3D Imaging Research Articles

Can Radiologists Replace Digital 2D Mammography with Synthetic 2D Mammography in Breast Cancer Screening and Diagnosis, or Are Both Still Needed?

Background/Objectives: Digital mammography (DM) has long been the standard for breast cancer screening, while digital breast tomosynthesis (DBT) offers an advanced 3D imaging modality capable of generating 2D Synthetic Mammography (SM) images. Despite SM’s potential to reduce radiation exposure, many clinics favor DM, with DBT and SM, due to its perceived diagnostic reliability. This study investigates whether radiologists can replace DM with SM in breast cancer screening and diagnosis or if both modalities are necessary. Methods: We retrospectively analyzed DM and SM images from 375 women aged 40–65 who underwent DM with DBT at King Khaled University Hospital from 2020–2022. Three radiologists evaluated the images using ACR BI-RADS, assessing diagnostic accuracy via the area under the receiver operating characteristic (ROC) curve (AUC). The agreement in cancer conspicuity, breast density, size, and calcifications were measured using weighted kappa (κ). Results: Among 57 confirmed cancer cases and 290 cancer-free cases, DM demonstrated higher sensitivity (82.5% vs. 78.9%) and diagnostic accuracy (AUC 0.800 vs. 0.783, p < 0.05) compared to SM. However, SM detected more suspicious calcifications in cancer cases (75.6% vs. 51.2%, p < 0.05). Agreement was fair for conspicuity (κ = 0.288) and calcifications (κ = 0.409), moderate for density (κ = 0.591), and poor for size (κ = 0.254). Conclusions: while SM demonstrates enhanced effectiveness in detecting microcalcifications, DM still proves superior in overall diagnostic accuracy and image clarity. Therefore, although SM offers certain advantages, it remains slightly inferior to DM and cannot yet replace DM in breast cancer screening.

Permanent deformation and particle crushability of calcareous sands under cyclic traffic-induced loadings through simple shear apparatus

This study focuses on investigating permanent deformations, encompassing normal and shear strains, of calcareous sandy soils subjected to drained cyclic traffic-induced loadings. The investigation utilizes a Simple Shear (SS) apparatus allowing for variations in both normal and shear stress components over each cycle and incorporates Principal Stress Rotation (PSR), a feature not replicable in conventional cyclic uniaxial triaxial tests which is the key aspect of this research. The study also accounts for the harmonically changing the effective horizontal stress resulting from cyclic variations of effective vertical stress, conducted under a horizontally constrained boundary condition with zero lateral strain. A series of drained cyclic simple shear experiments is carried out, implementing a heart-shaped stress path, encompassing up to 1000 cycles. The objective of the study is to analyze permanent normal and shear deformations, along with associated total particle crushing, using both sieving analyses and 2D image processing of particles. The study also evaluates the impact of an initial static shear stress originating from the longitudinal slope of roads. The findings highlight the influences of induced cyclic amplitudes of stress components, principal stress and strain rate rotation, and initial static shear stress on the development of permanent strains. Furthermore, the research characterizes particle crushability in terms of total crushability over such loading, examining its dependency on relative density and variations in both shear and normal stress components.

Robot-Assisted Reduction of the Ankle Joint via Multi-Body 3D–2D Image Registration

Robot-Assisted Reduction of the Ankle Joint via Multi-Body 3D–2D Image Registration

Three-Dimensional Fluoroscopic System to Assess Robotically Placed Pedicle Screws: Should We Confirm Robotic Pedicle Screw Placement With Advanced Imaging?

