High-resolution micro-CT Imaging Research Articles

Upscaling relative permeability and capillary pressure from digital core analysis in Otway formation: Considering the order and size effects of facies

Digital Core Analysis (DCA) has emerged as a crucial instrument in reservoir characterization in recent times. With the advent of high-resolution micro-CT imaging, it is now possible to visualize the three-dimensional microstructures of in-situ pores and flow patterns within rocks. DCA offers several notable benefits over traditional techniques, such as a higher density of measurements, faster processing times, and the preservation of rock samples. It also demonstrates considerable flexibility with challenging core conditions and can derive numerous parameters from each individual sample. The objective of this work is to utilise DCA data from Otway formation to enhance reservoir characterisation and CO2 plume forecast. However, a critical challenge is that these data are obtained at a micro-scale, necessitating an upscaling of DCA properties from the micro-scale to core scale, and subsequently to the site and field scale, for compatibility with reservoir simulation and field studies. This study proposes an implicit and iterative method to upscale DCA properties and explore the impact of facies order and size on the upscaling process of relative permeability and capillary pressure. This method is implemented in an open-source simulator known as the Matlab Reservoir Simulation Toolbox (MRST). The findings indicate that relative permeability and capillary pressure outcomes from upscaling are influenced by order and portion of facies. This highlights the importance of considering both the size and arrangement of facies during the upscaling process, given their potential impact on fluid dynamics and the accuracy of reservoir simulation results.

High-resolution hybrid micro-CT imaging pipeline for mouse brain region segmentation and volumetric morphometry.

Brain region segmentation and morphometry in humanized apolipoprotein E (APOE) mouse models with a human NOS2 background (HN) contribute to Alzheimer's disease (AD) research by demonstrating how various risk factors affect the brain. Photon-counting detector (PCD) micro-CT provides faster scan times than MRI, with superior contrast and spatial resolution to energy-integrating detector (EID) micro-CT. This paper presents a pipeline for mouse brain imaging, segmentation, and morphometry from PCD micro-CT. We used brains of 26 mice from 3 genotypes (APOE22HN, APOE33HN, APOE44HN). The pipeline included PCD and EID micro-CT scanning, hybrid (PCD and EID) iterative reconstruction, and brain region segmentation using the Small Animal Multivariate Brain Analysis (SAMBA) tool. We applied SAMBA to transfer brain region labels from our new PCD CT atlas to individual PCD brains via diffeomorphic registration. Region-based and voxel-based analyses were used for comparisons by genotype and sex. Together, PCD and EID scanning take ~5 hours to produce images with a voxel size of 22 μm, which is faster than MRI protocols for mouse brain morphometry with voxel size above 40 μm. Hybrid iterative reconstruction generates PCD images with minimal artifacts and higher spatial resolution and contrast than EID images. Our PCD atlas is qualitatively and quantitatively similar to the prior MRI atlas and successfully transfers labels to PCD brains in SAMBA. Male and female mice had significant volume differences in 26 regions, including parts of the entorhinal cortex and cingulate cortex. APOE22HN brains were larger than APOE44HN brains in clusters from the hippocampus, a region where atrophy is associated with AD. This work establishes a pipeline for mouse brain analysis using PCD CT, from staining to imaging and labeling brain images. Our results validate the effectiveness of the approach, setting a foundation for research on AD mouse models while reducing scanning durations.

BounTI (boundary-preserving threshold iteration): A user-friendly tool for automatic hard tissue segmentation.

