micro-CT Datasets Research Articles

Reconstructing 3D histological structures using machine learning (artificial intelligence) algorithms.

Histomorphometry is currently the gold standard for bone microarchitectural examinations. This relies on two-dimensional (2D) sections to deduce the spatial properties of structures. Micromorphometric parameters are calculated from these sections based on the assumption of aplate-like 3D microarchitecture, resulting in the loss of 3D structure due to the destructive nature of classical histological processing. To overcome the limitation of histomorphometry and reconstruct the 3D architecture of bone core biopsy samples from 2D histological sections, bone core biopsy samples were decalcified and embedded in paraffin. Subsequently, 5 µm thick serial sections were stained with hematoxylin and eosin and scanned using a3DHISTECH PANNORAMIC 1000 Digital Slide Scanner (3DHISTECH, Budapest, Hungary). Amodified U‑Net architecture was trained to categorize tissues on the sections. LoFTR feature matching combined with affine transformations was employed to create the histologic reconstruction. Micromorphometric parameters were calculated using Bruker's CTAn software (v. 1.18.8.0, Bruker, Kontich, Belgium) for both histological and microCT datasets. Our method achieved an overall accuracy of 95.26% (95% confidence interval (CI): [94.15%, 96.37%]) with an F‑score of 0.9320 (95% CI: [0.9211, 0.9429]) averaged across all classes. Correlation coefficients between micromorphometric parameters measured on microCT imaging and histological reconstruction showed astrong linear relationship, with Spearman's ρ‑values of 0.777, 0.717, 0.705, 0.666, and 0.687 for bone volume/tissue volume (BV/TV), bone surface/TV, trabecular pattern factor, trabecular thickness, and trabecular separation, respectively. Bland-Altman and mountain plots indicated good agreement between the methods for BV/TV measurements. This method enables examination of tissue microarchitecture in 3D with an even higher resolution than microcomputed tomography (microCT), without losing information on cellularity. However, the limitation of this procedure is its destructive nature, which precludes subsequent mechanical testing of the sample or any further secondary measurements. Furthermore, the number of histological sections that can be created from asingle sample is limited.

Enhancement of early proximal caries annotations in radiographs: introducing the Diagnostic Insights for Radiographic Early-caries with micro-CT (ACTA-DIRECT) dataset

BackgroundProximal caries datasets for training artificial intelligence (AI) algorithms commonly include clinician-annotated radiographs. These conventional annotations are susceptible to observer variability, and early caries may be missed. Micro-computed tomography (CT), while not feasible in clinical applications, offers a more accurate imaging modality to support the creation of a reference-standard dataset for caries annotations. Herein, we present the Academic Center for Dentistry Amsterdam—Diagnostic Insights for Radiographic Early-caries with micro-CT (ACTA-DIRECT) dataset, which is the first dataset pairing dental radiographs and micro-CT scans to enable higher-quality annotations.MethodsThe ACTA-DIRECT dataset encompasses 179 paired micro-CT scans and radiographs of early proximal carious teeth, along with three types of annotations: conventional annotations on radiographs, micro-CT-assisted annotations on radiographs, and micro-CT annotations (reference standard). Three dentists independently annotated proximal caries on radiographs, both with and without micro-CT assistance, enabling determinations of interobserver agreement and diagnostic accuracy. To establish a reference standard, one dental radiologist annotated all caries on the related micro-CT scans.ResultsMicro-CT support improved interobserver agreement (Cohen’s Kappa), averaging 0.64 (95% confidence interval [CI]: 0.59–0.68) versus 0.46 (95% CI: 0.44–0.48) in its absence. Likewise, average sensitivity and specificity increased from 42% (95% CI: 34–51%) to 63% (95% CI: 54–71%) and from 92% (95% CI: 88–95%) to 95% (95% CI: 92–97%), respectively.ConclusionThe ACTA-DIRECT dataset offers high-quality images and annotations to support AI-based early caries diagnostics for training and validation. This study underscores the benefits of incorporating micro-CT scans in lesion assessments, providing enhanced precision and reliability.Graphical

Are Artificial Intelligence-Assisted Three-Dimensional Histological Reconstructions Reliable for the Assessment of Trabecular Microarchitecture?

