Thin-section Images Research Articles

The role of nanomaterials in enhancing adhesion properties between bitumen and aggregate particles

During production and construction stages of asphalt mixtures, rheology of bitumen must be set at certain level. In addition, the adhesion strength between bitumen and the aggregate particles should be high enough so that during the service life of the pavement, asphalt mixture can resist various distresses. There are different methods of controlling rheological properties of bitumen binders and increasing adhesion properties of asphalt mixtures. One of the most effective methods of modifying bitumen binders of asphalt mixtures is using nanomaterials. In this research, two bitumen types of 60/70 and 85/100 penetration grades, three aggregate types (limestone, siliceous, and granite) and four nanomaterials types (nanohydrated lime NHL, nanosilica NSiO2, nanolime NCaCO3, and nanoclay bentonite NClay) were used at 2, 4, and 6% of the weight of bitumen. X-ray diffraction and X-ray fluorescence tests were performed and, microscopic images of thin-sections of aggregates and asphalt mixtures were taken with the aim of evaluating petrographic properties of aggregates and bonding characteristics of the bitumen binders with aggregate particles. Physical and rheological properties of the nano-modified bitumen binders were evaluated, performing dynamic shear rheometer and rotational viscometer tests. Boiling water and pull-off tests were also carried out in order to investigate the coating ability and the adhesion strength that is performed at the aggregate surface in the interface with the bitumen binders. Digital images were analyzed using Digimizer Software. The results indicated that the addition of nanomaterials at certain optimal percentage affected viscosity and improved rheological behavior of the bitumen binders at different temperatures, and increased adhesion properties of the bitumen coating the aggregate particles. Comparing the effects of different aggregates, it resulted that the binder coating the limestone aggregates performed better than the siliceous and the granite aggregates. The adhesion and the rheological properties of the 60/70 pen bitumen that was modified with nanomaterials were better than those of the 85/100 pen bitumen. Nano-modified binders at 4% NHL, or 2% NSiO2, or 4% NCaCO3, or 4% NClay exhibited the best behavior. Among these, the nano-modified binders containing 4% NHL resulted in better performance. Finally, the correlation between the adhesion and rheological behavior was suitable for nano-modified binders in order to improve the asphalt mixes' performance in operation and construction stages.

Pore-scale numerical investigation on spontaneous imbibition in natural fracture with heterogeneous wettability using the volume of fluid method

Understanding the spontaneous imbibition in the natural fracture with heterogeneous wettability is crucial for predicting and mitigating the impacts of unstable displacement on unconventional recovery. In this paper, the fracture structured mesh model is reconstructed based on the micro-computed tomography (micro-CT) image of naturally fractured tight sandstone. The mineralogy map-based modeling method for heterogeneous-wetting fracture is developed by combining the thin section images, X-ray diffraction (XRD) analysis, and multiple point statistics method. The simulation of the single-phase flow is performed to test the mesh independence. The effects of gravity and wettability on spontaneous imbibition in natural fracture and corresponding imbibition front dynamics are analyzed and discussed using the volume of fluid (VOF) method. The results show that (1) The structured mesh reconstruction method proposed in this paper can more effectively preserve the fracture structure compared to the unstructured mesh reconstruction method. (2) Gravity has a negligible impact on the pore-scale spontaneous imbibition in natural fracture. Under homogeneous-wetting conditions, spontaneous imbibition in natural fracture consistently exhibits stable displacement without significant residual gas formation. However, under the heterogeneous-wetting condition, the spontaneous imbibition displays typical capillary fingering, resulting in approximately 24.04% of the gas being trapped after spontaneous imbibition. The residual gas trapping mechanisms mainly include adhered, isolated, and connected gas. (3) Under both homogeneous- and heterogeneous-wetting conditions, the imbibing water saturation and the length of the imbibition front are proportional to the power of imbibition time during spontaneous imbibition in the natural fracture.