X-ray Computed Tomography (CT) images are widely used in various fields of natural, physical, and biological sciences. 3D reconstruction of the images involves segmentation of the structures of interest. Manual segmentation has been widely used in the field of biological sciences for complex structures composed of several sub-parts and can be a time-consuming process. Many tools have been developed to automate the segmentation process, all with various limitations and advantages, however, multipart segmentation remains a largely manual process. The aim of this study was to develop an open-access and user-friendly tool for the automatic segmentation of calcified tissues, specifically focusing on craniofacial bones. Here we describe BounTI, a novel segmentation algorithm which preserves boundaries between separate segments through iterative thresholding. This study outlines the working principles behind this algorithm, investigates the effect of several input parameters on its outcome, and then tests its versatility on CT images of the craniofacial system from different species (e.g. a snake, a lizard, an amphibian, a mouse and a human skull) with various scan qualities. The case studies demonstrate that this algorithm can be effectively used to segment the craniofacial system of a range of species automatically. High-resolution microCT images resulted in more accurate boundary-preserved segmentation, nonetheless significantly lower-quality clinical images could still be segmented using the proposed algorithm. Methods for manual intervention are included in this tool when the scan quality is insufficient to achieve the desired segmentation results. While the focus here was on the craniofacial system, BounTI can be used to automatically segment any hard tissue. The tool presented here is available as an Avizo/Amira add-on, a stand-alone Windows executable, and a Python library. We believe this accessible and user-friendly segmentation tool can benefit the wider anatomical community.

Postnatal changes in thyroid cartilage shape and cartilage matrix composition are not synchronized in Mus musculus.

The study was conducted to quantify laryngeal cartilage matrix composition and to investigate its relationship with cartilage shape in a mouse model. A sample of 30 mice (CD-1 mouse, Mus musculus) from five age groups (postnatal Days 2, 21, 90, 365, and 720) were used. Three-dimensional mouse laryngeal thyroid cartilage reconstructions were generated from contrast-enhanced micro-computed tomography (CT) image stacks. Cartilage matrix composition was estimated as Hounsfield units (HU). HU were determined by overlaying 3D reconstructions as masks on micro-CT image stacks and then measuring the attenuation. Cartilage shape was quantified with landmarks placed on the surface of the thyroid cartilage. Shape differences between the five age groups were analyzed using geometric morphometrics and multiparametric analysis of landmarks. The relationship between HU and shape was investigated with correlational analyses. Among five age groups, HU became higher in older animals. The shape of the thyroid cartilage changes with age throughout the entire life of a mouse. The changes in shape were not synchronized with changes in cartilage matrix composition. The thyroid cartilage of young and old M. musculus larynx showed a homogenous mineralization pattern. High-resolution contrast-enhanced micro-CT imaging makes the mouse larynx accessible for analysis of genetic and environmental factors affecting shape and matrix composition.

SUBCHONDRAL MICROCHANNEL NETWORK CHANGES IN EARLY OSTEOARTHRITIS

Articular cartilage (AC) and subchondral bone (SB) are intimately intertwined, forming a complex unit called the AC-SB interface. Our recent studies have shown that cartilage and bone marrow are connected by a three-dimensional network of microchannels (i.e. cartilage-bone marrow microchannel connector; CMMC), which differ microarchitecturally in number, size and morphology depending on the maturation stage of the bone and the region of the joint. However, the pathological significance of CMMC is largely unknown. Here, we quantitatively assessed how CMMC microarchitecture relates to cartilage condition and regional differences in early idiopathic osteoarthritis (OA).Two groups of cadaveric female human femoral heads (intact cartilage vs early cartilage lesions) were identified and biopsy-based high-resolution micro-CT imaging was used. Subchondral bone (SB) thickness, CMMC number, maximum and minimum CMMC size, and CMMC morphology were quantified and compared between the two groups. The effect of joint region and cartilage condition on each dependent variable was examined.The number and morphology of CMMCs were influenced by the region of the joint, but not by the cartilage condition. On the other hand, the minimum and maximum CMMC size was modified by both joint location and cartilage condition. The smallest CMMCs were consistently found in the load bearing region (LBR) of the joint. Compared to healthy subjects, the size of the microchannels was increased in early OA, most notably in the non-load bearing region (NLBR) and the peripheral rim (PR) of the femoral head. In addition, subchondral bone thinning was observed in early OA as a localized event associated with areas of partial chondral defect.Our data suggest an enlargement of the SB microchannel network and a collective structural deterioration of the SB in early idiopathic OA.

