The neurovasculature plays a critical role in a wide range of neuropathological processes from dementia to stroke to tumors [1]. In addition, the physiological underpinnings of image contrast in functional MRI (fMRI) critically involves the neurovasculature [2], and is central to understanding drug delivery and pharmacokinetics of novel therapies in the brain [3], [4]. The challenges in obtaining high-resolution vascular data from patients, combined with new pre-clinical models of neuropathology, have made the mouse brain indispensable to investigations of these areas [5], [6].

Historically, high-resolution characterization of the murine neurovascular architecture has involved either two-dimensional (2D) measurements made on tissue sections from 3D specimens [7] or three-dimensional (3D) measurements made on corrosion casts [8]. Although 3D reconstruction of 2D tissue slices generates high resolution imaging data, the practical limit as to the number of adjacent tissue slices that can be obtained limits the region of brain that can be interrogated. Additionally, recovering the 3D blood vessel and white matter fiber geometry once destroyed by tissue sectioning requires complex reconstruction techniques. Conversely, corrosion casts allow extensive brain regions to be analyzed but require dissolving of tissues to visualize cast vessels, and thus precludes simultaneous assessment of the vasculature and neuroanatomy.

Recent advances in imaging technology have permitted the visualization of the murine neuroarchitecture in exquisite detail using magnetic resonance microscopy (μMRI), a type of 3D MRI that offers high ex vivo imaging resolutions (currently ∼20–30 µm³) [9]. Using μMRI one can obtain images with different kinds of “physiological stains” or image contrast mechanisms, e.g. diffusion tensor imaging (DTI) to visualize the orientations of white matter fibers in the brain [10].

Studies describing wide-area-mapping of the murine brain vasculature with 3D μMRI have been limited due to the difficulties associated with sample preparation, contrast mechanism employed, image co-registration, and image processing for vessel extraction. For example, Dorr et al combined vascular data obtained using micro computed tomography (μCT) with a μMRI murine brain atlas. While this approach exploits the strengths of each modality, co-registration of these 3D image volumes is challenging [11]. This is especially true when it involves registration with DTI or the abnormal vasculature of brain tumors. Gelatin doped with gadolinium chelates has also been employed to image both, mouse embryo [12] and adult rat brain [13] vasculature using μMRI. Unfortunately, the use of gelatin perfusate is not ideal as it needs to be mixed with high concentrations of gadolinium chelate to achieve sufficient T₁ relaxation, and diffusion of these chelates out of the blood vessels over time results in loss of vascular contrast. Here we describe a new method for extending the utility of 3D μMRI to include “whole brain” mapping of the vasculature that circumvents the abovementioned drawbacks. We validated our vascular μMRI technique using optical and μCT techniques and present three important biological applications to demonstrate the utility of this approach for characterizing changes in the neurovascular microenvironment wrought by invasive and non-invasive pre-clinical brain tumor models.

To circumvent the limitations of traditional vascular imaging approaches, we perfused murine brains with an inert silicone rubber compound (MICROFIL®, FlowTech Inc., MA) which has a low viscosity that permits complete filling of the murine cerebral vasculature [14]. Following polymerization, Microfilled blood vessels appear dark on gradient-echo MRI images due to the absence of mobile water and potential susceptibility contrast. Being hydrophobic restricts the Microfil® to the vasculature with minimal extravasation. Additionally, because Microfil® is radio-opaque and light microscopy opaque, it enables validation of the μMRI-derived vascular data with μCT, x-ray imaging and bright field whole mount microscopy as we have demonstrated in this study.

We demonstrate the potential of our μMRI technique via several applications that include combined 3D imaging of angiogenesis-induced vascular remodeling and white matter reorganization in both, invasive and non-invasive brain tumor models; characterization of the morphological heterogeneity of the neurovasculature; and “multi-scale” imaging of brain tumor angiogenesis. Since μMRI generates digitized 3D images, the neurovascular data derived from it can be employed in a plethora of applications ranging from the study of cerebral hemodynamics [15] to image contrast mechanisms in MRI [16].

Traditionally, the neurovasculature in murine models of disease has been characterized at two spatial scales: the “cellular” or microscopic scale using 3D optical techniques [18], [19], and the “systemic” or macroscopic scale using methods such as contrast enhanced MRI, magnetic resonance angiography (MRA) [20] [21] or computed tomography (CT) [11], [22] [17]. Optical approaches suffer from limited tissue penetration and provide relatively small fields of view. This makes “whole brain” coverage and co-registration with in vivo imaging data from complementary imaging modalities challenging. Conversely, μCT provides unparalleled 3D detail, but has limited soft tissue contrast (e.g. gray and white matter contrast), which is crucial to any comprehensive study of the neurovasculature. “Mesoscopic” resolution (20–40 µm) vascular μMRI bridges the gap between these spatial scales and enables imaging of the entire brain vasculature while simultaneously providing outstanding gray and white matter contrast.

While newer methods such as volumetric computed tomography (VCT) [23] and synchrotron tomography [24] are capable of vascular imaging at exquisite spatial resolutions, they lack soft tissue contrast and require specialized detectors and a high-energy synchrotron source, respectively. Compared to these methods, vascular μMRI has three major advantages. First, it does not require specialized MRI hardware, and is inexpensive and easy to implement on any μMRI scanner. Second, it provides exceptionally high quality, 3D images of the vasculature by exploiting a novel contrast mechanism that can be easily combined with other MRI contrast mechanism (e.g., DTI) without affecting their quality. Third, it yields stable samples that can be imaged non-destructively with MRI, CT or optical microscopy without requiring any additional contrast agents or labels.