Identifying the high potential zones for hydraulic fracture propagation / Eastern Baghdad field

For a reservoir with high storage capacity and low ability to produce, the serious problem is the sharp reduction in the recorded well productivity within a short period. One solution to this problem is to create hydraulic fractures that increase formation permeability and keep its production at high rates for a sufficient time. The field under study is the East Baghdad oil field of three formations: Saadi, Tanuma, and Khasib. Knowing the geomechanical behavior of these reservoirs has a critical effect on the success of hydraulic fracturing operations. In this study, rock stress magnitude and direction, rock elasticity, rock strength to fracturing initiation, and all these parameters in addition to petrophysical properties will be used to identify whether the hydraulic fracturing operation could be successful or not. An integrated modeling of the studied reservoir is an essential step including 1-D geomechanical evaluation of many formations in order to choose the perfect layer to create hydraulic fracture. Then, a 3-D distribution of geomechanical properties and petrophysical properties was presented to make a perfect selection of these properties. The geomechanical evaluation of the reservoirs under study is supported by experimental evaluation of core samples including Energy Dispersive X-ray spectroscopy (EDS), Scanning Electron Microscopic (SEM) image, and thin section (TS) image. The results show that a reduction in the calculated geomechanical properties in terms of Poisson ratio young modulus and compressive strength are favorable for candidate layer selection. Among the studies of rock mechanical properties, it is also noticed that unconfined compressive strength is a crucial parameter for best layer selection. The suitable depths for fracturing jobs are given in detail in this study using brief data collected from four wells.

IMAGE-BASED YOLOV4 ARCHITECTURE FOR DETECTING MINERAL IN SEDIMENTARY ROCKS THIN SECTION

Thin section analysis of sedimentary rocks is the basis for identifying minerals and textures. In general, quantitative analysis of thin sections of rock often requires many hours of work when done manually. In today's era, mineralogical interpretation and percentage calculations must be carried out automatically using more practical applications. The research method begins with the identification of 44 thin section samples in parallel plane polarized (PPL) and crossed polarized (XPL) conditions with thin section analysis then mineralogy detection is carried out using a computational approach, namely the use of image-based Deep Learning YoloV4 architecture with 2D RGB image objects from the thin section of sedimentary rock. The results of this study show the best values of Average Precision in Quartz, Feldspar, and lithic are 39.21% in the XPL model, 26.53% in the XPL model, and 15.75% in combined mode, according to the training and testing of YoloV4 Models for the identification of rock minerals in thin sections. Based on the complexity of the mineral types, the granularity of the detection, and the specific geological objectives, establishing a meaningful benchmark or baseline for comparison is always challenging. Additionally, consider discussing the trade-offs between precision and recall, as a higher precision may be more critical in some geological applications. It is expected that the application of this research can produce practical, fast and accurate interpretation of the determination of minerals in sedimentary rocks from all thin-section images of rocks and thus provide a complete understanding of geological views automatically.

Intelligent Classification and Segmentation of Sandstone Thin Section Image Using a Semi-Supervised Framework and GL-SLIC

This study presents the development and validation of a robust semi-supervised learning framework specifically designed for the automated segmentation and classification of sandstone thin section images from the Yanchang Formation in the Ordos Basin. Traditional geological image analysis methods encounter significant challenges due to the labor-intensive and error-prone nature of manual labeling, compounded by the diversity and complexity of rock thin sections. Our approach addresses these challenges by integrating the GL-SLIC algorithm, which combines Gabor filters and Local Binary Patterns for effective superpixel segmentation, laying the groundwork for advanced component identification. The primary innovation of this research is the semi-supervised learning model that utilizes a limited set of manually labeled samples to generate high-confidence pseudo labels, thereby significantly expanding the training dataset. This methodology effectively tackles the critical challenge of insufficient labeled data in geological image analysis, enhancing the model’s generalization capability from minimal initial input. Our framework improves segmentation accuracy by closely aligning superpixels with the intricate boundaries of mineral grains and pores. Additionally, it achieves substantial improvements in classification accuracy across various rock types, reaching up to 96.3% in testing scenarios. This semi-supervised approach represents a significant advancement in computational geology, providing a scalable and efficient solution for detailed petrographic analysis. It not only enhances the accuracy and efficiency of geological interpretations but also supports broader hydrocarbon exploration efforts.

Unsupervised segmentation for sandstone thin section image analysis

Unsupervised segmentation for sandstone thin section image analysis

The influence of vesicularity on grain morphology in basaltic pyroclasts from Mauna Loa and Kīlauea volcanoes