Retrospective cohort study. The purpose of this study is to determine the utility of advanced imaging to confirm the placement of robotic pedicle screws. With increasing robotic adoption, certain institutions and surgeons have developed protocols for obtaining 3D intraoperative imaging after robotic pedicle screw placement to ensure proper hardware placement. No studies have assessed the utility of these protocols relative to the potential risks of increased radiation exposure and operative time. The purpose of this study is to determine if we should be obtaining advanced imaging to confirm the placement of robotic pedicle screws. This is a single institution retrospective cohort study of patients from May 2022 to July 2023 who underwent lumbar spinal fusion by a high-volume orthopedic spine surgeon at a level 1 metropolitan hospital. All cases used combined robotics and navigation systems for pedicle screw placement and intraoperative 3D imaging for evaluation of screw position. Pedicle screw accuracy was assessed using the Gertzbein and Robbins system (GRS). Acceptable pedicle screw position was defined as GRS A or B. Seventy patients with 354 robotically placed pedicle screws were assessed with intraoperative 3D fluoroscopy. All pedicle screws were placed in either a GRS type A or type B position. Three hundred forty-seven were placed in a GRS A classification (99.2%, 351/354), and 3 were placed in a GRS B classification (0.08% 3/354). No patients had screw-related complications. The average radiation dosage of 3D imaging was 289.7±164.6mGy. The robotic system places pedicle screws accurately without 3D intraoperative imaging. Given the increased radiation and operative time associated with 3D imaging protocols 3D imaging scans should only be obtained in cases with heightened clinical concern. Level IV.

Digital Manufacturing System of Custom Wigs with Measurable 3D Images

Wigs are major important means for improving life quality of the increasing people suffered from hair loss through their image enhancements. For the production of custom made wigs, there requires a mold that reproduces the wearer’s head dimension and the shape of hair loss. The conventional method for this is to make a resin film that adheres to the wig wearer’s head using vinyl tape, and to make a wig pattern with drawing the shape of hair loss onto the film, and to produce a head mold with this pattern, and then to plant hair in a wig cap made based on this mold and wig pattern. This method requires highly skilled manual work, and generates a large amount of chemical waste during the manufacturing process, and takes a long manufacturing period. However, the digital automation of measuring head dimension and the shape of hair loss can replace or omit the production of wig patterns. In this study, a 3D head image capable of actual measurement was implemented by using a 3D polygon head shape measurement device and a camera, which makes a digital production system possible, which can replace the manual wig pattern production process for custom wigs.

Resolution Improvement in Near-Virtual-Image-Mode Light-Field Display Using Resolution-Priority Technique

A light-field display with a near-virtual-image mode, which employs both a lens array and an aperture array, was previously proposed to provide a wide viewing zone angle and bright three-dimensional (3D) images. However, it is desirable to enhance its resolutions, which are presently equal to those of conventional displays. Thus, we proposed a technique for increasing the resolutions of 3D images generated by the light-field display with the near-virtual-image mode. The gap between the flat-panel display and lens array is reduced to decrease the magnification of the virtual images of the pixels and to enable the observation of multiple virtual pixel images through each lens. Further, we imaged the aperture array using the lens array to eliminate the gaps between the multiple pixels observed through adjacent lenses. We constructed a prototype display based on the proposed technique and verified the increase in the resolution of the prototype display compared to the original near-virtual-image light-field display.

An Artificial Intelligence-Based Hybrid Approach to Detect the Type of Buried Objects with Broad Frequency Band Antenna Systems

Knowing the type of buried object before excavation prevents unnecessary excavation. Moreover, it saves time and money. In this study, an experiment set was prepared for the detection of buried objects. The experimental set was composed of an antenna that sends and receives electromagnetic waves in a wide frequency band, software that records and processes reflections, and a sandbox. In the study, metallic and non-metallic objects with different depths, sizes and shapes were buried in this sand pool and measurements were taken along a profile. 2D images were created from the measurements and image processing techniques were applied to these images. Classification algorithms were used to detect the type of bruied object from processed images. To increase the success of the algorithms, correlation-based attribute selection (CFS) and Principal Component Analysis (PCA) were used as attribute selection techniques. Genetic algorithm (GA), Particle Swarm Optimization (PSO), Harmony search (HA), and Evolutionary search (EA), which are among the metaheuristic optimization algorithms, were preferred as search methods in attribute selection with CFS. The performance of the algorithms was analyzed using the 10-fold cross-validation method. As a result, it was understood that the use of the PCA algorithm in attribute selection increases the classification success more than metaheuristic algorithms. The most successful among the classification algorithms used is the Random tree algorithm. After PCA, the accuracy value of this algorithm was 95.8 Therefore, a hybrid approach is proposed in which PCA and Random tree algorithms are used in the software embedded in the measurement system.

