of information for specialists involved in tomographic control in the laboratory. The described technology and recommendations will help to improve the quality and efficiency of tomographic inspection in their work. The research results presented in this article can improve the inspection process, reduce errors, and detect even the smallest defects in samples. In general, this paper offers a recommended technology for tomographic inspection in the laboratory, taking into account key aspects such as the advantages of the method, inspection stages, error elimination, optimal parameters, object positioning, and data processing. Taking these recommendations into account, specialists will be able to provide more accurate, efficient and reliable tomographic control in their work. Thus, this article will be a valuable addition to the scientific literature in the field of non-destructive testing, will help improve the quality of tomographic testing and promote the development of this important method in the laboratory.

Terahertz 3D point cloud imaging for complex targets.

The reconstruction of complex targets using terahertz technology is often hindered by diffraction and interference of electromagnetic waves, leading to the loss of fine target details. In this research article, we have introduced a terahertz synthetic aperture radar (SAR) imaging method that integrates an iterative closest point (ICP) algorithm, referred to as SAR-ICP, to achieve accurate reconstruction of intricate target structures. To accomplish this, multiple sets of point cloud data are acquired by varying the illumination viewpoint. The ICP algorithm is then employed to align and fuse these datasets, resulting in the generation of high-quality three-dimensional (3D) images. The experimental results validate the effectiveness of the proposed SAR-ICP method. The information entropy of the reconstructed 3D image using the SAR-ICP is approximately 0.05 times that of the conventional SAR method, indicating a superior image quality. In the future, we anticipate the widespread application of this method in areas such as security inspection, non-destructive testing, and other complex scenarios.

Phase compensation algorithm based on image segmentation in dual-wavelength holographic microscopy.

In order to solve the problem of phase compensation errors in the traditional 2π phase compensation method caused by a rough surface and complex structure of objects in dual-wavelength digital holographic microscopy, a phase compensation algorithm based on image segmentation was proposed. First, the phase less than zero in the phase obtained by an equivalent wavelength is compensated for by adding 2π initially. Then the phase after the initial compensation is binarized, and the small connected areas in the binarized graph are removed, so as to obtain a new binarized graph. Finally, according to the two binarized graphs, the phase of the object after the initial 2π phase compensation is recompensated for in different regions, so as to obtain the continuous phase distribution of the object. Based on the dual-wavelength digital holographic microscopy experimental system with an adjustable equivalent wavelength, the proposed algorithm is used to perform three-dimensional imaging of the channel of the microfluidic chip. The experimental results show that the proposed method can effectively obtain the continuous real phase of the object when the structure of the object is known, so as to obtain a more accurate and reliable three-dimensional topography of the object. The above results provide a new idea for the high-quality three-dimensional imaging of the microfluidic system.

3D ultrasonic imaging of surface-breaking cracks using a linear array

Ultrasonic linear arrays have great potential to generate high-quality three-dimensional (3D) images by scanning the array. However, the generated images suffer from low resolution in the elevation plane, limiting the image quality for a reliable 3D Non-Destructive Testing (NDT) inspection. Although several ultrasonic imaging methods have been implemented to inspect different types of defects, there has been limited research to characterise surface-breaking cracks (SBCs) in 3D quantitatively. To improve the characterisation of surface-breaking cracks (SBCs), a 3D hybrid imaging method is proposed by combining the Half Skip Total Focusing Method (HSTFM) and the Synthetic Aperture Focusing Technique (SAFT) using a linear array. This paper proposed the implementation of an array with a reduced element length for full matrix capture (FMC) data acquisition. In conjunction with the hybrid imaging method, a reduced element array enables the utilisation of the information from a broad ultrasonic beam in the elevation direction to achieve improved image resolution. The imaging capability is assessed via a point spread function (PSF) as well as numerical simulations. From the PSF measurements, the image resolution is shown to improve with the smaller element length of the array, which is attributable to the combination of wide beamwidth and hybrid imaging method. Thereafter, experimental validation was performed with arrays of different elevation lengths, where an excellent match with the numerical results was observed. Furthermore, the crack sizing was performed using a 6-dB-drop rule, which assisted in accurately predicting the shape and size of the SBCs and is shown to measure the depth of SBCs with greater confidence. It is shown that a reduced array elevation with the hybrid imaging method and sizing method yields improved image resolution contrary to conventional linear arrays. This approach can offer a significant improvement in manifesting complete comprehension of the spatial defect relationship, enabling NDT engineers to analyse the inspection results quantitatively in 3D for progressive reliability.

