Gap filling of 3-D microvascular networks by tensor voting

Simulation of oxygen transport and estimation of tissue perfusion in extensive microvascular networks: Application to cerebral cortex

Advanced imaging techniques have made available extensive three-dimensional microvascular network structures. Simulation of oxygen transport by such networks requires information on blood flow rates and oxygen levels in vessels crossing boundaries of the imaged region, which is difficult to obtain experimentally. Here, a computational method is presented for estimating blood flow rates, oxygen levels, tissue perfusion and oxygen extraction, based on incomplete boundary conditions. Flow rates in all segments are estimated using a previously published method. Vessels crossing the region boundary are classified as arterioles, capillaries or venules. Oxygen levels in inflowing capillaries are assigned based on values in outflowing capillaries, and similarly for venules. Convective and diffusive oxygen transport is simulated. Contributions of each vessel to perfusion are computed in proportion to the decline in oxygen concentration along that vessel. For a vascular network in the mouse cerebral cortex, predicted tissue oxygen levels show a broad distribution, with 99% of tissue in the range of 20 to 80 mmHg under reference conditions, and steep gradients near arterioles. Perfusion and extraction estimates are consistent with experimental values. A 30% reduction in perfusion or a 30% increase in oxygen demand, relative to reference levels, is predicted to result in tissue hypoxia.

Automatic Collateral Scoring From 3D CTA Images

The collateral score is an important biomarker in decision making for endovascular treatment (EVT) of patients with ischemic stroke. The existing collateral grading systems are based on visual inspection and prone to subjective interpretation and interobserver variation. The purpose of our work is the development of an automatic collateral scoring method. In this work, we present a method that is inspired by human collateral scoring. Firstly, we define an anatomical region by atlas-based registration and extract vessel structures using a deep convolutional neural network. From this, high-level features based on the ratios of vessel length and volume of the occluded and the contralateral side are defined. Multi-class classification models are used to map the feature space to a four-grade collateral score and a quantitative score. The dataset used for training, validation and testing is from a registry of images acquired in clinical routine at multiple medical centers. The model performance is tested on 269 subjects, achieving an accuracy of 0.8. The dichotomized collateral score accuracy is 0.9. The error is comparable to the interobserver variation, the results are comparable to the performance of two radiologists with 10 to 30 years of experience.

From whole-organ imaging to in-silico blood flow modeling: A new multi-scale network analysis for revisiting tissue functional anatomy

We present a multi-disciplinary image-based blood flow perfusion modeling of a whole organ vascular network for analyzing both its structural and functional properties. We show how the use of Light-Sheet Fluorescence Microscopy (LSFM) permits whole-organ micro-vascular imaging, analysis and modelling. By using adapted image post-treatment workflow, we could segment, vectorize and reconstruct the entire micro-vascular network composed of 1.7 million vessels, from the tissue-scale, inside a ∼ 25 × 5 × 1 = 125mm3 volume of the mouse fat pad, hundreds of times larger than previous studies, down to the cellular scale at micron resolution, with the entire blood perfusion modeled. Adapted network analysis revealed the structural and functional organization of meso-scale tissue as strongly connected communities of vessels. These communities share a distinct heterogeneous core region and a more homogeneous peripheral region, consistently with known biological functions of fat tissue. Graph clustering analysis also revealed two distinct robust meso-scale typical sizes (from 10 to several hundred times the cellular size), revealing, for the first time, strongly connected functional vascular communities. These community networks support heterogeneous micro-environments. This work provides the proof of concept that in-silico all-tissue perfusion modeling can reveal new structural and functional exchanges between micro-regions in tissues, found from community clusters in the vascular graph.