A comparative analysis of super-resolution techniques for enhancing micro-CT images of carbonate rocks

High-resolution digital rock micro-CT images captured from a wide field of view are essential for various geosystem engineering and geoscience applications. However, the resolution of these images is often constrained by the capabilities of scanners. To overcome this limitation and achieve superior image quality, advanced deep learning techniques have been used. This study compares four different super-resolution techniques, including super-resolution convolutional neural network (SRCNN), efficient sub-pixel convolutional neural networks (ESPCN), enhanced deep residual neural networks (EDRN), and super-resolution generative adversarial networks (SRGAN) to enhance the resolution of micro-CT images obtained from heterogeneous porous media. Our investigation employs a dataset consisting of 5000 micro-CT images acquired from a highly heterogeneous carbonate rock. The performance of each algorithm is evaluated based on its accuracy to reconstruct the pore geometry and connectivity, grain-pore edge sharpness, and preservation of petrophysical properties, such as porosity. Our findings indicate that EDRN outperforms other techniques in terms of the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index, increased by nearly 4 dB and 17%, respectively, compared to bicubic interpolation. Furthermore, SRGAN exhibits superior performance compared to other techniques in terms of the learned perceptual image patch similarity (LPIPS) index and porosity preservation error. SRGAN shows a nearly 30% reduction in LPIPS compared to bicubic interpolation. Our results provide deeper insights into the practical applications of these techniques in the domain of porous media characterizations, facilitating the selection of optimal super-resolution CNN-based methodologies.

Root Cause Analysis of An Inverse Relationship Between The Ice Nucleation Temperature, Process Efficiency And Quality of A Lyophilized Product

The aim of this study was to probe an unexpected relationship between the ice nucleation temperature (TIN), process efficiency and product attributes in a controlled ice nucleation (CIN) lyophilization process. An amorphous product was lyophilized with (CIN-5 °C, CIN-7 °C or CIN-10 °C) or without (NOCIN) control of ice nucleation. Process parameters and product attributes were monitored and compared using a series of advanced in-line and off-line process analytical technology (PAT) tools. Unexpectedly, an indirect relationship was observed between TIN and primary drying efficiency for the CIN processes. Further, the CIN-5 °C process was associated with higher product resistance to mass flow than corresponding CIN-7 °C and CIN-10 °C processes. Surprisingly, the air voids in some NOCIN products were larger than CIN-5 °C products but comparable to CIN-7 °C. Heat flux analysis revealed an indirect relationship between TIN and the minimum hold time required to complete solidification. The heat flux analysis also revealed all products underwent complete solidification prior to primary drying. The order of homogeneity in water activity of the products was CIN-5 °C ≥NOCIN>CIN-7 °C. The higher homogeneity in water activity of CIN-5 °C than corresponding CIN-7 °C processes indicated that the lower process efficiency of CIN-5 °C could not be attributed to unsuccessful induction of ice nucleation during CIN-5 °C. High resolution micro-CT imaging and Artificial Intelligence Image analysis revealed cake wall deformation in CIN-7 °C and NOCIN products but not in CIN-5 °C. In addition, NOCIN products had bimodal distribution in air voids with median size range of 4–5 µm and 151.9–309 µm, respectively, hence the lower process efficiency of NOCIN despite the higher D90. Thus, the observed relationship between TIN and process efficiency may be attributed to microstructural changes post freezing. This hypothesis was corroborated by visible macroscopic cake collapse in NOCIN products but not in CIN products after lyophilization at a higher shelf temperature. In conclusion, the advantages of controlling the ice nucleation temperature of a lyophilization process may only be attained through a robust process design that takes into consideration the primary and secondary drying process parameters. Further, combined use of advanced in-line and off-line PAT tools for process and product characterization may hasten the at scale adoption of advance techniques such as CIN.

Quantitative analysis of small coronary artery calcium detectability with an accurate simulation and validation on a clinical CT scanner.

