3D Imaging Research Articles

Metasurface-enabled 3D imaging via local bright spot gray scale matching using the structured light dot array.

Three-dimensional (3D) imaging is widely utilized in various applications, such as light detection, autonomous vehicles, and machine vision. However, conventional 3D imaging systems often rely on bulky optical components. Metasurfaces, as next-generation optical devices, possess flexible wavefront modulation capabilities and excellent combination with computer vision algorithms. Here, we propose a large field-of-view (FOV) structured light dot array projection device based on a metasurface, covering a 2π-FOV, for projecting coded point clouds in Fourier space. We explore a local bright spot gray scale matching algorithm for depth extraction, enabling 3D imaging. This algorithm simplifies the data processing flow and optimizes depth extraction and feature matching processes through a customized region gray scale comparison. As a result, it effectively reduces computational complexity and enhances tolerance to image quality fluctuations. The proposed approach provides new possibilities for developing compact and high-performance planar 3D optical imaging devices, which will drive the advancement of fields such as computer vision and artificial intelligence.

Imaging with a twist: Three-dimensional insights of the chiral nematic phase of cellulose nanocrystals via SHG microscopy.

Cellulose nanocrystals (CNCs) are bio-based nanoparticles that, under the right conditions, self-align into chiral nematic liquid crystals with a helical pitch. In this work, we exploit the inherent confocal effect of second-harmonic generation (SHG) microscopy to acquire highly resolved three-dimensional (3D) images of the chiral nematic phase of CNCs in a label-free manner. An in-depth analysis revealed a direct link between the observed variations in SHG intensity and the pitch. The highly contrasted 3D images provided unprecedented detail into liquid crystal's native structure. Local alignment, morphology, as well as the presence of defects are readily revealed, and a provisional framework relating the SHG response to the orientational distribution of CNC nanorods within the liquid crystal structure is presented. This paper illustrates the numerous benefits of using SHG microscopy for visualizing CNC chiral nematic systems directly in the suspension-liquid phase and paves the road for using SHG microscopy to characterize other types of aligned CNC structures, in wet and dry states.

3D manipulation of small multicellular organisms in soft microtubes

Small multicellular organisms, such as C. elegans and zebrafish larvae, are essential models in biological research, but their 3D manipulation poses challenges due to their small size. Manual positioning is labor-intensive and imprecise. To address this, we developed a soft PDMS microtube that can be gently stretched and released, immobilizing organisms without anesthetization and damage. This tube enables easy rotation and adjustment of orientation, facilitating comprehensive imaging. Functionality testing showed effective immobilization as well as precise imaging and microinjection. Zebrafish larvae were successfully injected with fluorescein in the hindbrain without anesthesia. This technique offers a simple, efficient, and non-damaging method for 3D manipulation and imaging of small multicellular organisms and provides a versatile tool for biological research practices.

Multifunctional computational fluorescence self-interference holographic microscopy

Fluorescence microscopy is crucial in various fields such as biology, medicine, and life sciences. Fluorescence self-interference holographic microscopy has great potential in bio-imaging owing to its unique wavefront coding characteristics; thus, it can be employed as three-dimensional (3D) scanning-free super-resolution microscopy. However, the available approaches are limited to low optical efficiency, complex optical setups, and single imaging functions. The geometric phase lens can efficiently manipulate the optical field’s amplitude, phase, and polarization. Inspired by geometric phase and self-interference holography, a self-interference fluorescent holographic microscope-based geometric phase lens is proposed. This system allows for wide-field, 3D fluorescence holographic imaging, and edge-enhancement from the reconstruction of only one complex-valued hologram. Experiments demonstrate the effectiveness of our method in imaging biological samples, with improved resolution and signal-to-noise ratio. Furthermore, its simplicity and convenience make it easily compatible with existing optical microscope setups, making it a powerful tool for observing biological samples and detecting industrial defects.

Occupational Radiation Exposure During Intraoperative 3-Dimensional Fluoroscopy in Pelvis and Acetabular Surgery.

To quantify the occupational radiation exposure with a 3-dimensional (3D) fluoroscopic machine during routine use in pelvic and acetabular surgery and to determine whether the additional radiation exposure encountered with the 3D fluoroscopic spin is within previously accepted limits. Prospective cohort study. Level I trauma center. All patients undergoing 3D fluoroscopy intraoperatively during pelvis (OTA/AO 61B,C) or acetabular (OTA/AO 62A-C) surgery between April 2021 and July 2021. Radiation dose at standardized locations around the operating room during the spin portion of the 3D fluoroscopy. Seventy-six 3D spins were performed on 69 patients during the study period. The average emitted radiation dose from the machine for the routine fluoroscopy portion of the case was 74.5 mGy. The average displayed radiation dose in the air for the spin portion of the case was 39.9 mGy, an average of 53.6% less radiation than the routine fluoroscopy portion. For the spin portion, the average radiation exposure seen by the patient was 3.42 mGy (centered on the patient) and the average maximal exposure in the room was 0.062 mGy. Minimal radiation was detected outside the operating room doors. The radiation exposure encountered by operating room personnel with 3D fluoroscopy appears to be within safe occupational limits. The marginal increase in radiation exposure during pelvic and acetabular surgery should not discourage the use of 3D imaging intraoperatively. Level IV, Case Series.

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.

Detailing organelle division and segregation in Plasmodium falciparum.

The malaria-causing parasite, P. falciparum, replicates through schizogony, a tightly orchestrated process where numerous daughter parasites are formed simultaneously. Proper division and segregation of one-per-cell organelles, like the mitochondrion and apicoplast, are essential, yet remain poorly understood. We developed a new reporter parasite line that allows visualization of the mitochondrion in blood and mosquito stages. Using high-resolution 3D imaging, we found that the mitochondrion orients in a cartwheel structure, prior to stepwise, non-geometric division during last-stage schizogony. Analysis of focused ion beam scanning electron microscopy data confirmed these mitochondrial division stages. Furthermore, these data allowed us to elucidate apicoplast division steps, highlighted its close association with the mitochondrion, and showed putative roles of the centriolar plaques in apicoplast segregation. These observations form the foundation for a new detailed mechanistic model of mitochondrial and apicoplast division and segregation during P. falciparum schizogony and pave the way for future studies into the proteins and protein complexes involved in organelle division and segregation.

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.

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.

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.

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.