Contribution of 3D virtual modeling in locating hepatic metastases, particularly “vanishing tumors”: a pilot study

Aim: This article aims to explore how three dimensions (3D) virtual modeling enhances accuracy and efficiency in detecting hepatic metastases, with a specific focus on “vanishing tumors” that are difficult to detect using traditional imaging techniques. It also aims to demonstrate the potential impact of these advanced technologies on improving diagnostic and treatment strategies for patients with liver cancer. Methods: Eight patients with liver metastases from colorectal cancer were studied using magnetic resonance imaging (MRI) and 3D virtual modeling to enhance surgical planning by accurately locating lesions. The concordance between these imaging techniques and the Gold Standard was assessed using Gwet’s AC1 coefficient, with statistical analysis performed through resampling methods and non-parametric tests due to non-normal AC1 distribution. Liver segmentation was conducted semi-automatically using SurgiPrint software, which is crucial for detecting lesions undetectable post-chemotherapy. The study’s evaluation model involved questionnaires for medical professionals across four cohorts, aiming to determine the 3D model’s effectiveness in identifying lesion locations for surgery. Results: This study investigated the efficacy of 3D virtual modeling in identifying hepatic metastases, particularly comparing its accuracy with traditional MRI in locating lesions. The findings indicate that MRI generally provided better concordance with the Gold Standard for lesion localization, except for a few experienced users who had prior familiarity with the 3D models. Despite the mixed results in accuracy, the study suggests potential benefits of 3D modeling in enhancing surgical planning and execution, particularly in detecting “vanishing” liver metastases that are difficult to visualize with standard imaging techniques. The research aligns with broader evidence indicating the utility of 3D models in improving outcomes in hepatic surgery by enabling more precise resections and reducing postoperative complications. However, the study also notes the challenge of quantifying the added value of 3D modeling due to the unique nature of each surgical case and the potential bias in the user experience with the technology. Conclusion: The conclusion of this study underscores the significant potential of 3D virtual modeling in enhancing the precision of locating and resecting hepatic metastases, particularly those elusive “vanishing tumors”. This research underscores the value of integrating advanced 3D imaging techniques into surgical practice, suggesting a paradigm shift toward more accurate and less invasive liver surgeries. Future studies should aim to further quantify the benefits of 3D modeling in surgical outcomes, investigate its utility across different surgical experiences, and explore the integration of artificial intelligence (AI) to maximize its effectiveness in clinical settings.

Voxel-wise segmentation for porosity investigation of additive manufactured parts with 3D unsupervised and (deeply) supervised neural networks

AbstractAdditive Manufacturing (AM) has emerged as a manufacturing process that allows the direct production of samples from digital models. To ensure that quality standards are met in all samples of a batch, X-ray computed tomography (X-CT) is often used in combination with automated anomaly detection. For the latter, deep learning (DL) anomaly detection techniques are increasingly used, as they can be trained to be robust to the material being analysed and resilient to poor image quality. Unfortunately, most recent and popular DL models have been developed for 2D image processing, thereby disregarding valuable volumetric information. Additionally, there is a notable absence of comparisons between supervised and unsupervised models for voxel-wise pore segmentation tasks. This study revisits recent supervised (UNet, UNet++, UNet 3+, MSS-UNet, ACC-UNet) and unsupervised (VAE, ceVAE, gmVAE, vqVAE, RV-VAE) DL models for porosity analysis of AM samples from X-CT images and extends them to accept 3D input data with a 3D-patch approach for lower computational requirements, improved efficiency and generalisability. The supervised models were trained using the Focal Tversky loss to address class imbalance that arises from the low porosity in the training datasets. The output of the unsupervised models was post-processed to reduce misclassifications caused by their inability to adequately represent the object surface. The findings were cross-validated in a 5-fold fashion and include: a performance benchmark of the DL models, an evaluation of the post-processing algorithm, an evaluation of the effect of training supervised models with the output of unsupervised models. In a final performance benchmark on a test set with poor image quality, the best performing supervised model was UNet++ with an average precision of 0.751 ± 0.030, while the best unsupervised model was the post-processed ceVAE with 0.830 ± 0.003. Notably, the ceVAE model, with its post-processing technique, exhibited superior capabilities, endorsing unsupervised learning as the preferred approach for the voxel-wise pore segmentation task.