A MPS-based novel method of reconstructing 3D reservoir models from 2D images using seismic constraints

While multi-point geostatistics (MPS) be used to model complex underground reservoirs, this method relies heavily on high-quality three-dimensional (3D) training images that may be difficult to acquire in real field studies. Furthermore, the original MPS technique does not include seismic data, which may noticeably improve underground reservoir predictions. Our proposed model, Se3DRCS, is a novel method to reconstruct 3D geological models from two-dimensional (2D) images and seismic information. In Se3DRCS, we are able to perform MPS-based modeling using 2D training images, rather than 3D images. These 2D images are produced by analyzing well profiles and sedimentary facies planes. After using the Se3DRCS method to obtain an initial geological model, we then generate elastic parameters and synthetic seismograms via direct sampling and convolution with the extracted seismic wavelets, respectively. By comparing the observed and synthetic seismic records, we refine the simulation results using the adaptive sampling method. The initial geological model is then updated with conditional data after each subsequent simulation iteration. When the error between the observed and synthetic seismograms falls below a reasonable threshold value, we have arrived at the best-fitting geological model. Our results indicate that the Se3DRCS method is capable of predicting the data elicited from wells with an accuracy of up to 82.88%, providing constraints on the distribution of branch channels in a delta reservoir, and reproducing local complex non-stationary geological features.

A two-stage deep generative adversarial quality enhancement network for real-world 3D CT images

High-quality (HQ) three-dimensional (3D) images are the premise of analyzing the properties of porous media such as rocks. X-ray computed tomography (CT) is one of the most widely used imaging tools to capture the 3D images of rock samples. Nevertheless, the quality (e.g., resolution, sharpness, and the signal-to-noise ratio) of the collected rock CT images may not meet the needs of practical applications in some cases due to the limitations of imaging systems, leading to inaccurate results of property analysis. In this paper, aiming at improving the quality of rock CT images as well as the accuracy of property analysis, we develop a two-stage deep generative adversarial quality enhancement network for real-world 3D CT images, namely the CTQENet. More specifically, the proposed CTQENet consists of a two-dimensional (2D) reconstruction module (2DRM) and a 3D fusion module (3DFM), which enhance the quality of 3D CT images from the perspective of 2D slices and 3D volumes, respectively. In order to remove artifacts and enhance the resolution of real-world CT images, the 2DRM takes the cycle-consistent generative adversarial network as the backbone to learn the mapping from low-quality (LQ) 2D slices to HQ ones without one-to-one paired training data. Then, the 3D CT volumes stacked by the reconstructed HQ slices along the x/y/z-axis are adaptively fused in the generative adversarial network-based 3DFM, to achieve more reliable 3D morphological structures. Qualitative and quantitative comparisons show the effectiveness of the proposed CTQENet for real-world 3D CT images of rock samples. In particular, the reconstructed HQ 3D CT images by CTQENet show similar morphological characteristics and statistical properties with HQ targets. This study makes it possible to obtain higher quality 3D CT images that partly exceed the limitations of CT imaging systems for better visual experience and more accurate property analysis.

