Mucosal maps of the canine nasal cavity: Micro-computed tomography and histology

Nasal turbinals, delicate and complex bones of the nasal cavity that support respiratory or olfactory mucosa (OM), are now easily studied using high resolution micro-computed tomography (mu-CT). Standard mu-CT currently lacks the capacity to identify OM or other mucosa types without additional radio-opaque staining techniques. However, even unstained mucosa is more radio-opaque than air, and thus mucosal thickness can be discerned. Here, we assess mucosal thickness of the nasal fossa using the cranium of a cadaveric adult dog that was mu-CT scanned with an isotropic resolution of 30 mu m, and subsequently histologically sectioned and stained. After co-alignment of mu-CT slice planes to that of histology, mucosal thickness was estimated at four locations. Results based on either mu-CT or histology indicate olfactory mucosa is thicker on average compared with non-olfactory mucosa (non-OM). In addition, olfactory mucosa has a lesser degree of variability than the non-OM. Variability in the latter appears to relate mostly to the varying degree of vascularity of the lamina propria. Because of this, in structures with both specialized vascular respiratory mucosa and OM, such as the first ethmoturbinal (ET I), the range of thickness of OM and non-OM may overlap. Future work should assess the utility of diffusible iodine-based contrast enhanced CT techniques, which can differentiate epithelium from the lamina propria, to enhance our ability to differentiate mucosa types on more rostral ethmoturbinals. This is especially critical for structures such as ET I, which have mixed functional roles in many mammals.

Automated summary

The study assessed mucosal thickness of the nasal fossa using high resolution mu-CT and histology in a cadaveric adult dog, finding that olfactory mucosa is thicker on average compared with non-olfactory mucosa and has lesser variability. Future work could explore the utility of diffusible iodine-based contrast enhanced CT techniques to differentiate mucosa types more effectively.