A current limitation of using μMRI to image the vasculature of the whole mouse brain is that even at ∼30 µm resolution, arterioles, venules and capillary-sized vessels are undetectable. This difference is apparent when comparing the vascular coverage visible in μMRI (Fig. 1d) and that visible in μCT (Fig. 1d) and histology (Fig. 1e). This constraint on the μMRI spatial resolution results in partial volume effects that impact the calculation of morphological vascular parameters. For example, partial volume effects can lead to overestimations of vessel length, caliber and fractional volume, and an underestimation of vessel tortuosity.

With the availability of higher magnetic field imaging systems (9.4 Tesla or higher) and more powerful imaging gradients, it will be possible to acquire higher spatial resolution μMRI images. However, this improvement in resolution will come at the expense of increased image acquisition times, which will result in large size image data sets and limit the number of samples that can be imaged. While strategies such as doping the sample with gadolinium to shorten the tissue T₁ or employing partial Fourier reconstruction schemes ameliorates protracted imaging times [25], achieving reasonably high signal-to-noise ratio (SNR) with smaller voxel sizes remains a technical challenge. This is further compounded by the fact that Microfilled vessels contain a limited amount of mobile “MR visible” water.

Both, excision from the skull and tissue fixation result in distortion of the brain tissue. For μMRI, the brain sample is enclosed in a 10mm glass NMR tube and for μCT the sample is embedded in a gel prior to imaging. Each of these steps also induce tissue distortions. Collectively, these tissue distortions preclude direct vessel-to-vessel co-registration between the μMRI and μCT data at their native spatial resolution. For example, at the ∼60 µm resolution of μMRI, misalignments of the order of a few μMRI voxels between the μMRI and μCT-derived vasculature precludes meaningful voxel-wise statistical analyses. Therefore, to validate the vascular coverage of the μMRI data with that derived from μCT, we computed the fractional blood volume (FV) for both datasets on a coarse spatial grid. We selected the FV as it is a morphological parameter that is function of both, vessel caliber and vessel density [26]. To ensure insensitivity to misalignments between the two datasets, we computed the FV by calculating the fractional occupancy of the binarized vasculature derived from the μMRI and μCT data within a coarse 8×8×8 spatial grid and found good agreement between the FVs obtained from the two methods. Due to the variety of measurement techniques employed, reported values for the blood volume in the mouse brain range from ∼0.5%–6% [22], [27], [28], [29], [30]. The FV of 3.2% computed from the μCT data fall within this range, while the FV of 7.9% computed from the μMRI data overestimates the FV due to partial volume effects. Although μMRI does not have the ability to resolve cortical capillaries, it is capable of “wide-area” or “whole brain” mapping of the neurovasculature. While it does offer superior coverage compared to most optical techniques, μMRI cannot outperform μCT in terms of spatial resolution. The advantage of μMRI is that one can employ complementary contrast mechanisms such as DTI, to simultaneously measure changes in the brain's microenvironment.

Recent evidence has emerged that antiangiogenic therapy in brain tumors promotes an invasive phenotype [31], [32]. This has created an urgent need for noninvasive methods to characterize the angiogenesis-invasion nexus and the efficacy of combined antiangiogenic/anti-invasive therapies in brain tumor models. Here, we demonstrated the feasibility of imaging changes in the whole brain microenvironment induced by both, invasive and non-invasive brain tumor models. While others have characterized brain tumor induced alterations in the brain's cytoarchitecture with μMRI [33], simultaneous imaging of the whole-brain vasculature has not been reported. It is this ability of μMRI to simultaneously visualize brain tumor vascularization and white matter remodeling over the entire murine brain that makes it a powerful new tool for understanding the interplay between angiogenesis and invasion in pre-clinical brain tumor models. In a recent study, we successfully demonstrated the ability of μMRI to phenotype global and zonal changes in the brain tumor microenvironment with tumor progression [34].

The evolution in our understanding of brain tumor angiogenesis has been the result of pioneering studies spanning the endothelial cell [35], microvasculature [36] and tissue level [37]. Many of these primary data on angiogenesis are in the form of images from pre-clinical models that provide a wealth of qualitative and quantitative information in many dimensions, and across different spatial scales. However, simultaneously visualizing changes in the complex angiogenic microenvironment at different spatial scales remains a challenge due to the lack of integration between micro- and macroscopic imaging data and the difficulty in obtaining such data from patients. Therefore, “mesoscopic” μMRI could be an expedient tool for integrating micro- and macroscopic angiogenesis imaging data. Here, we showed an example in which co-registered FV maps computed from ex vivo μMRI data were employed to validate in vivo macroscopic blood volume measurements. This imaging platform could easily be extended to establish a toolbox for integrating multi-scale data on brain tumor angiogenesis.

Our approach for μMRI imaging of the neurovasculature has several widespread applications: integration with extant MRI brain atlases, incorporation in biophysical models of MR contrast mechanisms, co-registration of in vivo imaging data with gene/protein data from immunohistochemistry, fluid dynamic simulations of tumor blood flow, multi-scale mathematical models of angiogenesis, and phenotyping pathological samples. The advantages and ease-of-implementation of vascular μMRI make it an indispensable tool for the study of neuropathologies that involve an aberrant neurovasculature.