Vascular phenotyping of brain tumors using magnetic resonance microscopy (μMRI)

Abstract

Abnormal vascular phenotypes have been implicated in neuropathologies ranging from Alzheimer's disease to brain tumors. The development of transgenic mouse models of such diseases has created a crucial need for characterizing the murine neurovasculature. Although histologic techniques are excellent for imaging the microvasculature at submicron resolutions, they offer only limited coverage. It is also challenging to reconstruct the three-dimensional (3D) vasculature and other structures, such as white matter tracts, after tissue sectioning. Here, we describe a novel method for 3D whole-brain mapping of the murine vasculature using magnetic resonance microscopy (μMRI), and its application to a preclinical brain tumor model. The 3D vascular architecture was characterized by six morphologic parameters: vessel length, vessel radius, microvessel density, length per unit volume, fractional blood volume, and tortuosity. Region-of-interest analysis showed significant differences in the vascular phenotype between the tumor and the contralateral brain, as well as between postinoculation day 12 and day 17 tumors. These results unequivocally show the feasibility of using μMRI to characterize the vascular phenotype of brain tumors. Finally, we show that combining these vascular data with coregistered images acquired with diffusion-weighted MRI provides a new tool for investigating the relationship between angiogenesis and concomitant changes in the brain tumor microenvironment.

Figure 1

Image processing flowchart depicting an axial slice of a D12 brain. (A) First-echo MGE image of a D12 brain. (B) Output of the ‘tubeness' filter in red overlaid on the original image. (C) Three-dimensional binary vascular structure after thresholding, filtering, and resampling. (D) Maximum intensity projection (MIP) of the skeletonized vascular structure. (E) Three-dimensional EDM map showing the minimum distance in μm between each vessel voxel and the background. The color reproduction of this figure is available on the html full text version of the manuscript. D12, postinoculation day 12; MGE, multiple gradient echo.

Figure 2

Raw data and final results of image processing for representative D12 and D17 brains. Axial slices of the raw first-echo MGE μMRI data of a (A) D12 and (B) D17 brain. Tumor and contralateral ROIs are overlaid in red and green, respectively. (C, D) Three-dimensional vascular structures color coded by vessel radius and volume rendered ROIs corresponding to the μMRI data in panels A and B. D12, postinoculation day 12; D17, postinoculation day 17; MGE, multiple gradient echo;μMRI, micro-magnetic resonance imaging; ROI, region of interest.

Figure 3

Box and whisker plots of six vascular and two diffusion parameters computed for D12 contralateral, D12 tumor, D17 contralateral, and D17 tumor ROIs (n=5 for all groups, ^*P<0.05). (A) The median vessel length, (B) average vessel radius, (C) MVD, (D) L_V, (E) FV, (F) median vessel tortuosity (G) ADC, and (H) FA. ADC, apparent diffusion coefficient; D12, postinoculation day 12; D17, postinoculation day 17; FA, fractional anisotropy; FV, fractional vascular volume; L_V, length per unit volume; MVD, microvessel density; ROI, region of interest.

Figure 4

ROI analysis of tumor ADC and FA. Coronal slices of the ADC and FA maps of representative D12 (A, B) and D17 (C, D) tumor-bearing brains. The contralateral ROI is outlined in green; the tumor core, intermediate (Inter), and rim zones are outlined in red. Box and whisker plots of D12 tumor (E) ADC, (F) FA, and D17 tumor (G) ADC, and (H) FA by zone (n=5 for all groups, ^*P<0.05): core, intermediate, and rim. Zones were defined by performing sequential 3D morphologic erosions. The color reproduction of this figure is available on the html full text version of the manuscript. ADC, apparent diffusion coefficient; D12, postinoculation day 12; D17, postinoculation day 17; FA, fractional anisotropy; ROI, region of interest; 3D, three dimensional.

Figure 5

Box and whisker plots of select morphometric vascular parameters for D12 and D17 tumors by zone (n=5 for all groups, ^*P<0.05): core, intermediate (Inter), and rim. Zones were defined by performing sequential 3D morphologic erosions. D12 tumor (A) MVD, (B) L_V and (C) FV. D17 (D) MVD, (E) L_V and (F) FV. D12, postinoculation day 12; D17, postinoculation day 17; FV, fractional vascular volume; L_V, length per unit volume; MVD, microvessel density.

Figure 6

Double dendrogram showing the results of the unsupervised hierarchical clustering analysis. The clustering variables are organized by rows, and brain ROIs by columns. Based on ADC, FA, and six vascular morphometric parameters, the clustering algorithm separated contralateral from tumor, and ordered all 20 ROIs correctly by group (D12 contralateral, D17 contralateral, D12 tumor, D17 tumor), with D12 contralateral and D17 tumor being farthest apart in parameter space. ADC, apparent diffusion coefficient; D12, postinoculation day 12; D17, postinoculation day 17; FA, fractional anisotropy; FV, fractional vascular volume; L_V, length per unit volume; MVD, microvessel density; ROI, region of interest.

Figure 7

Comparison of histology and μMRI vascular and diffusion data. Representative histology of (A) D12 and (B) D17 9L tumor-bearing mouse brains. Nuclei are visible in blue, and microfilled vessels in green. (C, D) Corresponding slices of μMRI MGE images. Tumor and contralateral ROIs are shown in red and blue, respectively. Segmented vessels are shown in green. (E–H) Complementary diffusion data inherently coregistered to the vascular data. (Panels E and F) ADC and (panels G and H) FA maps of the slices shown in panels C and D), respectively, overlaid with 3D renderings of coregistered vasculature in green. Gray scale bars are omitted for clarity and are the same as shown in Figure 4. ADC, apparent diffusion coefficient; D12, postinoculation day 12; D17, postinoculation day 17; FA, fractional anisotropy; MGE, multiple gradient echo; μMRI, micro-magnetic resonance imaging; ROI, region of interest; 3D, three dimensional.