Research on the Laser Scattering Characteristics of Three-Dimensional Imaging Based on Electro–Optical Crystal Modulation

In this paper, we construct a laser 3D imaging simulation model based on the 3D imaging principle of electro–optical crystal modulation. Unlike the traditional 3D imaging simulation method, this paper focuses on the laser scattering characteristics of the target scene. To accurately analyze and simulate the scattering characteristic model of the target under laser irradiation, we propose a BRDF (Bidirectional Reflectance Distribution Function) model fitting algorithm based on the hybrid BBO–Firefly model, which can accurately simulate the laser scattering distribution of the target at different angles. Finally, according to the fitted scattering characteristic model, we inverted the target imaging gray map. We used the laser 3D imaging restoration principle to reconstruct the 3D point cloud of the target to realize the laser 3D imaging of the target.

Machine learning assisted crystallographic reconstruction from atom probe tomographic images

Atom probe tomography (APT) is a powerful technique for three-dimensional (3D) atomic-scale imaging, enabling the accurate analysis on the compositional distribution at the nanoscale. How to accurately reconstruct crystallographic information from APT data, however, is still a great challenge due to the intrinsic nature of the APT technique. In this paper, we propose a novel approach that consists of the modified forward simulation process and the backward machine learning process to recover the tested crystal from APT data. The high-throughput forward simulations on Al single crystals of different orientations generate 10 000 original 3D images and data augmentation is implemented on the original images, resulting in 100 000 3D images. The big data allows the development of deep learning models and three deep learning algorithms of Convolutional Neural Network (CNN), Vision Transformer (ViT), and Variational Autoencoder (VAE) are used in the backward process. After training, the ViT model performs superior than the CNN and VAE models, which can recover the crystalline orientation outstandingly, as evaluated by the coefficient of determinationR2and the Mean Percent Error (MPE), viz.,R2= 0.93 and MPE = 0.43%,R2= 0.97 and MPE = 0.35%, andR2= 0.93 and MPE = 0.77% for the rotation anglesϕ,ψandθ, respectively, on the test dataset. The present work clearly demonstrates the capability of deep learning models in the recovery of the tested crystals from APT data, thereby paving the way for the further development of large artificial intelligent models of APT.

Analyzing 3D facial morphology: Insights from a comparative European and South African study on population affinity, sex, age, and allometry

Variable growth patterns and multifactorial mechanisms cause variation in facial shape. These differences in facial morphology pose challenges for craniofacial reconstruction. Three-dimensional (3D) imaging modalities are a valuable resource for examining these variations. In this study, we used geometric morphometric methods to evaluate the effects of population affinity, sex, age, and allometry on the variation and covariation of hard and soft tissue facial morphology matrices in a sample of French and white South African individuals. Seventy-six and 108 cone-beam computed tomography scans of white South African and French nationals, respectively, were retrospectively acquired. Three-dimensional anatomical structures (hard and soft tissue matrices) were extracted using MeVisLab© v. 2.7.1 software for dense landmarking of 43 craniometric, 50 capulometric, and 559 sliding landmarks. Geometric morphometric analyses were used to quantify shape differences attributed to population affinity, sex, age, and allometry and assess the covariation between hard tissue structures and soft facial tissues. Hard and soft tissue facial matrices were influenced by population differences, sexual dimorphism, and aging. Compared to sex and age, population affinity had the strongest influence on variation. In French individuals, all hard and soft tissue matrices were sexually dimorphic, except for the eyes and left external auditory meatus (EAM). In white South Africans, sexual dimorphism was observed for the mouth, midface, and left EAM. Significant shape differences were also observed for specific age groups. The underlying skull and overlying soft tissues were strongly associated with the nose and anterior nasal aperture (correlation, r2-PLS = 0.976), followed by the right ear and right EAM (r2-PLS = 0.875) and the left ear and left EAM (r2-PLS = 0.871) in white South Africans. For French individuals, relatively weak to moderate correlations were observed, and the covariation between matrices was nonsignificant, except for the association between the right ear and right EAM (r2-PLS = 0.499). The smallest covariation was observed between the mouth and midfacial matrix in both populations (South African: r2-PLS = 0.464; French: r2-PLS = 0.367), which was also nonsignificant. This study revealed that 3D imaging technology and geometric morphometric methods can accurately quantify and visualize facial morphology differences. These methods can also evaluate the association between skull structure and soft facial features.