Dynamic Changes in Microvascular Flow Conductivity and Perfusion After Myocardial Infarction Shown by Image-Based Modeling

Background Microcirculation is a decisive factor in tissue reperfusion inadequacy following myocardial infarction ( MI ). Nonetheless, experimental assessment of blood flow in microcirculation remains a bottleneck. We sought to model blood flow properties in coronary microcirculation at different time points after MI and to compare them with healthy conditions to obtain insights into alterations in cardiac tissue perfusion. Methods and Results We developed an image-based modeling framework that permitted feeding a continuum flow model with anatomical data previously obtained from the pig coronary microvasculature to calculate physiologically meaningful permeability tensors. The tensors encompassed the microvascular conductivity and were also used to estimate the arteriole-venule drop in pressure and myocardial blood flow. Our results indicate that the tensors increased in a bimodal pattern at infarcted areas on days 1 and 7 after MI while a nonphysiological decrease in arteriole-venule drop in pressure was observed; contrary, the tensors and the arteriole-venule drop in pressure on day 3 after MI , and in remote areas, were closer to values for healthy tissue. Myocardial blood flow calculated using the condition-dependent arteriole-venule drop in pressure decreased in infarcted areas. Last, we simulated specific modes of vascular remodeling, such as vasodilation, vasoconstriction, or pruning, and quantified their distinct impact on microvascular conductivity. Conclusions Our study unravels time- and region-dependent alterations of tissue perfusion related to the structural changes occurring in the coronary microvasculature due to MI . It also paves the way for conducting simulations in new therapeutic interventions in MI and for image-based microvascular modeling by applying continuum flow models in other biomedical scenarios.

Quantitative analysis of vascular properties derived from ultrafast DCE-MRI to discriminate malignant and benign breast tumors

PURPOSE

We propose a novel methodology to integrate morphological and functional information of tumor-associated vessels to assist in the diagnosis of suspicious breast lesions.

THEORY AND METHODS

Ultrafast, fast, and high spatial resolution DCE-MRI data were acquired on 15 patients with suspicious breast lesions. Segmentation of the vasculature from the surrounding tissue was performed by applying a Hessian filter to the enhanced image to generate a map of the probability for each voxel to belong to a vessel. Summary measures were generated for vascular morphology, as well as the inputs and outputs of vessels physically connected to the tumor. The ultrafast DCE-MRI data was analyzed by a modified Tofts model to estimate the bolus arrival time, K^trans (volume transfer coefficient), and v_p (plasma volume fraction). The measures were compared between malignant and benign lesions via the Wilcoxon test, and then incorporated into a logistic ridge regression model to assess their combined diagnostic ability.

RESULTS

A total of 24 lesions were included in the study (13 malignant and 11 benign). The vessel count, K^trans , and v_p showed significant difference between malignant and benign lesions (P = 0.009, 0.034, and 0.010, area under curve [AUC] = 0.76, 0.63, and 0.70, respectively). The best multivariate logistic regression model for differentiation included the vessel count and bolus arrival time (AUC = 0.91).

CONCLUSION

This study provides preliminary evidence that combining quantitative characterization of morphological and functional features of breast vasculature may provide an accurate means to diagnose breast cancer.

3D Active Vessel Tracking Using an Elliptical Prior

In this paper, we propose a novel vessel tracking method, called active vessel tracking (AVT). The proposed method retains the major advantages that most 2D segmentation methods have demonstrated for 3D tracking while overcoming the drawbacks of previous 3D vessel tracking methods. Under the assumption that the vessel is cylindrical, thereby making its cross-section elliptical, the AVT finds a plane perpendicular to the vessel axis while tracking the vessel along its length. Also, We propose a method for vessel branch detection to automatically track complete vascular networks from a single starting point, whereas the previously proposed solutions have usually been limited in handling vessel bifurcations precisely on 3D or have required considerable user interaction. Our results show that the method is robust and accurate in both synthetic and clinical cases. In an experiment on synthetic data sets, the proposed method achieved a tracking accuracy of 96.1±0.5, detecting 99.1% of the branches. In an experiment on abdominal CTA data sets, it achieved a tracking accuracy of 98.4±0.5 for six target vessels, detecting 98.3% of the branches. These results show that the proposed method can outperform previous methods for vessel tracking.