Objectives: This study aimed to create a three-dimensional histological reconstruction through the AI-assisted classification of tissues and the alignment of serial sections. The secondary aim was to evaluate if the novel technique for histological reconstruction accurately replicated the trabecular microarchitecture of bone. This was performed by conducting micromorphometric measurements on the reconstruction and comparing the results obtained with those of microCT reconstructions. Methods: A bone biopsy sample was harvested upon re-entry following sinus floor augmentation. Following microCT scanning and histological processing, a modified version of the U-Net architecture was trained to categorize tissues on the sections. Detector-free local feature matching with transformers was used to create the histological reconstruction. The micromorphometric parameters were calculated using Bruker's CTAn software (version 1.18.8.0, Bruker, Kontich, Belgium) for both histological and microCT datasets. Results: Correlation coefficients calculated between the micromorphometric parameters measured on the microCT and histological reconstruction suggest a strong linear relationship between the two with p-values of 0.777, 0.717, 0.705, 0.666, and 0.687 for BV/TV, BS/TV, Tb.Pf Tb.Th, and Tb.Sp, respectively. Bland-Altman and mountain plots suggest good agreement between BV/TV measurements on the two reconstruction methods. Conclusions: This novel method for three-dimensional histological reconstruction provides researchers with a tool that enables the assessment of accurate trabecular microarchitecture and histological information simultaneously.

3D QUANTITATIVE CHARACTERIZATION OF THE HUMAN MENISCAL VASCULAR NETWORK USING X-RAY MICRO-COMPUTED TOMOGRAPHY

A comprehensive understanding of the self-repair abilities of menisci and their overall function in the knee joint requires three-dimensional information. However, previous investigations of the meniscal blood supply have been limited to two-dimensional imaging methods, which fail to accurately capture tissue complexity. In this study, micro-CT was used to analyse the 3D microvascular structure of the meniscus, providing a detailed visualization and precise quantification of the vascular network.A contrast agent (μAngiofil®) was injected directly into the femoral artery of cadaver legs to provide the proper contrast enhancement. First, the entire knee joint was analysed with micro-CT, then to increase the applicable resolution the lateral and medial menisci were excised and investigated with a maximum resolution of up to 4 μm. The resulting micro-CT datasets were analysed both qualitatively and quantitatively. Key parameters of the vascular network, such as vascular volume fraction, vessel radius, vessel length density, and tortuosity, were separately determined for the lateral and medial meniscus, and their four circumferential zones defined by Cooper.In accordance with previous literature, the quantitative micro-CT data confirm a decrease in vascular volume fraction along the meniscal zones. The highest concentration of blood vessels was measured in the meniscocapsular region 0, which is characterized by vascular segments with a significantly larger average radius. Furthermore, the highest vessel length density observed in zone 0 suggests a more rapid delivery of oxygen and nutrients compared to other regions. Vascular tortuosity was detected in all circumferential regions, indicating the occurrence of vascular remodelling in all tissue areas.In conclusion, micro-CT is a non-invasive imaging technique that allows for the visualization of the internal structure of an object in three dimensions. These advanced 3D vascular analyses have the potential to establish new surgical approaches that rely on the healing potential of specific areas of the meniscus.Acknowledgements: The authors acknowledge R. Hlushchuk, S. Halm, and O. Khoma from the University of Bern for their help with contrast agent perfusions.

Daily administration of parathyroid hormone slows the progression of basic multicellular units in the cortical bone of the rabbit distal tibia.