The comparison of the efficacy of 3D fast spoiled gradient echo sequence and steady state free precession sequence in imaging vascular compression in patients with trigeminal neuralgia

Aims: Trigeminal neuralgia is a condition characterized by sudden and intense pain in the face, affecting the fifth cranial nerve. While it can occur for various reasons, vascular compression is a well-known cause. Additionally, conditions such as tumors, arachnoid cysts, idiopathic inflammation, damage, or demyelination can result in trigeminal neuralgia, extending to the distal branches of the trigeminal nerve from the pons. We aimed to investigate the diagnostic efectiveness of post-contrast three-dimensional fast spoiled gradient echo images (3D-FSPGR) in addition to steady-state free precession (SSFP) images in the imaging of trigeminal neuralgia. Methods: A total of 33 patients with a preliminary diagnosis of trigeminal neuralgia between May 2022 and October 2023 were investigated and five individuals were excluded. Among the remaining 28 patients, vascular compression was observed in only 14 patients. Trigeminal nerve thickness on both side, the distance between trigeminal nerve and superior cerebellar artery on both side, presence of vascular compression, level of the compression, presence or absence of displacement and atrophy were reported for each sequences. The findings and results were compared using independent sample T-test and paired T-test. Results: The mean age of the patient group was calculated to be 56.29 ± 4.5, while for the control group, it was 52.07 ± 3.09. Trigeminal nerve thickness wasn’t different between patient with or without vascular compression (p=0.874 for right side on SSFP, p=0.804 for left side on SSFP, p=0.667 for right side on 3D-FSPGR and 0.769 for left side on 3D-FSPGR). In 10 patients, unilateral compression of the trigeminal nerve was observed, while in 4 patients, it was bilateral. In the right trigeminal nerve, vascular compression was observed in SSFP images in 5 cases in the transitional zone, 2 cases in the nerve root entry zone, and 1 case distally. In 3D-FSPGR images, it was interpreted as being in the transitional zone in 4 cases, in the NR entry zone in 3 cases, and distally in 1 case. On the other hand, for the left trigeminal nerve, vascular compression was observed in both SSFP and 3D FSGPR images in 5 cases in the nerve root entry zone, 3 cases in the distal zone, and 2 cases in the transitional zone. Conclusion: Both SSFP and 3D-FSPGR images can provide comparable information regarding the spatial relationship between the trigeminal nerve and vascular structures. Contrast enhanced imaging allows us diferentiate the tumoral and infammatory demyelinating processes from vascular compression, we recommended to continue with SSFP images in cases where contrast enhanced 3D FSGPR images are inconclusive.

Mixed reality combined with surgical navigation technology assisted in parapharyngeal space tumors surgery: a clinical study