Automated motion-artifact correction in an OCTA image using tensor voting approach

Optical coherence tomography angiography (OCTA) is a promising tool for imaging subsurface microvascular networks owing to its micron-level resolution and high sensitivity. However, it is not uncommon that OCTA imaging suffers from strip artifacts induced by tissue motion. Although various algorithms for motion correction have been reported, a method that enables motion correction on a single en face OCTA image remains a challenge. In this study, we propose a motion correction approach based on microvasculature detection and broken gap filling. Unlike previous methods using registration to restore disturbed vasculature during motion artifact removal, tensor voting is performed in an individual projected image to connect the broken vasculature. Both simulation and in vivo 3D OCTA imaging of the mouse bladder are performed to validate the effectiveness of this method. A comparison of in vivo images before and after motion correction shows that our method effectively corrects tissue motion artifacts while preserving the continuity of vasculature network. Furthermore, in vivo results of this technique are presented to demonstrate its utility for imaging tumor angiogenesis in the mouse bladder.

Toward quantitative three-dimensional microvascular networks segmentation with multiview light-sheet fluorescence microscopy

Three-dimensional (3-D) large-scale imaging of microvascular networks is of interest in various areas of biology and medicine related to structural, functional, developmental, and pathological issues. Light-sheet fluorescence microscopy (LSFM) techniques are rapidly spreading and are now on the way to offer operational solutions for large-scale tissue imaging. This contribution describes how reliable vessel segmentation can be handled from LSFM data in very large tissue volumes using a suitable image analysis workflow. Since capillaries are tubular objects of a few microns scale radius, they represent challenging structures to reliably reconstruct without distortion and artifacts. We provide a systematic analysis of multiview deconvolution image processing workflow to control and evaluate the accuracy of the reconstructed vascular network using various low to high level, metrics. We show that even if low-level structural metrics are sensitive to isotropic imaging enhancement provided by a larger number of views, functional high-level metrics, including perfusion permeability, are less sensitive. Hence, combining deconvolution and registration onto a few number of views appears sufficient for a reliable quantitative 3-D vessel segmentation for their possible use for perfusion modeling.

Analysis of vascular homogeneity and anisotropy on high-resolution primate brain imaging

Using a systematic investigation of brain blood volume, in high-resolution synchrotron 3D images of microvascular structures within cortical regions of a primate brain, we challenge several basic questions regarding possible vascular bias in high-resolution functional neuroimaging. We present a bilateral comparison of cortical regions, where we analyze relative vascular volume in voxels from 150 to 1000 μm side lengths in the white and grey matter. We show that, if voxel size reaches a scale smaller than 300 µm, the vascular volume can no longer be considered homogeneous, either within one hemisphere or in bilateral comparison between samples. We demonstrate that voxel size influences the comparison between vessel-relative volume distributions depending on the scale considered (i.e., hemisphere, lobe, or sample). Furthermore, we also investigate how voxel anisotropy and orientation can affect the apparent vascular volume, in accordance with actual fMRI voxel sizes. These findings are discussed from the various perspectives of high-resolution brain functional imaging. Hum Brain Mapp 38:5756-5777, 2017. © 2017 Wiley Periodicals, Inc.

Clustering Tree-Structured Data on Manifold

Tree-structured data usually contain both topological and geometrical information, and are necessarily considered on manifold instead of euclidean space for appropriate data parameterization and analysis. In this study, we propose a novel tree-structured data parameterization, called Topology-Attribute matrix (T-A matrix), so the data clustering task can be conducted on matrix manifold. We incorporate the structure constraints embedded in data into the non-negative matrix factorization method to determine meta-trees from the T-A matrix, and the signature vector of each single tree can then be extracted by meta-tree decomposition. The meta-tree space turns out to be a cone space, in which we explore the distance metric and implement the clustering algorithm based on the concepts like Fréchet mean. Finally, the T-A matrix based clustering (TAMBAC) framework is evaluated and compared using both simulated data and real retinal images to illustrate its efficiency and accuracy.

Mapping the 3D Connectivity of the Rat Inner Retinal Vascular Network Using OCT Angiography

PURPOSE

The purpose of this study is to demonstrate three-dimensional (3D) graphing based on optical coherence tomography (OCT) angiography for characterization of the inner retinal vascular architecture and determination of its topologic principles.