Basic Multicellular Units (BMUs) conduct bone remodeling, a critical process of tissue turnover which, if imbalanced, can lead to disease, including osteoporosis. Parathyroid hormone (PTH 1-34; Teriparatide) is an osteoanabolic treatment for osteoporosis; however, it elevates the rate of intra-cortical remodeling (activation frequency) leading, at least transiently, to increased porosity. The purpose of this study was to test the hypothesis that PTH not only increases the rate at which cortical BMUs are initiated but also increases their progression (Longitudinal Erosion Rate; LER). Two groups (n = 7 each) of six-month old female New Zealand white rabbits were both administered 30 μg/kg of PTH once daily for a period of two weeks to induce remodeling. Their distal right tibiae were then imaged in vivo by in-line phase contrast micro-CT at the Canadian Light Source synchrotron. Over the following two weeks the first group (PTH) received continued daily PTH while the second withdrawal group (PTHW) was administrated 0.9 % saline. At four weeks all animals were euthanized, their distal tibiae were imaged by conventional micro-CT ex vivo and histomorphometry was performed. Matching micro-CT datasets (in vivo and ex vivo) were co-registered in 3D and LER was measured from 612 BMUs. Counter to our hypothesis, mean LER was lower (p < 0.001) in the PTH group (30.19 ± 3.01 μm/day) versus the PTHW group (37.20 ± 2.77 μm/day). Despite the difference in LER, osteonal mineral apposition rate (On.MAR) did not differ between groups indicating the anabolic effect of PTH was sustained after withdrawal. The slowing of BMU progression by PTH warrants further investigation; slowed resorption combined with elevated bone formation rate, may play an important role in how PTH enhances coupling between resorption and formation within the BMU. Finally, the prolonged anabolic response following withdrawal may have utility in terms of optimizing clinical dosing regimens.

Comparison of Performance of Micro-Computed Tomography (Micro-CT) and Synchrotron Radiation CT in Assessing Coronary Stenosis Caused by Calcified Plaques in Coronary Artery Phantoms

Synchrotron-radiation-computed tomography (SRCT) allows more accurate calcified plaque and coronary stenosis assessment as a result of its superior spatial resolution; however, typical micro-computed tomography (micro-CT) systems have even higher resolution. The purpose of this study was to compare the performance of high-resolution micro-CT with SRCT in the assessment of calcified plaques and a previously published dataset of coronary stenosis assessment. This experimental study involved micro-CT scanning of three-dimensional printed coronary artery models with calcification in situ used in our previously published SRCT study on coronary stenosis assessment. Measurements of coronary stenosis utilizing both modalities were compared using a paired sample t-test. The degrees of stenosis measured on all but one micro-CT dataset were statistically significantly lower than the corresponding SRCT measurements reported in our previous paper (p &lt; 0.0005–0.05). This indicates that the superior spatial resolution of micro-CT was able to further reduce over-estimation of stenosis caused by extensive calcification of coronary arteries and, hence, false positive results. Our results showed that the high-resolution micro-CT used in this study outperformed the Australian Synchrotron SRCT in both calcified plaque and coronary stenosis assessment. These findings will become clinically important for cardiovascular event prediction and enable reclassification of individuals with low and intermediate risk into appropriate risk categories when the technical challenges of micro-CT in clinical practice such as the small field of view and demanding on image processing power are addressed.

A human head micro-CT dataset : skull and soft tissues

A human head micro-CT dataset : skull and soft tissues

ADJUST: a dictionary-based joint reconstruction and unmixing method for spectral tomography

Advances in multi-spectral detectors are causing a paradigm shift in x-ray computed tomography (CT). Spectral information acquired from these detectors can be used to extract volumetric material composition maps of the object of interest. If the materials and their spectral responses are known a priori, the image reconstruction step is rather straightforward. If they are not known, however, the maps as well as the responses need to be estimated jointly. A conventional workflow in spectral CT involves performing volume reconstruction followed by material decomposition, or vice versa. However, these methods inherently suffer from the ill-posedness of the joint reconstruction problem. To resolve this issue, we propose ‘A Dictionary-based Joint reconstruction and Unmixing method for Spectral Tomography’ (ADJUST). Our formulation relies on forming a dictionary of spectral signatures of materials common in CT and prior knowledge of the number of materials present in an object. In particular, we decompose the spectral volume linearly in terms of spatial material maps, a spectral dictionary, and the indicator of materials for the dictionary elements. We propose a memory-efficient accelerated alternating proximal gradient method to find an approximate solution to the resulting bi-convex problem. From numerical demonstrations on several synthetic phantoms, we observe that ADJUST performs exceedingly well compared to other state-of-the-art methods. Additionally, we address the robustness of ADJUST against limited and noisy measurement patterns. The demonstration of the proposed approach on a spectral micro-CT dataset shows its potential for real-world applications. Code is available at https://github.com/mzeegers/ADJUST.