The absence of coronary artery calcium (CAC) measured via CT is associated with very favorable prognosis, and current guidelines recommend low-density lipoprotein cholesterol (LDL-c) lowering therapy for individuals with any CAC. This motivates early detection of small granules of CAC; however, calcium scan sensitivity for detecting very low levels of calcium has not been quantified. In this work, the size limit of detectability of small calcium hydroxyapatite (CaHA) granules with clinical CAC scanning was assessed using validated simulations. CT projections of digital 3D mathematical phantoms containing small CaHA granules were simulated analytically; images were reconstructed using a filter designed to reproduce the point spread function of a specific commercial scanner, and a relationship of HU number versus diameter was derived. These simulation results were validated with experimental measurements of HU versus diameter from phantoms containing small granules of CaHA on a GE Revolution CT scanner in the clinic; ground truth measurements of the CaHA granule diameters were obtained using a Zeiss Xradia 510 Versa high-resolution 3D micro-CT imaging system. Using experimental measurements on the clinical CT scanner, detectability was quantified with a detectability index (d') using a non-prewhitened matched filter. The effect of changes to reconstruction slice thickness and reconstruction kernel on granule detectability was evaluated. Under typical clinical calcium scanning and reconstruction conditions, the minimum detectable diameter of a simulated spherical calcium granule with a clinically relevant CaHA density was 0.76mm. The minimum detectable volume was 2.4 times smaller on images reconstructed at a slice thickness of 0.625mm compared to 2.5mm. The detectability index d' increased by a factor of 1.7 when images were reconstructed with 0.625mm slices compared to 2.5mm slices. d' did not change when images were reconstructed with the high-resolution BONE filter compared to the less sharp STANDARD resolution filter on the GE Revolution CT. We have quantified detectability versus size of small calcium granules at the resolution limit of a widely available clinical CT scanner. Detectability increased significantly with reduced slice thickness and did not change with a sharper reconstruction kernel. The simulation can be used to calculate the trade-off between dose and CAC detectability.

STRUCTURAL DEFECTS AND VARIABLE POROSITY IN 3D-PRINTED CUPS FROM SIX MANUFACTURERS

3D printing is rapidly being adopted by manufacturers to produce orthopaedic implants. There is a risk however of structural defects which may impact mechanical integrity. There are also no established standards to guide the design of bone-facing porous structures, meaning that manufacturers may employ different approaches to this. Characterisation of these variables in final-production implants will help understanding of the impact of these on their clinical performance.We analysed 12 unused, final-production custom-made 3D printed acetabular cups that had been produced by 6 orthopaedic manufacturers. We performed high resolution micro-CT imaging of each cup to characterise the morphometric features of the porous layers: (1) the level of porosity, (2) pore size, (3) thickness of porous struts and (4) the depth of the porous layers. We then examined the internal cup structures to identify the presence of any defects and to characterise: (1) their total number, (2) volume, (3) sphericity, (4) size and (5) location.There was a variability between designs in the level of porosity (34% to 85%), pore size (0.74 to 1.87mm), strut thickness (0.28 to 0.65mm), and porous layer depth (0.57 to 11.51mm). One manufacturer printed different porous structures between the cup body and flanges; another manufacturer printed two differing porous regions within the cup body.5 cups contained a median (range) of 90 (58–101) defects. The median defect volume was 5.17 (1.05–17.33) mm3. The median defect sphericity and size were 0.47 (0.19–0.65) and 0.64 (0.27–8.82) mm respectively. The defects were predominantly located adjacent to screw holes, within flanges and at the transition between the flange and main cup body; these were between 0.17 and 4.66mm from the cup surfaces.There is a wide variability between manufacturers in the porous titanium structures they 3D print. The size, shape and location of the structural defects identified are such that there may be an increased risk of crack initiation from them, potentially leading to a fracture. Regulators, surgeons, and manufacturers should be aware of this variability in final print quality.