Vesicularity of individual pyroclasts from airfall tephra deposits is an important parameter that is commonly measured at basaltic volcanoes. Conventional methods used to determine pyroclast vesicularity on a large number of clasts has the potential to be time consuming, particularly when rapid analysis is required. Here we propose dynamic image analysis on two-dimensional (2D) projection shapes of crushed pyroclasts from tephra deposits as a new method to estimate vesicularity. This method relies on the influence of vesicles and uses grain morphology as a proxy for vesicle size and abundance. Pyroclasts from a variety of basaltic tephra deposits from the volcanoes of Mauna Loa and Kīlauea were analyzed. Vesicularities between 52–98% were measured via nitrogen-gas pycnometry. The same pyroclasts were then crushed and sieved, and their grain shapes measured using dynamic image analysis on a CAMSIZER®. This yields values for the mean sphericity, elongation, compactness, and Krumbein roundness of the grains. Our data show that grains become increasingly irregular with increasing vesicularity, with the degree of correlation between shape parameters and vesicularity depending on the size of measured grains. Shape irregularities in small grains (60–250 µm) are mostly area-based, with elongation being the best vesicularity indicator, whereas shape irregularities in large grains (250–700 µm) are mostly perimeter-based, with Krumbein roundness as the best vesicularity indicator. Using mean shape parameter values with all grain sizes included, grain elongation is the most well-correlated shape parameter with vesicularity, with the best fitted model explaining 76% of variation in the observations. Microscope images of thin sections of intact pyroclasts, as well as from crushed pyroclasts, were analyzed using CSDCorrections 1.6 software in ImageJ to find local vesicularity, vesicle size, grain size, grain elongation, and vesicle spatial distribution by stereological conversion. Observed correlation between grain shape and vesicularity can be explained by the local effect of vesicles on the shape of the solid structure in between those vesicles. Grain shape depends not only on vesicularity, but also on vesicle to grain size ratio and the spatial distribution of vesicles. The influence of vesicles on grain shape is best captured by grains with the size of the solid structure in between vesicles, which generally increases with decreasing vesicularity. Dynamic image analysis is a useful tool to quickly gauge vesicularity, which could be used in near-real-time during an eruption response. However, this method is best suited for highly vesicular (> 80%) basaltic pyroclasts from tephra deposits with few microlites and phenocrysts. Further research on crushing techniques, optimum grain size for shape measurements, and Krumbein roundness measurements for the grain size range of 250–700 µm might enable application of this method to lower vesicularity pyroclasts.

Object detection algorithms to identify skeletal components in carbonate cores

Identification of constituent grains in carbonate rocks requires specialist experience. A carbonate sedimentologist must be able to distinguish between skeletal grains that change through geological ages, preserved in differing alteration stages, and cut in random orientations across core sections. Recent studies have demonstrated the effectiveness of machine learning in classifying lithofacies from thin section, core, and seismic images, with faster analysis times and reduction of natural biases. In this study, we explore the application and limitations of convolutional neural network (CNN) based object detection frameworks to identify and quantify multiple types of carbonate grains within close-up core images of carbonate lithologies. We compiled nearly 400 images of high-resolution core images from three ODP and IODP expeditions. Over 9000 individual carbonate components of 11 different classes were manually labelled from this dataset. Using pre-trained weights, a transfer learning approach was applied to evaluate one-stage (YOLO v5) and two-stage (Faster R–CNN) detectors under different feature extractors (CSP-Darknet53 and ResNet50-FPN, respectively). Despite the current popularity of one-stage detectors, our results show Faster R–CNN with ResNet50-FPN backbone provides the most robust performance, achieving 0.73 mean average precision (mAP). Furthermore, we extend the approach by deploying the trained model to two ODP sites from Leg 194 that were not part of the training set (ODP Sites 1196 and 1199), providing a performance comparison with benchmark human interpretation.

Research on the generation and annotation method of thin section images of tight oil reservoir based on deep learning

The cast thin sections of tight oil reservoirs contain important parameters such as rock mineral composition and content, porosity, permeability and stratigraphic characteristics, which are of great significance for reservoir evaluation. The use of deep learning technology for intelligent identification of thin section images is a development trend of mineral identification. However, the difficulty of making cast thin sections, the complexity of the making process and the high cost of thin section annotation have led to a lack of cast thin section images, which cannot meet the training requirements of deep learning image recognition models. In order to increase the sample size and improve the training effect of deep learning model, we proposed a generation and annotation method of thin section images of tight oil reservoir based on deep learning, by taking Fuyu reservoir in Sanzhao Sag as the target area. Firstly, the Augmentor strategy space was used to preliminarily augment the original images while preserving the original image features to meet the requirements of the model. Secondly, the category attention mechanism was added to the original StyleGAN network to avoid the influence of the uneven number of components in thin sections on the quality of the generated images. Then, the SALM annotation module was designed to achieve semi-automatic annotation of the generated images. Finally, experiments on image sharpness, distortion, standard accuracy and annotation efficiency were designed to verify the advantages of the method in image quality and annotation efficiency.