Deep convolutional neural network for 3D mineral identification and liberation analysis

Mineral liberation analysis (MLA) is an automated mineral analysis system that identifies minerals in polished two-dimensional (2D) sections of drill or lump cores or particulate mineral matter. MLA allows a wide range of mineral characteristics to be investigated, including fragment size, mineral abundance, and liberation. To date, this analysis has been primarily limited to two-dimensional (2D) section information. In this study, we describe an MLA workflow that enables the extension of MLA into 3D via the utilization of 3D X-ray microcomputed tomography and convolutional neural network (CNN) guided by M4-Tornado micro-X-ray fluorescence (micro-XRF) data. With the combination of 3D greyscale micro-CT data with several 2D identified element mappings, the state-of-the-art CNN architecture called EfficientU-Net-b3 is trained and tested for multimineral segmentation on both an intact complex iron ore sample and the corresponding crushed fragments. Compared to traditional manual segmentation methods, where only greyscale thresholds are selected by humans, CNN-based segmentation takes the information from unbiased microXRF and extracts not only the greyscale values but also the texture features from the image. After the segmentation of the 3D micro-CT datasets, several mineral liberation analyses are performed in the 3D domain, as well as 2D slices that are uniformly selected from the 3D segmented fragments data. The results from 2D and 3D MLA demonstrate that the 2D analysis results are heterogeneous and significantly different (up to a 14 % difference in the association indicator matrix) from the 3D analysis results. The loss of mineral information from 2D could influence ore body characterization and the proposed mineral processing procedure. Overall, the proposed workflow provides a digital mineral framework for 3D MLA for future ore characterization applications.

Paraffin Fixed Human Trabecular Bone Specimens for Study of Osteoporosis

Examining and quantifying specific measures of trabecular bone to investigate the progression of osteoporosis presents a challenge. Specifically, the development of analytical and computational strategies to interpret computed tomography (CT) generated data sets requires gross anatomical specimens that closely approximate the intraosseous matrix of the marrow while maintaining the structural integrity of the trabecular network. Moreover, these specimens must be durable, easily transported, and stored at room temperature (e.g., do not require freeze/thaw cycles). Here we investigated if paraffin impregnated bone specimens maintained the trabecular network and bone density of prepared fresh‐frozen specimens used in imaging studies for osteoporosis. Standardized fresh‐frozen human distal tibia specimens (n = 12), from the institution’s Deeded Body Program, were scanned using multi‐row detector computed tomography (MDCT) and micro computed tomography (micro‐CT) imaging at different times in the preparation process to evaluate and quantify the preservation of microstructure and continuity of cortical and trabecular bone constituents. Once scanned, the specimens were placed in a series of acetone baths to remove water and marrow. With the de‐marrowing procedure complete, four specimens were rescanned using MDCT and micro‐CT. De‐marrowed specimens were then impregnated, under vacuum controlled conditions, with either a silicone polymer, gelatin matrix solution, or paraffin wax. After impregnation and curing, the specimens were re‐scanned. Analysis demonstrated a high correlation in the region of interest (ROI) for trabecular bone network area density from micro‐CT data sets of the preprocessed and de‐marrowed states (r = 0.99). Quantitatively, wax impregnated specimens demonstrated congruence in MDCT bone volume fraction (r = 0.92) and trabecular network area (r = 0.94) measures when the preprocessed and impregnated states were correlated. Results were more robust in the micro‐CT data in the bone volume fraction (r = 0.97) and the trabecular network area (r = 0.99). MDCT analysis at the (ROI) corresponding to 14‐16% length measured from the distal aspect of specimens demonstrated an increased estimate of bone mineral density (BMD) relative to scans acquired with the unmodified tissue (polymer +160 mg/cc; gelatin +58 mg/cc), whereas the paraffin wax condition slightly underestimated BMD (‐21 mg/cc). Of the impregnating procedures, paraffin wax provided the best qualitative specimens for handling, storage, and processing time. Together these data suggest that the trabecular structure is preserved through the de‐marrowing procedure and demonstrates that specimens can be transferred away from cold storage. Impregnating bone specimens with paraffin wax effectively provides a space occupying matrix that protects the delicate trabecular network without altering the ability to quantify important biomechanical measures in clinical CT as well as micro‐CT imaging for assessing bone integrity for the study of osteoporosis. This procedure creates specimens that are easily stored and transported, can be scanned across various imaging modalities, and effectively retain the trabecular micro‐structure.