Numerical investigation of progressive damage and associated seismicity on a laboratory fault

Understanding rock shear failure behavior is crucial to gain insights into slip-related geohazards such as rock avalanches, landslides, and earthquakes. However, descriptions of the progressive damage on the shear surface are still incomplete or ambiguous. In this study, we use the hybrid finite-discrete element method (FDEM) to simulate a shear experiment and obtain a detailed comprehension of shear induced progressive damage and the associated seismic activity. We built a laboratory fault model from high resolution surface scans and micro-CT imaging. Our results show that under quasi-static shear loading, the fault surface experiences local dynamic seismic activities. We found that the seismic activity is related to the stress concentration on interlocking asperities. This interlocking behavior (i) causes stress concentration at the region of contact that could reach the compressive strength, and (ii) produces tensile stress up to the tensile strength in the region adjacent to the contact area. Thus, different failure mechanisms and damage patterns including crushing and sub-vertical fracturing are observed on the rough surface. Asperity failure creates rapid local slips resulting in significant stress perturbations that alter the overall stress condition and may trigger the slip of adjacent critically stressed asperities. We found that the spatial distribution of the damaged asperities and the seismic activity is highly heterogeneous; regions with intense asperity interactions formed gouge material, while others exhibit minimal to no damage. These results emphasize the important role of surface roughness in controlling the overall shear behavior and the local dynamic seismic activities on faults.

Processing of micro-CT images of granodiorite rock samples using convolutional neural networks (CNN), Part II: Semantic segmentation using a 2.5D CNN

X-ray computed tomography (XCT) is routinely used in geosciences for the purpose of rock characterisation. High-quality micro-CT images are successfully used for fracture characterisation, as well as analysis of grains and pores. In contrast, the use of XCT for mineral identification is uncommon and often ineffective. Implementation of micro-CT imaging techniques for mineral identification is affected by the accuracy and precision of the image segmentation results. Conventional segmentation methods such as thresholding, watershed, and active contouring are user-biased and do not provide the robust distinction between various heavy accessory minerals in granite rocks. Heavy ore minerals such as pyrite, chalcopyrite, molybdenite, and ilmenite are readily recognised in grey-scale micro-CT images because of their high attenuation coefficient, but further differentiation between these minerals using only traditional segmentation methods is challenging. Conversely, deep convolutional neural networks (CNNs) are fully self-trained, and they have demonstrated accurate semantic segmentation results for rock images. However, the application of CNN semantic segmentation for igneous rocks is not well documented. In this research, the U-Net 2.5D CNN was deployed to train the neural network on a combination of high-resolution micro-CT and mineral liberation analysis (MLA) images to identify different accessory mineral regions of interest (ROIs). The image segmentation results were assessed using MLA and SEM data, and the accuracy of segmentation was found to be greater than 97%. The methodology developed in this study can be extended to map the mineralogy of granite samples unseen by the CNN to further validate the robustness of the approach.

Towards Multiscale Digital Rocks: Application of a Sub-Resolution Production Model to a multiscale Sandstone

Many digital rock methodologies use a direct simulation approach, where only resolved pores are accounted for. This approach limits the types of rocks that can be analyzed, excluding some types of carbonates, unconventionals, and complex sandstones from the digital rock analysis. This is due to the challenge for single scale imaging to capture the full range of relevant pore sizes present in multiscale rocks. In this paper, a physical model is presented, within the context of an established direct simulation approach, to predict the production of hydrocarbons including the contribution of sub-resolution pores. The direct simulation component of the model employs a multiphase lattice Boltzmann method to simulate multiphase fluid flow displacement in resolved pores. In the production model, the amount of hydrocarbons present in the sub-resolution pores is identified and a physical description of the production behavior is provided. This allows a relative permeability curve to be predicted for rocks where mobile hydrocarbons are present in pores smaller than the image resolution. This simplified model for the oil movement in the unresolved pore space is based on a physical interpretation of different regions marked by simulation resolution limits in a USBM wettability test curve. The proposed methodology is applied to high-resolution microCT images of a sandstone that contains pores at multiple scales, some resolved and some not resolved. To allow for benchmarking, experimental routine and special core analysis data was also obtained. Good agreement to experimental results is observed, specifically in absolute and relative permeability. The presented multiscale model has the potential to extend the classes of reservoir rocks eligible for digital rock analysis and paves the way for further advancements in the modelling of multiscale rocks, particularly unconventionals and carbonates.