Zircon compositional systematics from Devonian oxidized I-type granitoids: examination of porphyry Cu fertility indices in the New Brunswick Appalachians, Canada

Zircon is a common, widely distributed accessory mineral in most igneous rocks and its refractory nature records magmatic evolution in terms of oxygen and U-Th-Pb isotopes, and trace-element contents all of which reflect the intrinsic physio-chemical evolution of the magmatic systems in which it crystallized. Zircon compositions can be used as an indicator of relative fertility of hypabyssal intrusions in terms Cu ± Mo ± Au porphyry mineralization. To further characterize syn- to post-collisional adakitic Devonian oxidized I-type granitoids in the New Brunswick (specifically, those with Cu ± Mo ± Au porphyry-style mineralization), LA-ICP-MS analyses (guided by µXRF-EDS mapping and SEM-BSE imaging of polished thin sections) of zircons from 13 granitoids was conducted. The zircons studied were similar in terms of their textures (homogenous cores, patchy zoning, oscillatory zoning, and some unzoned zircon); however, they have a wide range of trace- and minor-element (Hf, HREE, Y, Th, U) compositions. Specifically, Zr/Hf ranges between 24–60, whereas Th/U ranges between 0.15 and 5.37. The presence of inherited zircon affects the concentrations of Th and U, as well as other key elements. Estimated crystallization temperatures of granitoids, ranging from 737 to 899°C, were calculated via Ti-in-zircon geothermometry assuming reduced TiO2 and SiO2 activities. The calculated log fO2 values for zircons from some of these granitoids indicate a highly oxidized magmatic signature. Zr/Hf, Eu/Eu⁎, and (Eu/Eu⁎)/Y in zircon, as well as zircon (Ce/Nd)/Y are some of the best indicators of porphyry fertility. The Ce/Ce* in zircon exhibit a large range (1.1–590), with higher Ce/Ce* reflecting more metallogenically favourable oxidizing conditions. If Eu/Eu⁎ in zircon is ≥0.4 (relatively oxidized conditions), it indicates a high potential for an ore-forming porphyry Cu mineralizing system. Lower Eu contents reflect relatively reducing conditions, as Eu anomalies vary with oxygen fugacity as well, and the relative abundance of Eu2+ is higher, but does not substitute into the zircon lattice. The evidence extracted from analyzing the zircon composition within New Brunswick’s I-type granitoids indicates the fertility of these hypabyssal intrusions.

A New R35 and Fractal Joint Rock Typing Method Using MICP Analysis: A Case Study in Middle East Iraq

Reservoir characterization in carbonate formations plays a crucial role in understanding the complex pore structures and permeability properties. While absolute pore-throat radius (APTR) and R35 have been widely accepted in pore-scale rock typing, they fall short of providing detailed pore-throat distribution (PTD) information. To address this limitation and enhance PTD indication during rock classification, we introduce a novel approach—the R35 and fractal joint rock typing method—incorporating a new parameter, Dn (fractal dimension). This method is developed based on the analysis of mercury injection capillary pressure (MICP) data obtained from 20 carbonate samples in the Middle East, specifically Iraq. We delve into the discussion of APTR and R35 methods, employing both PTD and thin-section images to gain comprehensive insights into rock typing. Our approach incorporates a whole curve fractal-based model to determine the fractal dimension, Dn. The analysis reveals that Dn in each R35 rock type exhibits a decreasing trend with higher Hg intrusion peaks and wider pore-throat radius, providing valuable information on the distribution of pores within the rock samples. Furthermore, we extend our analysis to include the surface fractal dimension, Ds, obtained through two-dimensional (2D) fractal analysis on typical pores in binary thin-section images. The consistent decreasing trend of Dn aligns with the findings from Ds analysis, underscoring the effectiveness of parameter Dn in capturing the intricate pore structures within carbonate formations. Our results suggest that parameter Dn has the potential to serve as a standalone criterion in rock typing, eliminating the need for pretyping by R35 or APTR. The versatility of Dn as an indicator of pore distribution and complexity underscores its significance in advancing our understanding of carbonate reservoirs. As a recommendation, further exploration through additional fractal-based analyses is encouraged to solidify the role of parameter Dn in becoming a pivotal factor in the evolving field of rock typing.