Living in darkness: Exploring adaptation of Proteus anguinus in 3 dimensions by X-ray imaging.

BackgroundLightless caves can harbour a wide range of living organisms. Cave animals have evolved a set of morphological, physiological, and behavioural adaptations known as troglomorphisms, enabling their survival in the perpetual darkness, narrow temperature and humidity ranges, and nutrient scarcity of the subterranean environment. In this study, we focused on adaptations of skull shape and sensory systems in the blind cave salamander, Proteus anguinus, also known as olm or simply proteus—the largest cave tetrapod and the only European amphibian living exclusively in subterranean environments. This extraordinary amphibian compensates for the loss of sight by enhanced non-visual sensory systems including mechanoreceptors, electroreceptors, and chemoreceptors. We compared developmental stages of P. anguinus with Ambystoma mexicanum, also known as axolotl, to make an exemplary comparison between cave- and surface-dwelling paedomorphic salamanders.FindingsWe used contrast-enhanced X-ray computed microtomography for the 3D segmentation of the soft tissues in the head of P. anguinus and A. mexicanum. Sensory organs were visualized to elucidate how the animal is adapted to living in complete darkness. X-ray microCT datasets were provided along with 3D models for larval, juvenile, and adult specimens, showing the cartilage of the chondrocranium and the position, shape, and size of the brain, eyes, and olfactory epithelium.Conclusions P. anguinus still keeps some of its secrets. Our high-resolution X-ray microCT scans together with 3D models of the anatomical structures in the head may help to elucidate the nature and origin of the mechanisms behind its adaptations to the subterranean environment, which led to a series of troglomorphisms.

A high-throughput semi-automated bone segmentation workflow for murine hindpaw micro-CT datasets

IntroductionMicro-computed tomography (μCT) is a valuable imaging modality for longitudinal quantification of bone volumes to identify disease or treatment effects for a broad range of conditions that affect bone health. Complex structures, such as the hindpaw with up to 31 distinct bones in mice, have considerable analytic potential, but quantification is often limited to a single bone volume metric due to the intensive effort of manual segmentation. Herein, we introduce a high-throughput, user-friendly, and semi-automated method for segmentation of murine hindpaw μCT datasets. MethodsIn vivo μCT was performed on male (n = 4; 2–8-months) and female (n = 4; 2–5-months) C57BL/6 mice longitudinally each month. Additional 9.5-month-old male C57BL/6 hindpaws (n = 6 hindpaws) were imaged by ex vivo μCT to investigate the effects of resolution and integration time on analysis outcomes. The DICOMs were exported to Amira software for the watershed-based segmentation, and watershed markers were generated automatically at approximately 80% accuracy before user correction. The semi-automated segmentation method utilizes the original data, binary mask, and bone-specific markers that expand to the full volume of the bone using watershed algorithms. ResultsCompared to the conventional manual segmentation using Scanco software, the semi-automated approach produced similar raw bone volumes. The semi-automated segmentation also demonstrated a significant reduction in segmentation time for both experienced and novice users compared to standard manual segmentation. ICCs between experienced and novice users were >0.9 (excellent reliability) for all but 4 bones. DiscussionThe described semi-automated segmentation approach provides remarkable reliability and throughput advantages. Adoption of the semi-automated segmentation approach will provide standardization and reliability of bone volume measures across experienced and novice users and between institutions. The application of this model provides a considerable strategic advantage to accelerate various research opportunities in pre-clinical bone and joint analysis towards clinical translation.

Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Autoencoders.

Objective and Impact Statement. We adopt a deep learning model for bone osteolysis prediction on computed tomography (CT) images of murine breast cancer bone metastases. Given the bone CT scans at previous time steps, the model incorporates the bone-cancer interactions learned from the sequential images and generates future CT images. Its ability of predicting the development of bone lesions in cancer-invading bones can assist in assessing the risk of impending fractures and choosing proper treatments in breast cancer bone metastasis. Introduction. Breast cancer often metastasizes to bone, causes osteolytic lesions, and results in skeletal-related events (SREs) including severe pain and even fatal fractures. Although current imaging techniques can detect macroscopic bone lesions, predicting the occurrence and progression of bone lesions remains a challenge. Methods. We adopt a temporal variational autoencoder (T-VAE) model that utilizes a combination of variational autoencoders and long short-term memory networks to predict bone lesion emergence on our micro-CT dataset containing sequential images of murine tibiae. Given the CT scans of murine tibiae at early weeks, our model can learn the distribution of their future states from data. Results. We test our model against other deep learning-based prediction models on the bone lesion progression prediction task. Our model produces much more accurate predictions than existing models under various evaluation metrics. Conclusion. We develop a deep learning framework that can accurately predict and visualize the progression of osteolytic bone lesions. It will assist in planning and evaluating treatment strategies to prevent SREs in breast cancer patients.

Micro-CT image gallery visually presenting the effects of ocean warming and acidification on marine gastropod shells.

BackgroundDigitisation of specimens (e.g. zoological, botanical) can provide access to advanced morphological and anatomical information and promote new research opportunities. The micro-CT technology may support the development of "virtual museums" or "virtual laboratories" where digital 3D imaging data are shared widely and freely. There is currently a lack of universal standards concerning the publication and curation of micro-CT datasets.New informationThe aim of the current project was to create a virtual gallery with micro-CT scans of individuals of the marine gastropod Hexaplextrunculus, which were maintained under a combination of increased temperature and low pH conditions, thus simulating future climate change scenarios. The 3D volume-rendering models created were used to visualise the structure properties of the gastropods shells. Finally, the 3D analysis performed on the micro-CT scans was used to investigate potential changes in the shell properties of the gastropods. The derived micro-CT 3D images were annotated with detailed metadata and can be interactively displayed and manipulated using online tools through the micro-CT virtual laboratory, which was developed under the LifeWatchGreece Research Infrastructure for the dissemination of virtual image galleries collection supporting the principles of FAIR data.

Integration of Multiscale Imaging of Nanoscale Pore Microstructures in Gas Shales