METHODS

Rat eyes (N = 3) were imaged with a 1300-nm spectral/Fourier domain OCT microscope. A topologic model of the inner retinal vascular network was obtained from OCT angiography data using a combination of automated and manually-guided image processing techniques. Using a resistive network model, with experimentally-quantified flow in major retinal vessels near the optic nerve head as boundary conditions, theoretical changes in the distribution of flow induced by vessel dilations were inferred.

RESULTS

A topologically-representative 3D vectorized graph of the inner retinal vasculature, derived from OCT angiography data, is presented. The laminar and compartmental connectivity of the vasculature are characterized. In contrast to sparse connectivity between the superficial vitreal vasculature and capillary plexuses of the inner retina, connectivity between the two capillary plexus layers is dense. Simulated dilation of single arterioles is shown to produce both localized and lamina-specific changes in blood flow, while dilation of capillaries in a given retinal vascular layer is shown to lead to increased total flow in that layer.

CONCLUSIONS

Our graphing and modeling data suggest that vascular architecture enables both local and lamina-specific control of blood flow in the inner retina. The imaging, graph analysis, and modeling approach presented here will help provide a detailed characterization of vascular changes in a variety of retinal diseases, both in experimental preclinical models and human subjects.

A higher-order tensor vessel tractography for segmentation of vascular structures

A new vascular structure segmentation method, which is based on a cylindrical flux-based higher order tensor (HOT), is presented. On a vessel structure, the HOT naturally models branching points, which create challenges for vessel segmentation algorithms. In a general linear HOT model embedded in 3D, one has to work with an even order tensor due to an enforced antipodal-symmetry on the unit sphere. However, in scenarios such as in a bifurcation, the antipodally-symmetric tensor embedded in 3D will not be useful. In order to overcome that limitation, we embed the tensor in 4D and obtain a structure that can model asymmetric junction scenarios. During construction of a higher order tensor (e.g. third or fourth order) in 4D, the orientation vectors lie on the unit 3-sphere, in contrast to the unit 2-sphere in 3D tensor modeling. This 4D tensor is exploited in a seed-based vessel segmentation algorithm, where the principal directions of the 4D HOT is obtained by decomposition, and used in a HOT tractography approach. We demonstrate quantitative validation of the proposed algorithm on both synthetic complex tubular structures as well as real cerebral vasculature in Magnetic Resonance Angiography (MRA) datasets and coronary arteries from Computed Tomography Angiography (CTA) volumes.

Structure propagation for image registration

Mosaicing is a commonly used technique in many medical imaging applications where subimages are stitched together in order to obtain a larger field of view. However, stitching, which involves alignment or registration in overlapping regions, is often challenging when the information shared by subimages is absent or small. While it is not possible to perform an alignment without overlap using existing techniques, imaging artifacts such as distortions towards image boundaries present further complications during registration by decreasing the reliability of available information. Without taking these into consideration, a registration approach might violate the continuity and the smoothness of structures across subimages. In this paper, we propose a novel registration approach for the stitching of subimages in such challenging scenarios. By using a perceptual grouping approach, we extend subimages beyond their boundaries by propagating available structures in order to obtain structural maps in the extended regions. These maps are then used to establish correspondences between subimages when the shared information is absent, small or unreliable. Using our approach ensures the continuity and the smoothness of structures across subimage boundaries. Furthermore, since only structures are used, the proposed method can also be used for the stitching of multi-modal images. Our approach is unique in that it also enables contactless stitching. We demonstrate the effectiveness of the proposed method by performing several experiments on synthetic and medical images. Moreover, we show how stitching is possible in the presence of a physical gap between subimages.

Directional Histogram Ratio at Random Probes: A Local Thresholding Criterion for Capillary Images

With the development of micron-scale imaging techniques, capillaries can be conveniently visualized using methods such as two-photon and whole mount microscopy. However, the presence of background staining, leaky vessels and the diffusion of small fluorescent molecules can lead to significant complexity in image analysis and loss of information necessary to accurately quantify vascular metrics. One solution to this problem is the development of accurate thresholding algorithms that reliably distinguish blood vessels from surrounding tissue. Although various thresholding algorithms have been proposed, our results suggest that without appropriate pre- or post-processing, the existing approaches may fail to obtain satisfactory results for capillary images that include areas of contamination. In this study, we propose a novel local thresholding algorithm, called directional histogram ratio at random probes (DHR-RP). This method explicitly considers the geometric features of tube-like objects in conducting image binarization, and has a reliable performance in distinguishing small vessels from either clean or contaminated background. Experimental and simulation studies suggest that our DHR-RP algorithm is superior over existing thresholding methods.