Automatic segmentation tool for 3D digital rocks by deep learning

Obtaining an accurate segmentation of images obtained by computed microtomography (micro-CT) techniques is a non-trivial process due to the wide range of noise types and artifacts present in these images. Current methodologies are often time-consuming, sensitive to noise and artifacts, and require skilled people to give accurate results. Motivated by the rapid advancement of deep learning-based segmentation techniques in recent years, we have developed a tool that aims to fully automate the segmentation process in one step, without the need for any extra image processing steps such as noise filtering or artifact removal. To get a general model, we train our network using a dataset made of high-quality three-dimensional micro-CT images from different scanners, rock types, and resolutions. In addition, we use a domain-specific augmented training pipeline with various types of noise, synthetic artifacts, and image transformation/distortion. For validation, we use a synthetic dataset to measure accuracy and analyze noise/artifact sensitivity. The results show a robust and accurate segmentation performance for the most common types of noises present in real micro-CT images. We also compared the segmentation of our method and five expert users, using commercial and open software packages on real rock images. We found that most of the current tools fail to reduce the impact of local and global noises and artifacts. We quantified the variation on human-assisted segmentation results in terms of physical properties and observed a large variation. In comparison, the new method is more robust to local noises and artifacts, outperforming the human segmentation and giving consistent results. Finally, we compared the porosity of our model segmented images with experimental porosity measured in the laboratory for ten different untrained samples, finding very encouraging results.

Fast and High-Quality 3-D Terahertz Super-Resolution Imaging Using Lightweight SR-CNN

High-quality three-dimensional (3-D) radar imaging is one of the challenging problems in radar imaging enhancement. The existing sparsity regularizations are limited to the heavy computational burden and time-consuming iteration operation. Compared with the conventional sparsity regularizations, the super-resolution (SR) imaging methods based on convolution neural network (CNN) can promote imaging time and achieve more accuracy. However, they are confined to 2-D space and model training under small dataset is not competently considered. To solve these problem, a fast and high-quality 3-D terahertz radar imaging method based on lightweight super-resolution CNN (SR-CNN) is proposed in this paper. First, an original 3-D radar echo model is presented and the expected SR model is derived by the given imaging geometry. Second, the SR imaging method based on lightweight SR-CNN is proposed to improve the image quality and speed up the imaging time. Furthermore, the resolution characteristics among spectrum estimation, sparsity regularization and SR-CNN are analyzed by the point spread function (PSF). Finally, electromagnetic computation simulations are carried out to validate the effectiveness of the proposed method in terms of image quality. The robustness against noise and the stability under small are demonstrate by ablation experiments.

The importance of preoperative planning to perform safely temporal lobe surgery

Neurosurgeons should know the anatomy required for safe temporal lobe surgery approaches. The present study aimed to determine the angles and distances necessary to reach the temporal stem and temporal horn in surgical approaches for safe temporal lobe surgery by using a 3.0 T magnetic resonance imaging technique in post-mortem human brain hemispheres fixed by the Klingler method. In our study, 10 post-mortem human brain hemisphere specimens were fixed according to the Klingler method. Magnetic resonance images were obtained using a 3.0 T magnetic resonance imaging scanner after fixation. Surgical measurements were conducted for the temporal stem and temporal horn by magnetic resonance imaging, and dissection was then performed under a surgical microscope for the temporal stem. Each stage of dissection was achieved in high-quality three-dimensional images. The angles and distances to reach the temporal stem and temporal horn were measured in transcortical T1, trans-sulcal T1–2, transcortical T2, trans-sulcal T2–3, transcortical T3, and subtemporal trans–collateral sulcus approaches. The safe maximum posterior entry point for anterior temporal lobectomy was measured as 47.16 ± 5.00 mm. Major white-matter fibers in this region and their relations with each other are shown. The distances to the temporal stem and temporal horn, which are important in temporal lobe surgical interventions, were measured radiologically, and safe borders were determined. Surgical strategy and preoperative planning should consider the relationship of the lesion and white-matter pathways.

Diagnostic Role of Magnetic Resonance Imaging in Low Back Pain Caused by Vertebral Endplate Degeneration.