Automated Reservoir Characterization of Carbonate Rocks using Deep Learning Image Segmentation Approach

Summary The objective of this study is to develop a systematic and novel workflow for the automated and objective characterization of carbonate reservoirs with the help of deep learning architectures. An image database of more than 6,000 carbonate thin-section images was generated using the optical microscope and image augmentation techniques. Five features, namely clay/silt/mineral, calcite, pores, fossils, and opaque minerals, were identified with the help of manual petrography of the thin sections under the microscope. A total of four deep learning models were developed, which included U-Net, U-Net with ResNet34 backbone, U-Net with Mobilenetv2 backbone, and LinkNet with ResNet34 backbone. The Ensemble model of U-Net + ResNet34 and U-Net + MobileNetv2 yielded the highest intersection over union (IoU) score of 75%, followed by the U-Net + ResNet34 model with an IoU score of 61%. The models struggled with class imbalance, which was very prominent in the image database, with classes such as fossils and opaques considered to be rare. The statistical analysis of the relative errors revealed that the major classes play a more important role in increasing the final IoU score as opposed to the common understanding that the rare classes affect the model performance. The novel workflow developed in this paper can be extended to real carbonate reservoirs for time efficient, objective, and accurate characterization.

Experiments on image data augmentation techniques for geological rock type classification with convolutional neural networks

The integration of image analysis through deep learning (DL) into rock classification represents a significant leap forward in geological research. While traditional methods remain invaluable for their expertise and historical context, DL offers a powerful complement by enhancing the speed, objectivity, and precision of the classification process. This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks (CNNs) for geological image analysis, particularly in the classification of igneous, metamorphic, and sedimentary rock types from rock thin section (RTS) images. This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision. Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities, achieving an F1-Score of 0.9869 for igneous rocks, 0.9884 for metamorphic rocks, and 0.9929 for sedimentary rocks, representing improvements compared to the baseline original results. Moreover, the weighted average F1-Score across all classes and techniques is 0.9886, indicating an enhancement. Conversely, methods like Distort lead to decreased accuracy and F1-Score, with an F1-Score of 0.949 for igneous rocks, 0.954 for metamorphic rocks, and 0.9416 for sedimentary rocks, exacerbating the performance compared to the baseline. The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results. The findings of this study can benefit various fields, including remote sensing, mineral exploration, and environmental monitoring, by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.

An improved corner dealiasing and recognition algorithm for 2D Wadell roundness computation

This paper optimizes the 2D Wadell roundness calculation of particles based on digital image processing methods. An algorithm for grouping corner key points is proposed to distinguish each independent corner. Additionally, the cyclic midpoint filtering method is introduced for corner dealiasing, aiming to mitigate aliasing issues effectively. The relationships between the number of corner pixels (m), the central angle of the corner (α) and the parameter of the dealiasing degree (n) are established. The Krumbein chart and a sandstone thin section image were used as examples to calculate the 2D Wadell roundness. A set of regular shapes is calculated, and the error of this method is discussed. When α ≥ 30°, the maximum error of Wadell roundness for regular shapes is 5.21%; when 12° ≤ α < 30°, the maximum error increases. By applying interpolation to increase the corner pixels to the minimum number (m0) within the allowable range of error, based on the α-m0 relational expression obtained in this study, the error of the corner circle can be minimized. The results indicate that as the value of m increases, the optimal range interval for n also widens. Additionally, a higher value of α leads to a lower dependence on m. The study's results can be applied to dealiasing and shape analysis of complex closed contours.

Multi-channel attention transformer for rock thin-section image segmentation

Accurate rock thin-section image segmentation can help to analyze the chemical composition, particle size distribution, pore structure and cement composition. However, precise instance segmentation is currently challenging due to the issues of small sample size, lack of integration of sequence images with different lighting angles and low representation learning capability. To address the aforementioned challenges, this paper introduces a groundbreaking Multi-Channel Attention Transformer (MCAT) approach for rock thin-section image segmentation. At first, the copy paste method is applied for data augmentation to overcome the small sample issue. Secondly, a novel multi-channel attention module is developed to integrate the correlation between the image sequence derived from different lighting angles. Finally, the powerful Transformer module is employed to enhance feature learning. The experiments conducted on the real rock thin image dataset validate the superiority of the proposed MCAT approach over the existing methods.