Morphometric analysis of patient-specific 3D-printed acetabular cups: a comparative study of commercially available implants from 6 manufacturers

Background3D printed patient-specific titanium acetabular cups are used to treat patients with massive acetabular defects. These have highly porous surfaces, with the design intent of enhancing bony fixation. Our aim was to characterise these porous structures in commercially available designs.MethodsWe obtained 12 final-production, patient-specific 3D printed acetabular cups that had been produced by 6 manufacturers. High resolution micro-CT imaging was used to characterise morphometric features of their porous structures: (1) strut thickness, 2) the depth of the porous layer, (3) pore size and (4) the level of porosity. Additionally, we computed the surface area of each component to quantify how much titanium may be in contact with patient tissue. Statistical comparisons were made between the designs.ResultsWe found a variability between designs in relation to the thickness of the struts (0.28 to 0.65 mm), how deep the porous layers are (0.57 to 11.51 mm), the pore size (0.74 to 1.87 mm) and the level of porosity (34 to 85%). One manufacturer printed structures with different porosities between the body and flange; another manufacturer had two differing porous regions within the body of the cups. The cups had a median (range) surface area of 756.5 mm2 (348 – 1724).ConclusionsThere is a wide variability between manufacturers in the porous titanium structures they 3D print. We do not currently know whether there is an optimal porosity and how this variability will impact clinically on the integrity of bony fixation; this will become clearer as post market surveillance data is generated.

High-resolution micro-CT for 3D infarct characterization and segmentation in mice stroke models

Characterization of brain infarct lesions in rodent models of stroke is crucial to assess stroke pathophysiology and therapy outcome. Until recently, the analysis of brain lesions was performed using two techniques: (1) histological methods, such as TTC (Triphenyltetrazolium chloride), a time-consuming and inaccurate process; or (2) MRI imaging, a faster, 3D imaging method, that comes at a high cost. In the last decade, high-resolution micro-CT for 3D sample analysis turned into a simple, fast, and cheaper solution. Here, we successfully describe the application of brain contrasting agents (Osmium tetroxide and inorganic iodine) for high-resolution micro-CT imaging for fine location and quantification of ischemic lesion and edema in mouse preclinical stroke models. We used the intraluminal transient MCAO (Middle Cerebral Artery Occlusion) mouse stroke model to identify and quantify ischemic lesion and edema, and segment core and penumbra regions at different time points after ischemia, by manual and automatic methods. In the transient-ischemic-attack (TIA) mouse model, we can quantify striatal myelinated fibers degeneration. Of note, whole brain 3D reconstructions allow brain atlas co-registration, to identify the affected brain areas, and correlate them with functional impairment. This methodology proves to be a breakthrough in the field, by providing a precise and detailed assessment of stroke outcomes in preclinical animal studies.

Changes of the subchondral bone microchannel network in early osteoarthritis

We have identified a 3D network of subchondral microchannels that connects the deep zone of cartilage to the bone marrow (i.e., cartilage-bone marrow microchannel connectors; CMMC). However, the pathological significance of CMMC is largely unknown. Here, we quantitatively evaluated how the CMMC microarchitecture is related to cartilage condition, as well as regional differences in early idiopathic osteoarthritis (OA). Two groups of cadaveric female human femoral heads (intact cartilage vs early cartilage lesions) were identified, and a biopsy-based high-resolution micro-CT imaging was employed. Subchondral bone (SB) thickness, CMMC number, maximum and minimum CMMC size, and the CMMC morphology were quantified and compared between the two groups. The effect of joint's region and cartilage condition was examined on each dependent variable. The CMMC number and morphology were affected by region of the joint, but not by cartilage condition. On the other hand, the minimum and maximum CMMC size was changed by both the location on the joint, as well as the cartilage condition. The smallest CMMC were consistently detected at the load-bearing region (LBR) of the joint. Compared to non-pathological subjects, the size of the microchannels was enlarged in early OA, most noticeably at the non-load-bearing region (NLBR) and the peripheral rim (PR) of the femoral head. Furthermore, subchondral bone thinning was observed in early OA as a localized occurrence linked with areas of partial chondral defect. Our data point to an enlargement of the SB microchannel network, and a collective structural deterioration of SB in early idiopathic OA.