On the normalization of cerebral blood flow

Cerebral blood flow (CBF) is the most common parameter for the quantification of brain's function. Literature data indicate a widespread dispersion of values that might be related to some differences in the measurement conditions that are not properly taken into account in CBF evaluation. Using recent high-resolution imaging of the complete cortical microvasculature of primate brain, we perform extensive numerical evaluation of the cerebral perfusion. We show that blood perfusion associated with intravascular tracers should be normalized by the surface of the voxel rather than by its volume and we consistently test this result on the available literature data.

Automatic correction of gaps in cerebrovascular segmentations extracted from 3D time-of-flight MRA datasets

OBJECTIVES

Exact cerebrovascular segmentations are required for several applications in today's clinical routine. A major drawback of typical automatic segmentation methods is the occurrence of gaps within the segmentation. These gaps are typically located at small vessel structures exhibiting low intensities. Manual correction is very time-consuming and not suitable in clinical practice. This work presents a post-processing method for the automatic detection and closing of gaps in cerebrovascular segmentations.

METHODS

In this approach, the 3D centerline is calculated from an available vessel segmentation, which enables the detection of corresponding vessel endpoints. These endpoints are then used to detect possible connections to other 3D centerline voxels with a graph-based approach. After consistency check, reasonable detected paths are expanded to the vessel boundaries using a level set approach and combined with the initial segmentation.

RESULTS

For evaluation purposes, 100 gaps were artificially inserted at non-branching vessels and bifurcations in manual cerebrovascular segmentations derived from ten Time-of-Flight magnetic resonance angiography datasets. The results show that the presented method is capable of detecting 82% of the non-branching vessel gaps and 84% of the bifurcation gaps. The level set segmentation expands the detected connections with 0.42 mm accuracy compared to the initial segmentations. A further evaluation based on 10 real automatic segmentations from the same datasets shows that the proposed method detects 35 additional connections in average per dataset, whereas 92.7% were rated as correct by a medical expert.

CONCLUSION

The presented approach can considerably improve the accuracy of cerebrovascular segmentations and of following analysis outcomes.

Vectorization of optically sectioned brain microvasculature: learning aids completion of vascular graphs by connecting gaps and deleting open-ended segments

A graph of tissue vasculature is an essential requirement to model the exchange of gasses and nutriments between the blood and cells in the brain. Such a graph is derived from a vectorized representation of anatomical data, provides a map of all vessels as vertices and segments, and may include the location of nonvascular components, such as neuronal and glial somata. Yet vectorized data sets typically contain erroneous gaps, spurious endpoints, and spuriously merged strands. Current methods to correct such defects only address the issue of connecting gaps and further require manual tuning of parameters in a high dimensional algorithm. To address these shortcomings, we introduce a supervised machine learning method that (1) connects vessel gaps by "learned threshold relaxation"; (2) removes spurious segments by "learning to eliminate deletion candidate strands"; and (3) enforces consistency in the joint space of learned vascular graph corrections through "consistency learning." Human operators are only required to label individual objects they recognize in a training set and are not burdened with tuning parameters. The supervised learning procedure examines the geometry and topology of features in the neighborhood of each vessel segment under consideration. We demonstrate the effectiveness of these methods on four sets of microvascular data, each with >800(3) voxels, obtained with all optical histology of mouse tissue and vectorization by state-of-the-art techniques in image segmentation. Through statistically validated sampling and analysis in terms of precision recall curves, we find that learning with bagged boosted decision trees reduces equal-error error rates for threshold relaxation by 5-21% and strand elimination performance by 18-57%. We benchmark generalization performance across datasets; while improvements vary between data sets, learning always leads to a useful reduction in error rates. Overall, learning is shown to more than halve the total error rate, and therefore, human time spent manually correcting such vectorizations.