Quantification of the microstructures of shales is difficult due to their complexity which extends across many orders of magnitude of scale. Nevertheless, shale microstructures are extremely important, not only as shale gas resources but also as cap rocks in CCS resources, in geothermal reservoirs, and as a host to the long-term storage of radioactive materials. In this work, we have performed ultrahigh-resolution CT imaging (nano-CT), mercury injection porosimetry (MIP), and nitrogen adsorption experiments on a sample of gas shale for which we already have focused ion beam scanning electron microscopy (FIB-SEM) and high-resolution CT (micro-CT) data sets. The combination of these data sets has allowed us to examine the microstructure of the shale in unprecedented depth across a wide range of scales (from about 20 nm to 0.5 mm). Overall, the sample shows a porosity of 0.67 ± 0.009% from the nano-CT data, 0.0235 ± 0.003% from nitrogen adsorption, and 0.60 ± 0.07% from MIP, which compare with 0.10 ± 0.01%, 0.52 ± 0.05%, and 0.94 ± 0.09% from three FIB-SEM measurements and 0.06 ± 0.008% from one micro-CT measurement. The data vary due to the different scales at which each technique interrogates the rock and whether the pores are openly accessible (especially in the case of the nitrogen adsorption value). The measured kerogen fraction is 32.4 ± 1.45% from nano-CT compared with 34.8 ± 1.74%, 38.2 ± 1.91%, 41.4 ± 2.07%, and 44.5 ± 2.22% for three FIB-SEM and one micro-CT measurement. The pore size imaged by nano-CT ranged between 100 and 5000 nm, while the corresponding ranges were between 3 and 2000 nm for MIP analysis and between 2 and 90 nm for N2 adsorption. The distribution of pore aspect ratio and scale-invariant pore surface area to volume ratio (σ) as well as the calculated permeability shows the sample to have a high shale gas potential. Aspect ratios indicate that most of the pores that contribute significantly to pore volume are oblate, which is confirmed by the range of σ (3–13). Oblate pores have greater potential for interacting with other pores compared to equant and needle-shaped prolate pores as well optimizing surface area for gas to desorb from the kerogen into the pores. Permeability essays provide 2.61 ± 0.42 nD from the nano-CT data, 2.65 ± 0.45 nD from MIP, and (5.07 ± 0.02) × 10–4 nD from nitrogen adsorption, which are consistent with expectations for generic gas shales (i.e., tens of nD) and the measurements made previously on the same sample using FIB-SEM and micro-CT imaging techniques.

Elastic transformation of histological slices allows precise co-registration with microCT data sets for a refined virtual histology approach

Although X-ray based 3D virtual histology is an emerging tool for the analysis of biological tissue, it falls short in terms of specificity when compared to conventional histology. Thus, the aim was to establish a novel approach that combines 3D information provided by microCT with high specificity that only (immuno-)histochemistry can offer. For this purpose, we developed a software frontend, which utilises an elastic transformation technique to accurately co-register various histological and immunohistochemical stainings with free propagation phase contrast synchrotron radiation microCT. We demonstrate that the precision of the overlay of both imaging modalities is significantly improved by performing our elastic registration workflow, as evidenced by calculation of the displacement index. To illustrate the need for an elastic co-registration approach we examined specimens from a mouse model of breast cancer with injected metal-based nanoparticles. Using the elastic transformation pipeline, we were able to co-localise the nanoparticles to specifically stained cells or tissue structures into their three-dimensional anatomical context. Additionally, we performed a semi-automated tissue structure and cell classification. This workflow provides new insights on histopathological analysis by combining CT specific three-dimensional information with cell/tissue specific information provided by classical histology.

Fast and accurate lacunarity calculation for large 3D micro-CT datasets

Microcomputed-tomography (micro-CT) is a 3D imaging method capable of revealing the complete inner structure of materials. Besides imaging, micro-CT also provides quantitative information about numerous structural features including lacunarity, which describes the heterogeneity of samples quantitatively. Theoretically, lacunarity is easily calculated using the gliding box method. However, when implemented in 3D, the computational costs of this method increase enormously, thus preventing its widespread use for large micro-CT datasets. Here we suggest a faster alternative method, based on the fixed-grid algorithm, which offers a viable alternative and renders 3D lacunarity calculations on micro-CT data feasible. Since a possible shortcoming of this alternative is that its reduced data could result in an inferior description of the real spatial heterogeneity of the structures, the two methods are compared concerning the accuracy, computational time, and applicability in materials science. The calculations are carried out on real 3D micro-CT datasets. Our implementation of the fixed-grid method can approximate gliding box lacunarity values rapidly and accurately, especially for large datasets of homogeneous structures. Therefore, we propose adding the fixed-grid method lacunarity calculation to the routine micro-CT analysis toolbox. Our image acquisition platform-independent software (Lac3D) to carry out this calculation is made freely accessible here.

Synchrotron X-ray micro-CT as a validation dataset for diffusion MRI in whole mouse brain.