Ensemble learning and test-time augmentation for the segmentation of mineralized cartilage versus bone in high-resolution microCT images

High-resolution non-destructive 3D microCT imaging allows the visualization and structural characterization of mineralized cartilage and bone. Deriving statistically relevant quantitative structural information about these tissues, however, requires automated segmentation procedures, mainly because manual contouring is user-biased and time-consuming. Despite the increased spatial resolution in microCT 3D volumes, automatic segmentation of mineralized cartilage versus bone remains non-trivial since they have similar grayscale values. Our work investigates how reliable 2D segmentation masks can be predicted automatically based on a (set of) convolutional neural network(s) trained with a limited number of manually annotated samples. To do that, we compared different strategies to select the 2D samples to annotate and considered ensemble learning and test-time augmentation (TTA) to mitigate the limited accuracy and robustness resulting from the small number of annotated training samples. We show that, for a fixed amount of annotated image samples, 2D microCT slices to annotate should preferably be selected in distinct 3D volumes, at regular intervals, rather than being grouped in adjacent slices of a same 3D volume. Two main lessons are drawn regarding the use of ensembles or TTA instead of a single model. First, ensemble learning is shown to improve segmentation accuracy and to reduce the mean and standard deviation of the absolute errors in cartilage characteristics obtained with different initializations of the neural network training process. In contrast, TTA appears to be unable to improve the model’s robustness to unlucky initializations. Second, both TTA and ensembling improved the model’s confidence in its predictions and segmentation failure detection.

Three-Dimensional Whole-Organ Characterization of the Regional Alveolar Morphology in Normal Murine Lungs.

Alveolar architecture plays a fundamental role in the processes of ventilation and perfusion in the lung. Alterations in the alveolar surface area and alveolar cavity volume constitute the pathophysiological basis of chronic respiratory diseases such as pulmonary emphysema. Previous studies based on micro-computed tomography (micro-CT) of lung samples have allowed the geometrical study of acinar units. However, our current knowledge is based on the study of a few tissue samples in random locations of the lung that do not give an account of the spatial distributions of the alveolar architecture in the whole lung. In this work, we combine micro-CT imaging and computational geometry algorithms to study the regional distribution of key morphological parameters throughout the whole lung. To this end, 3D whole-lung images of Sprague–Dawley rats are acquired using high-resolution micro-CT imaging and analyzed to estimate porosity, alveolar surface density, and surface-to-volume ratio. We assess the effect of current gold-standard dehydration methods in the preparation of lung samples and propose a fixation protocol that includes the application of a methanol-PBS solution before dehydration. Our results show that regional porosity, alveolar surface density, and surface-to-volume ratio have a uniform distribution in normal lungs, which do not seem to be affected by gravitational effects. We further show that sample fixation based on ethanol baths for dehydration introduces shrinking and affects the acinar architecture in the subpleural regions. In contrast, preparations based on the proposed dehydration protocol effectively preserve the alveolar morphology.