Low back pain (LBP) is a common health issue worldwide with a huge economic burden on healthcare systems. In the United States alone, the cost is estimated to be $100 billion each year. Intervertebral disc degeneration is considered one of the primary causes of LBP. Moreover, the critical role of the vertebral endplates in disc degeneration and LBP is becoming apparent. Endplate abnormalities are closely correlated with disc degeneration and pain in the lumbar spine. Imaging modalities such as plain film radiography, computed tomography, and fluoroscopy are helpful but not very effective in detecting the causes behind LBP. Magnetic resonance imaging (MRI) can be used to acquire high-quality three-dimensional images of the lumbar spine without using ionizing radiation. Therefore, it is increasingly being used to diagnose spinal disorders. However, according to the American College of Radiology, current referral and justification guidelines for MRI are not sufficiently clear to guide clinical practice. This review aimed to evaluate the role of MRI in diagnosing LBP by considering the correlative contributions of vertebral endplates. The findings of the review indicate that MRI allows for fine evaluations of endplate morphology, endplate defects, diffusion and perfusion properties of the endplate, and Modic changes. Changes in these characteristics of the endplate were found to be closely correlated with disc degeneration and LBP. The collective evidence from the literature suggests that MRI may be the imaging modality of choice for patients suffering from LBP. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 3.

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.

Investigation of coal elastic properties based on digital core technology and finite element method

Rock elastic properties play an important role in the geological characteristics of reservoirs. The analysis of these properties is normally based physical experiments on rocks. However, such conventional physical experiments cannot meet actual requirements when the rock is fragile or has complex composition. With the development of computer technology and the application of micro-computed tomography scanning technology, digital rock physics technologies came into existence. In this work, micro-computed tomography was applied to obtain high-quality three-dimensional images of coal samples. Next, the image multi-threshold segmentation method was used to divide the grayscale image into three reasonable components, including mineral, organic matrix, and pores. Digital rock models with different gas saturations were established using mathematical morphology based methods. Five volume samples were selected from the original large digital rock model under different conditions of porosity, mineral, and gas saturation. Based on these three-dimensional digital cores and the finite element method, the effective elastic moduli of coal rock mass were simulated and the compressional wave velocity and shear wave velocity were computed. Results show that, in the absence of filled minerals, both bulk and shear moduli decrease with rising porosity; compressional and shear wave velocities decline, and the ratio of compressional wave velocity to shear wave velocity increases. However, a more realistic study considering filled minerals demonstrates decreasing shear wave velocity and counterintuitively rising compressional wave velocity when the porosity increases. Gas saturation only affects the compressional wave velocity. The obtained results improve our understanding of rock elastic behaviors in the coalbed. Cited as : Andhumoudine, A. B., Nie, X., Zhou, Q., Yu, J., Kane, O. I., Jin, L., Djaroun, R. R. Investigation of coal elastic properties based on digital core technology and finite element method. Advances in Geo-Energy Research, 2021, 5(1): 53-63, doi: 10.46690/ager.2021.01.06

Innovative Three-Dimensional Microscopic Analysis of Uremic Growth Plate Discloses Alterations in the Process of Chondrocyte Hypertrophy: Effects of Growth Hormone Treatment.

Chronic kidney disease (CKD) alters the morphology and function of the growth plate (GP) of long bones by disturbing chondrocyte maturation. GP chondrocytes were analyzed in growth-retarded young rats with CKD induced by adenine intake (AD), control rats fed ad libitum (C) or pair-fed with the AD group (PF), and CKD rats treated with growth hormone (ADGH). In order to study the alterations in the process of GP maturation, we applied a procedure recently described by our group to obtain high-quality three-dimensional images of whole chondrocytes that can be used to analyze quantitative parameters like cytoplasm density, cell volume, and shape. The final chondrocyte volume was found to be decreased in AD rats, but GH treatment was able to normalize it. The pattern of variation in the cell cytoplasm density suggests that uremia could be causing a delay to the beginning of the chondrocyte hypertrophy process. Growth hormone treatment appears to be able to compensate for this disturbance by triggering an early chondrocyte enlargement that may be mediated by Nkcc1 action, an important membrane cotransporter in the GP chondrocyte enlargement.

