“There has been very limited data available to inform detailed protocols for staining large specimens using diceCT. In our study, we systematically assessed the efficacy and mechanism of diffusion-based contrast-enhanced X-ray Computed Tomography (CT) with serial experiments and the validation of numerical modeling. We have applied a Diffusion-Sorption model to explain the CT contrast increasing pattern within the cranial tissues of a goose specimen over a long staining duration. We identified attributes of different tissues that affect the effective diffusion rate and staining efficacy, including partition coefficient, bulk density and tissue porosity. Based on our results, specific protocols—customized by tissue size and type—can be designed for diceCT to maximize visualization of these soft tissues contrasts.”
Comparisons of MRI to diceCT mouse brains (left), showing displacement heat maps (right). Gray areas in the heat map indicate regions of large differences (>0.5 mm) either due to extreme shrinkage or difference in segmentation.
“Phenotypic screens for brain defects traditionally used either histology or high-resolution magnetic resonance imaging (MRI) to look for structural abnormalities. The former is tedious and slow, while the latter is expensive. By coupling the excellent contrast offered by the iodine with the tissue preserving properties of hydrogel, we demonstrate that whole mouse brains can be effectively and rapidly imaged by μCT at a fraction of a cost of high-resolution MRI. Our methodology is best suited for rapid phenotyping applications where large numbers of samples need to be screened for gross differences in the overall brain shape using computational morphometric approaches.”