Objective: To assess the feasibility and application of mixed reality combined with surgical navigation technology in parapharyngeal space tumor surgery, and to provide a reference for the development and promotion of this technology. Methods: In this study, retrospective data collection was conducted on 16 patients with parapharyngeal space tumors who were treated at the Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology from June 2020 to June 2023. The patient's age was (39.6±17.8) years, with 4 males and 12 females. Mixed reality combined with surgical navigation technology was utilized to assist physicians in the treatment of these patients. Mixed reality combined with surgical navigation technology was used to assist physicians in treatment of these patients. The application steps included acquisition of image data, processing of image data [three-dimensional (3D) reconstruction, image fusion, and virtual surgical design], development of surgical navigation plan, connection of mixed reality and navigation system, automatic registration and intraoperative guidance and validation. In the preoperative plan, landmark points were placed on the virtual tumor and surrounding important structures reconstructed using digital software, serving to guide the localization of crucial anatomical structures. Intraoperative positioning deviation, surgical time, intraoperative blood loss, and postoperative complications were recorded and analyzed to evaluate the clinical application effectiveness of mixed reality combined with surgical navigation technology. Results: With the assistance of mixed reality combined with surgical navigation technology, 16 patients successfully underwent tumor resection. All patients were accurately diagnosed preoperatively by 3D reconstruction and image fusion technology, and a comprehensive preoperative plan was formulated; intraoperatively, mixed reality combined with surgical navigation technology was utilized for the localization of important structures. The average localization deviation of 38 landmark points during the operation were (4.43±1.96) mm, with 62% (26/42) of the points having a deviation of ≥0 and<5 mm. The average duration of the operation was (149.6±53.9) min and the blood loss was 70 (45, 150) ml. The average postoperative follow-up was 16 months, and five patients experienced postoperative complications involving facial paralysis, hoarseness, and choking. Conclusions: Mixed reality combined with surgical navigation technology can achieve the three-dimensional visualization of oral and maxillofacial anatomical structures to achieve precise preoperative diagnosis. During surgery, the technology can real-time display the relationship between soft tissue tumors and the surrounding important anatomical structures, guide surgical operation, and enhance the safety of surgery.

Large FoV 3D imaging of single-photon LiDAR at up to 12 km

Large FoV 3D imaging of single-photon LiDAR at up to 12 km

Artificial intelligence-based automated determination in breast and colon cancer and distinction between atypical and typical mitosis using a cloud-based platform

Artificial intelligence (AI) technology in pathology has been utilized in many areas and requires supervised machine learning. Notably, the annotations that define the ground truth for the identification of different confusing process pathologies, vary from study to study. In this study, we present our findings in the detection of invasive breast cancer for the IHC/ISH assessment system, along with the automated analysis of each tissue layer, cancer type, etc. in colorectal specimens. Additionally, models for the detection of atypical and typical mitosis in several organs were developed using existing whole-slide image (WSI) sets from other AI projects. All H&amp;amp;E slides were scanned by different scanners with a resolution of 0.12–0.50 μm/pixel, and then uploaded to a cloud-based AI platform. Convolutional neural networks (CNN) training sets consisted of invasive carcinoma, atypical and typical mitosis, and colonic tissue elements (mucosa-epithelium, lamina propria, muscularis mucosa, submucosa, muscularis propria, subserosa, vessels, and lymph nodes). In total, 59 WSIs from 59 breast cases, 217 WSIs from 54 colon cases, and 28 WSIs from 23 different types of tumor cases with relatively higher amounts of mitosis were annotated for the training. The harmonic average of precision and sensitivity was scored as F1 by AI. The final AI models of the Breast Project showed an F1 score of 94.49% for Invasive carcinoma. The mitosis project showed F1 scores of 80.18%, 97.40%, and 97.68% for mitosis, atypical, and typical mitosis layers, respectively. Overall F1 scores for the current results of the colon project were 90.02% for invasive carcinoma, 94.81% for the submucosa layer, and 98.02% for vessels and lymph nodes. After the training and optimization of the AI models and validation of each model, external validators evaluated the results of the AI models via blind-reader tasks. The AI models developed in this study were able to identify tumor foci, distinguish in situ areas, define colonic layers, detect vessels and lymph nodes, and catch the difference between atypical and typical mitosis. All results were exported for integration into our in-house applications for breast cancer and AI model development for both whole-block and whole-slide image-based 3D imaging assessment.