To introduce synchrotron X-ray microcomputed tomography (microCT) and demonstrate its use as a natively isotropic, nondestructive, 3D validation modality for diffusion MRI in whole, fixed mouse brain. Postmortem diffusion MRI and microCT data were acquired of the same whole mouse brain. Diffusion data were processed using constrained spherical deconvolution. Synchrotron data were acquired at an isotropic voxel size of 1.17 μm. Structure tensor analysis was used to calculate fiber orientation distribution functions from the microCT data. A pipeline was developed to spatially register the 2 datasets in order to perform qualitative comparisons of fiber geometries represented by fiber orientation distribution functions. Fiber orientations from both modalities were used to perform whole-brain deterministic tractography to demonstrate validation of long-range white matter pathways. Fiber orientation distribution functions were able to be extracted throughout the entire microCT dataset, with spatial registration to diffusion MRI simplified due to the whole brain extent of the microCT data. Fiber orientations and tract pathways showed good agreement between modalities. Synchrotron microCT is a potentially valuable new tool for future multi-scale diffusion MRI validation studies, providing comparable value to optical histology validation methods while addressing some key limitations in data acquisition and ease of processing.

Quantitative Analysis of the Anatomy of Mesial Roots of Mandibular First Molars with Vertucci Type I Root Canal Configuration by Means of Micro-Computed Tomography

Aim: This study aimed to evaluate root canal morphology of Vertucci type I root canal configuration detected in mesial roots of mandibular first molar teeth using micro-computed tomography (Micro-CT).&#x0D; Methodology: Micro-CT datasets of 269 specimens were evaluated for the detection of specimens with Vertucci type I root canal configuration for further analysis. Major and minor diameter, area, perimeter and roundness of root canal cross-sections at the beginning and midline of each root thirds were measured and recorded. The presence of apical delta, accessory canals, and deviation of major apical foramen from anatomical apex were investigated. Data was analyzed using descriptive analysis, ANOVA-Tukey and Kruskal-Wallis H-Wilcoxon signed-rank tests.&#x0D; Results: Vertucci type I canal configuration was present in the 4.46% of the specimens. The specimens often showed accessory root canals (50%) and apical deltas (41,6%). From the visual analysis of the 3D and 2D images, significant differences were detected among coronal, middle and apical thirds of the specimens regarding major and minor diameter, area, perimeter and roundness (P &lt; 0.05).&#x0D; Conclusion: Three- and two-dimensional analyses of mesial roots with Vertucci type I canal configuration indicated that this type of configuration presents large buccolingual diameters with long oval shape and has isthmus at coronal and middle thirds. At the apical third the root canals often terminate in an oval cross sectional shape.&#x0D; &#x0D; How to cite this article: Keles A, Keskin C. Quantitative analysis of the anatomy of mesial roots of mandibular first molars with Vertucci type I root canal configuration by means of micro-computed tomography. Int Dent Res 2020;10(1):17-21. https://doi.org/10.5577/intdentres.2020.vol10.no1.4&#x0D; &#x0D; Linguistic Revision: The English in this manuscript has been checked by at least two professional editors, both native speakers of English.

Liver Backscatter and the Hepatic Vasculature’s Autocorrelation Function

Ultrasound imaging of the liver is an everyday, worldwide clinical tool. The echoes are produced by inhomogeneities within the interrogated tissue, but what are the mathematical properties of these scatterers? In theory, the spatial correlation function and the backscatter coefficient are linked by a Fourier transform relationship, however direct measures of these are relatively rare. Under the hypothesis that the fractal branching vasculature and fluid channels are the predominant source of scattering in normal tissues, we compare theory and experimental measures of the autocorrelation function, the frequency dependence of scattering, and fractal dimension estimates from high contrast 3D micro-CT data sets of rat livers. The results demonstrate a fractal dimension of approximately 2.2 with corresponding power law estimates of autocorrelation and ultrasound scattering. These results support a general framework for the analysis of ultrasound scattering from soft tissues.