Micro-computed tomography in murine models of cerebral cavernous malformations as a paradigm for brain disease.

Published

Journal Article

BACKGROUND:Cerebral cavernous malformations (CCMs) are hemorrhagic brain lesions, where murine models allow major mechanistic discoveries, ushering genetic manipulations and preclinical assessment of therapies. Histology for lesion counting and morphometry is essential yet tedious and time consuming. We herein describe the application and validations of X-ray micro-computed tomography (micro-CT), a non-destructive technique allowing three-dimensional CCM lesion count and volumetric measurements, in transgenic murine brains. NEW METHOD:We hereby describe a new contrast soaking technique not previously applied to murine models of CCM disease. Volumetric segmentation and image processing paradigm allowed for histologic correlations and quantitative validations not previously reported with the micro-CT technique in brain vascular disease. RESULTS:Twenty-two hyper-dense areas on micro-CT images, identified as CCM lesions, were matched by histology. The inter-rater reliability analysis showed strong consistency in the CCM lesion identification and staging (K=0.89, p<0.0001) between the two techniques. Micro-CT revealed a 29% greater CCM lesion detection efficiency, and 80% improved time efficiency. COMPARISON WITH EXISTING METHOD:Serial integrated lesional area by histology showed a strong positive correlation with micro-CT estimated volume (r(2)=0.84, p<0.0001). CONCLUSIONS:Micro-CT allows high throughput assessment of lesion count and volume in pre-clinical murine models of CCM. This approach complements histology with improved accuracy and efficiency, and can be applied for lesion burden assessment in other brain diseases.

Full Text

Duke Authors

Cited Authors

  • Girard, R; Zeineddine, HA; Orsbon, C; Tan, H; Moore, T; Hobson, N; Shenkar, R; Lightle, R; Shi, C; Fam, MD; Cao, Y; Shen, L; Neander, AI; Rorrer, A; Gallione, C; Tang, AT; Kahn, ML; Marchuk, DA; Luo, Z-X; Awad, IA

Published Date

  • September 2016

Published In

Volume / Issue

  • 271 /

Start / End Page

  • 14 - 24

PubMed ID

  • 27345427

Pubmed Central ID

  • 27345427

Electronic International Standard Serial Number (EISSN)

  • 1872-678X

International Standard Serial Number (ISSN)

  • 0165-0270

Digital Object Identifier (DOI)

  • 10.1016/j.jneumeth.2016.06.021

Language

  • eng