The cavernous carotid inferolateral trunk and persistent primitive maxillary arteries: analysis of dissected arterially injected fetal specimens and high-resolution micro-CT of the dog\u2019s anastomotic arteries

PurposeThe presence of a persistent primitive maxillary artery is described in the literature dealing with the development of the cavernous carotid inferolateral trunk, and the relevant similarities of the cranial circulation of the human and dog. The literature includes no dissection photographs of the above-mentioned two human fetal arteries, only diagrammatic representations. This study’s objectives were to analyze photographs of fetal dissections for the presence of these two arteries, and also investigate the possibility of obtaining, in preserved dog specimens, high-resolution micro-CT imaging of arteries homologous with the above-mentioned two human arteries.MethodsThe literature describing the embryologic development of the cavernous carotid inferolateral trunk, the persistent primitive maxillary arteries, and their homologies in the dog was reviewed. Relevant dissections of fetal specimens were analyzed. High-resolution micro-CT images of un-dissected dog arteries were produced and analyzed.ResultsPhotographs of fetal specimen dissections demonstrate the cavernous carotid inferolateral trunk. A separate persistent primitive maxillary artery was not present in the dissected specimens. High-resolution micro-CT images of the dog demonstrate homologous arteries with segments of the human inferolateral trunk, and other skull base and brain arteries.ConclusionThis investigation provides the only photographs in the literature of dissected human fetal cavernous carotid inferolateral trunks. A persistent primitive maxillary artery was not present in the dissected specimens and is a non-existent structure, likely a previously misidentified carotid inferolateral trunk. High-resolution micro-CT images of the dog visualized arteries that are homologous to segments of the human cavernous carotid inferolateral trunk artery.

Fine pottery shaping techniques in Predynastic Egypt: A pilot study on non-destructive analysis using an X-Ray CT scanning system

Eight complete Egyptian Predynastic pottery samples were studied using X-ray CT scanning systems to assess such systems’ applicability as a means of refining our knowledge of pottery manufacturing techniques. This non-destructive analysis produces two- and three-dimensional renderings of the external surface and internal structure of an object with high-resolution micro-CT imaging. The 3D images of the air voids inside ceramic walls and observation of the wall cross-slices indicated that the Predynastic pottery was not made only by coiling/slab-building but also combined various techniques. There was also a distinct difference in the CT cross-slice void shapes of black-topped and decorated wares, probably due to variations in the pressure applied, usually associated with the use of a turning device. The results give clearer information about the ancient potter’s choice of technique and application according to shape and size, as well as about a development phase in the radial method.

Post-administration dosimetry in yttrium-90 radioembolization through micro-CT imaging of radiopaque microspheres in a porcine renal model

The purpose of this study is to perform post-administration dosimetry in yttrium-90 radioembolization through micro-CT imaging of radiopaque microsphere distributions in a porcine renal model and explore the impact of spatial resolution of an imaging system on the extraction of specific dose metrics. Following the administration of radiopaque microspheres to the kidney of a hybrid farm pig, the kidney was explanted and imaged with micro-CT. To produce an activity distribution, 400 MBq of yttrium-90 activity was distributed throughout segmented voxels of the embolized vasculature based on an established linear relationship between microsphere concentration and CT voxel value. This distribution was down-sampled to coarser isotropic grids ranging in voxel size from 2.5 to 15 mm to emulate nominal resolutions comparable to those found in yttrium-90 PET and Bremsstrahlung SPECT imaging. Dose distributions were calculated through the convolution of activity distributions with dose-voxel kernels generated using the GATE Monte Carlo toolkit. Contours were computed to represent normal tissue and target volumes. Dose-volume histograms, dose metrics, and dose profiles were compared to a ground truth dose distribution computed with GATE. The mean dose to the target for all studied voxel sizes was found to be within 5.7% of the ground truth mean dose. was shown to be strongly correlated with image voxel size of the dose distribution (r 2 = 0.90). is cited in the literature as an important dose metric and its dependence on voxel size suggests higher resolution dose distributions may provide new perspectives on dose-response relationships in yttrium-90 radioembolization. This study demonstrates that dose distributions with large voxels incorrectly homogenize the dose by attributing escalated doses to normal tissues and reduced doses in high-dose target regions. High-resolution micro-CT imaging of radiopaque microsphere distributions can provide increased confidence in characterizing the absorbed dose heterogeneity in yttrium-90 radioembolization.