3-D image pre-processing algorithms for improved automated tracing of neuronal arbors

The accuracy and reliability of automated neurite tracing systems is ultimately limited by image quality as reflected in the signal-to-noise ratio, contrast, and image variability. This paper describes a novel combination of image processing methods that operate on images of neurites captured by confocal and widefield microscopy, and produce synthetic images that are better suited to automated tracing. The algorithms are based on the curvelet transform (for denoising curvilinear structures and local orientation estimation), perceptual grouping by scalar voting (for elimination of non-tubular structures and improvement of neurite continuity while preserving branch points), adaptive focus detection, and depth estimation (for handling widefield images without deconvolution). The proposed methods are fast, and capable of handling large images. Their ability to handle images of unlimited size derives from automated tiling of large images along the lateral dimension, and processing of 3-D images one optical slice at a time. Their speed derives in part from the fact that the core computations are formulated in terms of the Fast Fourier Transform (FFT), and in part from parallel computation on multi-core computers. The methods are simple to apply to new images since they require very few adjustable parameters, all of which are intuitive. Examples of pre-processing DIADEM Challenge images are used to illustrate improved automated tracing resulting from our pre-processing methods.

Coupling and robustness of intra-cortical vascular territories

Vascular domains have been described as being coupled to neuronal functional units enabling dynamic blood supply to the cerebral cyto-architecture. Recent experiments have shown that penetrating arterioles of the grey matter are the building blocks for such units. Nevertheless, vascular territories are still poorly known, as the collection and analysis of large three-dimensional micro-vascular networks are difficult. By using an exhaustive reconstruction of the micro-vascular network in an 18 mm(3) volume of marmoset cerebral cortex, we numerically computed the blood flow in each blood vessel. We thus defined arterial and venular territories and examined their overlap. A large part of the intracortical vascular network was found to be supplied by several arteries and drained by several venules. We quantified this multiple potential to compensate for deficiencies by introducing a new robustness parameter. Robustness proved to be positively correlated with cortical depth and a systematic investigation of coupling maps indicated local patterns of overlap between neighbouring arteries and neighbouring venules. However, arterio-venular coupling did not have a spatial pattern of overlap but showed locally preferential functional coupling, especially of one artery with two venules, supporting the notion of vascular units. We concluded that intra-cortical perfusion in the primate was characterised by both very narrow functional beds and a large capacity for compensatory redistribution, far beyond the nearest neighbour collaterals.

Iterative tensor voting for perceptual grouping of ill-defined curvilinear structures

In this paper, a novel approach is proposed for perceptual grouping and localization of ill-defined curvilinear structures. Our approach builds upon the tensor voting and the iterative voting frameworks. Its efficacy lies on iterative refinements of curvilinear structures by gradually shifting from an exploratory to an exploitative mode. Such a mode shifting is achieved by reducing the aperture of the tensor voting fields, which is shown to improve curve grouping and inference by enhancing the concentration of the votes over promising, salient structures. The proposed technique is validated on delineating adherens junctions that are imaged through fluorescence microscopy. However, the method is also applicable for screening other organisms based on characteristics of their cell wall structures. Adherens junctions maintain tissue structural integrity and cell-cell interactions. Visually, they exhibit fibrous patterns that may be diffused, heterogeneous in fluorescence intensity, or punctate and frequently perceptual. Besides the application to real data, the proposed method is compared to prior methods on synthetic and annotated real data, showing high precision rates.

Cerebral blood flow modeling in primate cortex

We report new results on blood flow modeling over large volumes of cortical gray matter of primate brain. We propose a network method for computing the blood flow, which handles realistic boundary conditions, complex vessel shapes, and complex nonlinear blood rheology. From a detailed comparison of the available models for the blood flow rheology and the phase separation effect, we are able to derive important new results on the impact of network structure on blood pressure, hematocrit, and flow distributions. Our findings show that the network geometry (vessel shapes and diameters), the boundary conditions associated with the arterial inputs and venous outputs, and the effective viscosity of the blood are essential components in the flow distribution. In contrast, we show that the phase separation effect has a minor function in the global microvascular hemodynamic behavior. The behavior of the pressure, hematocrit, and blood flow distributions within the network are described through the depth of the primate cerebral cortex and are discussed.