Deep Learning-based Inaccuracy Compensation in Reconstruction of High Resolution XCT Data

While X-ray computed tomography (XCT) is pushed further into the micro- and nanoscale, the limitations of various tool components and object motion become more apparent. For high-resolution XCT, it is necessary but practically difficult to align these tool components with sub-micron precision. The aim is to develop a novel reconstruction methodology that considers unavoidable misalignment and object motion during the data acquisition in order to obtain high-quality three-dimensional images and that is applicable for data recovery from incomplete datasets. A reconstruction software empowered by sophisticated correction modules that autonomously estimates and compensates artefacts using gradient descent and deep learning algorithms has been developed and applied. For motion estimation, a novel computer vision methodology coupled with a deep convolutional neural network approach provides estimates for the object motion by tracking features throughout the adjacent projections. The model is trained using the forward projections of simulated phantoms that consist of several simple geometrical features such as sphere, triangle and rectangular. The feature maps extracted by a neural network are used to detect and to classify features done by a support vector machine. For missing data recovery, a novel deep convolutional neural network is used to infer high-quality reconstruction data from incomplete sets of projections. The forward and back projections of simulated geometric shapes from a range of angular ranges are used to train the model. The model is able to learn the angular dependency based on a limited angle coverage and to propose a new set of projections to suppress artefacts. High-quality three-dimensional images demonstrate that it is possible to effectively suppress artefacts caused by thermomechanical instability of tool components and objects resulting in motion, by center of rotation misalignment and by inaccuracy in the detector position without additional computational efforts. Data recovery from incomplete sets of projections result in directly corrected projections instead of suppressing artefacts in the final reconstructed images. The proposed methodology has been proven and is demonstrated for a ball bearing sample. The reconstruction results are compared to prior corrections and benchmarked with a commercially available reconstruction software. Compared to conventional approaches in XCT imaging and data analysis, the proposed methodology for the generation of high-quality three-dimensional X-ray images is fully autonomous. The methodology presented here has been proven for high-resolution micro-XCT and nano-XCT, however, is applicable for all length scales.

Detection of Vertical Root Fractures Using Three Different Imaging Modalities: An In Vitro Study

The diagnosis of nondisplaced longitudinal fractures [vertical root fractures (VRFs)] is challenging in clinical practice. Radiographic techniques showed a difficulty in detection of VRFs. Cone-beam computed tomography (CBCT) is a new diagnostic imaging modality that provides high-quality three-dimensional (3D) images for dental diagnosis. The aim of this in vitro study is to compare accuracy of three different imaging modalities: conventional periapical radiographs, digital radiographs, and CBCT in detecting VRFs in teeth that are endodontically as well as nonendodontically treated. An in vitro model consisting of 60 recently extracted human mandibular lower premolars were used. Root canal treatment was carried out for 30 teeth. Root fractures were created in 30 teeth (15 root canal treated and 15 non-treated) by mechanical force. Other 30 teeth remain intact. The teeth were mounted and images were taken with a periapical, digital, and CBCT X-ray unit. Three endodontists separately evaluated the images. Interobserver κ values showed a very good interobserver agreement (0.98 for CBCT, 0.88 for digital, and 0.93 for conventional periapical X-rays). There was an overall statistically significant difference (p = 0.00) in detecting of root fracture among the three imaging modalities and the highest accuracy with CBCT images. In in vitro model, CBCT scan appears to give the highest accuracy in detecting VRFs when compared with the periapical systems in both endodontically and nonendodontically treated teeth. The CBCT scan shows higher sensitivity in detection of VRFs in comparison with periapical images.

Radar Interferometric Survey of the Surface of an Unprepared Landing Area from on Board a Helicopter

Abstract—The paper considers the possibilities of radar interferometric survey of the surface of a landing site from on board a helicopter. It is shown that if the phase-difference dependence obtained in this way is superimposed on the radio-brightness pattern of the reflection according to resolution elements after irradiation of the studied surface under study, then it is possible to reconstruct a high-quality three-dimensional image of the landing area and to determine with high accuracy its possible slope, the characteristics of its relief, and the contours of foreign objects on it.