OmniSR-M: A Rock Sheet with a Multi-Branch Structure Image Super-Resolution Lightweight Method

With the rapid development of digital core technology, the acquisition of high-resolution rock thin section images has become crucial. Due to the limitation of optical principles, thin section imaging involves a contradiction between resolution and field of view. In order to solve this problem, this paper proposes a lightweight, fully aggregated network with multi-branch structure for super resolution of rock thin section images. The experimental results on the rock thin section dataset demonstrate that the improved method, called OmniSR-M, achieves significant enhancement compared to the original OmniSR method and also surpasses other state-of-the-art methods. OmniSR-M effectively recovers image details while maintaining its lightweight nature. Specifically, OmniSR-M reduces the number of parameters by 26.56% and the computation by 27.66% compared to OmniSR. Moreover, this paper quantitatively analyzes both the facies porosity rate and grain size features in the application scenario. The results show that the images generated by OmniSR-M successfully recover key information about the rock thin section.

Enhancing texture feature for mineral classification in tight sandstone rock thin-section images using super-resolution techniques

In petrological studies, accurately classifying minerals and pore objects in rock thin-section images is essential for understanding geological conditions of oil and gas reservoirs. A key challenge in current methodologies is the limited image resolution, which restricts the accuracy of texture feature recognition crucial for effective classification. This study introduces super-resolution (SR) techniques to overcome this limitation, aiming to enhance image resolution and thereby improve the recognition and classification of texture features in thin-section imagery. To evaluate the effectiveness of various SR methods, we conducted a series of experiments evaluating their impact on mineral type classification, employing advanced techniques such as Gabor filtering and SVM classifiers for texture feature extraction and classification.Our experiments demonstrated that all evaluated SR techniques can significantly enhance classification accuracy, with each method improving accuracy by at least 19.3% for mixed color spaces. Notably, the Enhanced Deep Super-Resolution (EDSR) model, while less intuitive for human visual assessment, emerged as highly effective in extracting intricate texture features, resulting in an approximate 24.2% increase in accuracy within multi color spaces. These results suggest that the most effective SR models for enhancing texture details are not necessarily aligned with human visual evaluation. The novelty of this work is grounded in its comprehensive assessment of SR techniques in petrological imaging, marking a substantial advancement over previous methods. This study not only enhances the discernibility of critical texture features in rock thin-section imagery but also provides a clear pathway for integrating advanced image processing techniques into petrological studies.

Cone-Beam CT of the Extremities in Clinical Practice.

Cone-beam CT (CBCT) is a promising tool with increasing applications in musculoskeletal imaging due to its ability to provide thin-section CT images of the appendicular skeleton and introduce weight bearing, which accounts for loading forces that typically interact with and affect this anatomy. CBCT devices include an x-ray source directly opposite a digital silicon detector panel that performs a single rotation around an object of interest, obtaining thin-section images. Currently, the majority of research has been focused on the utility of CBCT with foot and ankle pathologic abnormalities, due to the complex architectural arrangement of the tarsal bones and weight-bearing nature of the lower extremities. Associated software can provide a variety of options for image reconstruction, including metal artifact reduction, three-dimensional biometric measurements, and digitally reconstructed radiographs. Advancements in this technology have allowed imaging of the knee, hip, hand, and elbow. As more data are published, it is becoming evident that CBCT provides many additional benefits, including fast imaging time, low radiation dose, lower cost, and small equipment footprint. These benefits allow placement of CBCT units outside of the traditional radiology department, including the orthopedic clinic setting. These technologic developments have motivated clinicians to define the scope of CBCT for diagnostics, surgical planning, and longitudinal imaging. As efforts are made to create standardized protocol and measurements, the current understanding and surgical approach for various orthopedic pathologic conditions will continue to shift, with the hope of improving outcomes. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.

Polarization-Based Digital Histology of Human Skin Biopsies Assisted by Deep Learning

Mueller polarimetry has proven to be a powerful optical technique to complement medical doctors in their conventional histology analysis. In this work, various degenerative and malignant human skin lesions were evaluated ex vivo using imaging Mueller polarimetry. The Mueller matrix images of thin sections of biopsies were recorded and the differential decomposition of Mueller matrices was applied pixel-wise to extract the polarization fingerprint of the specimens under study. To improve the classification accuracy, a deep learning model was created. The results indicate the sensitivity of polarimetry to different skin lesions and healthy skin zones and their differentiation, while using standard histological analysis as a ground truth. In particular, the deep learning model was found sufficiently accurate to detect and differentiate between all eight classes in the data set. Special attention was paid to the overfitting problem and the reduction of the loss function of the model. Our approach is an effort in establishing digital histology for clinical applications by complementing medical doctors in their diagnostic decisions.

Large Volume Analysis of Core and Thin Section Images in the Assessment of Brazil Pre-Salt Reservoir Distribution

Large Volume Analysis of Core and Thin Section Images in the Assessment of Brazil Pre-Salt Reservoir Distribution