Measuring pore sizes of nonwoven geotextiles by X-ray CT images: a comparative evaluation

This paper focuses on evaluating the pore sizes of four nonwoven geotextile specimens with different pore size distribution techniques. The paper introduces a bubble analysis technique to find the two-dimensional (2D) pore size distribution of geotextile specimens. A new method is also introduced to determine the three-dimensional (3D) constriction sizes of nonwoven geotextiles from X-ray CT images. The digital image concept and image processing methods for the 2D image analysis and 3D constriction size measurement of geotextile specimens are explained. Finally, the paper compares the 3D constriction sizes with those measured from 2D image analysis, mercury intrusion porosimetry, manufacturer-reported opening size values based on the wet sieving method, and pore sizes calculated from theoretical equations. The results show that the largest pore opening sizes measured from the 3D constriction size method are in good agreement with the theoretical equations. The values measured from the wet sieving method were found to be the smallest, indicating the limitations of the wet sieving method to determine the largest pore opening size of nonwoven geotextiles. As it is the constriction size which must control filtration behaviour, the 3D constriction size method is recommended for assessing the reference porometry.

Temperature and rate dependent shear characterization and modeling of spread-tow woven Flax/PP composite laminates

This paper investigates the in-plane shear behavior of spread-tow woven structured flax fiber-reinforced polypropylene composites, focusing on temperature and displacement rate effects. For this aim, the study includes bias-extension experiments to characterize the shear behavior of the material. Several experiments were conducted at different displacement rates and temperature levels. The temperature levels were chosen to encompass the material's behavior during solid and molten-state thermoforming, given the melting and decomposition temperatures acquired from differential scanning calorimetry and thermogravimetric analysis tests, respectively. A new fixture was designed and manufactured to allow pre-heating of the oven and rapid placement of the samples, which in turn, prevents their degradation for the time duration required to reach a uniform temperature distribution in the oven. During the experiments, the 3D digital image correlation technique accurately measured local deformations and strains on the specimen's surface under varying conditions. Further, a finite element model is developed, incorporating a fiber reorientation algorithm with a hypo-elastic shear model to simulate the material's temperature and rate-dependent behavior. The finite element shear angle distributions, as well as the shear angle-force and shear angle-displacement curves, are compared with the Digital Image Correlation (DIC) data and load measurements for different test conditions, showing good agreement between the numerical and experimental approaches.

Fluorescent emission and nondestructive 3D textural imaging in geologic materials by multiphoton microscopy

This work greatly expands the application of multiphoton microscopy to geological investigations by using a tightly focused femtosecond laser beam to excite fluorescent emissions among minimally prepared rock and mineral samples. This novel finding provides a tool for spatially resolving UV-visible fluorescent sources in minerals. Through a combination of harmonic generation and fluorescence, unique opportunities are made available for mineralogical investigations of terrestrial rocks and astromaterials. This report includes the first demonstrations for 3D imaging of fluid inclusions in minerals and radiation-induced luminescence imaging in meteorites. Nonlinear optical mineralogy, enabled by multiphoton microscopy, provides unique insights into mineralogic samples and holds the potential to revolutionize the analysis of geologic and astromaterials samples in the coming years.

Correlative X-ray micro-nanotomography with scanning electron microscopy at the Advanced Light Source.

Geological samples are inherently multi-scale. Understanding their bulk physical and chemical properties requires characterization down to the nano-scale. A powerful technique to study the three-dimensional microstructure is X-ray tomography, but it lacks information about the chemistry of samples. To develop a methodology for measuring the multi-scale 3D microstructure of geological samples, correlative X-ray micro- and nanotomography were performed on two rocks followed by scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS) analysis. The study was performed in five steps: (i) micro X-ray tomography was performed on rock sample cores, (ii) samples for nanotomography were prepared using laser milling, (iii) nanotomography was performed on the milled sub-samples, (iv) samples were mounted and polished for SEM analysis and (v) SEM imaging and compositional mapping was performed on micro and nanotomography samples for complimentary information. Correlative study performed on samples of serpentine and basalt revealed multiscale 3D structures involving both solid mineral phases and pore networks. Significant differences in the volume fraction of pores and mineral phases were also observed dependent on the imaging spatial resolution employed. This highlights the necessity for the application of such a multiscale approach for the characterization of complex aggregates such as rocks. Information acquired from the chemical mapping of different phases was also helpful in segmentation of phases that did not exhibit significant contrast in X-ray imaging. Adoption of the protocol used in this study can be broadly applied to 3D imaging studies being performed at the Advanced Light Source and other user facilities.