Vascular Tree Reconstruction by Minimizing A Physiological Functional Cost

The reconstruction of complete vascular trees from medical images has many important applications. Although vessel detection has been extensively investigated, little work has been done on how connect the results to reconstruct the full trees. In this paper, we propose a novel theoretical framework for automatic vessel connection, where the automation is achieved by leveraging constraints from the physiological properties of the vascular trees. In particular, a physiological functional cost for the whole vascular tree is derived and an efficient algorithm is developed to minimize it. The method is generic and can be applied to different vessel detection/segmentation results, e.g. the classic rigid detection method as adopted in this paper. We demonstrate the effectiveness of this method on both 2D and 3D data.

Micro computed tomography for vascular exploration

Vascular exploration of small animals requires imaging hardware with a very high spatial resolution, capable of differentiating large as well as small vessels, in both in vivo and ex vivo studies. Micro Computed Tomography (micro-CT) has emerged in recent years as the preferred modality for this purpose, providing high resolution 3D volumetric data suitable for analysis, quantification, validation, and visualization of results. The usefulness of micro-CT, however, can be adversely affected by a range of factors including physical animal preparation, numerical quantification, visualization of results, and quantification software with limited possibilities. Exacerbating these inherent difficulties is the lack of a unified standard for micro-CT imaging. Most micro-CT today is aimed at particular applications and the software tools needed for quantification, developed mainly by imaging hardware manufacturers, lack the level of detail needed to address more specific aims. This review highlights the capabilities of micro-CT for vascular exploration, describes the current state of imaging protocols, and offers guidelines and suggestions aimed at making micro-CT more accurate, replicable, and robust.

Correlations of neuronal and microvascular densities in murine cortex revealed by direct counting and colocalization of nuclei and vessels

It is well known that the density of neurons varies within the adult brain. In neocortex, this includes variations in neuronal density between different lamina as well as between different regions. Yet the concomitant variation of the microvessels is largely uncharted. Here, we present automated histological, imaging, and analysis tools to simultaneously map the locations of all neuronal and non-neuronal nuclei and the centerlines and diameters of all blood vessels within thick slabs of neocortex from mice. Based on total inventory measurements of different cortical regions ( approximately 10(7) cells vectorized across brains), these methods revealed: (1) In three dimensions, the mean distance of the center of neuronal somata to the closest microvessel was 15 mum. (2) Volume samples within lamina of a given region show that the density of microvessels does not match the strong laminar variation in neuronal density. This holds for both agranular and granular cortex. (3) Volume samples in successive radii from the midline to the ventral-lateral edge, where each volume summed the number of cells and microvessels from the pia to the white matter, show a significant correlation between neuronal and microvessel densities. These data show that while neuronal and vascular densities do not track each other on the 100 mum scale of cortical lamina, they do track each other on the 1-10 mm scale of the cortical mantle. The absence of a disproportionate density of blood vessels in granular lamina is argued to be consistent with the initial locus of functional brain imaging signals.

A 3D-investigation shows that angiogenesis in primate cerebral cortex mainly occurs at capillary level

This paper describes the use of a new 3D high-resolution imaging technique dedicated to functional vessels for a systematic quantitative study of angiogenesis in the primate cortex. We present a new method which permits, using synchrotron X-ray micro-tomography imaging, the identification of micro-vascular components as well as their automatic numerical digitalization and extraction from very large 3D image analysis and post-treatments. This method is used to analyze various levels of micro-vascular organization and their postnatal modifications. Comparing newborn- and adult marmosets, we found an increase in vascular volume (270%), exchange surface (260%) and vessel length (290%) associated to a decrease in distances between vessel and tissue (32%). The increase in relative vascular volumes between the two ages, examined through the whole cortical depth, has been found to be mainly sustained by events occurring at the capillary level, and only marginally at the perforating vessel level. This work shows that the postnatal cortical maturation classically described in terms of synaptogenesis, gliogenesis and connectivity plasticity is accompanied by an intensive remodeling of micro-vascular patterns.