A quantitative X-ray microcomputed tomography workflow for dentoalveolar tissue analysis of soft and hard tissues in...

Dentoalveolar tissues in the maxilla and mandible combine mineralized and nonmineralized components optimized for mastication. Such a combination of hard and soft tissues is difficult to simultaneously visualize and quantify by X-ray microcomputed tomography (μCT). While μCT provides excellent high-resolution, nondestructive imaging of mineralized structures, soft tissues such as predentin, pulp, and periodontal ligament provide only low X-ray attenuation (low contrast) and are often regretfully excluded from analyses. In addition, the relatively high water content of soft tissues further reduces image contrast. Whereas some contrast-enhancing approaches have been used for soft tissues, these may alter mineralized tissues. Here, we present a reproducible μCT-based workflow integrating iodine stain contrast enhancement combined with dehydration by critical-point drying, and hybrid segmentation for 3D multiscale analysis of mouse hemimandibles. Segmentation is then combined with intensity-based analysis methods using deep learning-assisted refinement (U-Net). The workflow facilitates simultaneous visualization and quantification of both mineralized and soft tissues, including enamel, dentin, predentin, pulp, cementum, mandibular and alveolar bone, and periodontal ligament. Quantitative outputs include volumetric measurements, thickness mapping, and normalized tissue fractions, among other metrics. The workflow applied to the Hyp mouse model of X-linked hypophosphatemia effectively detected established alterations in pulp morphology in pathologic mineralized tissues. This imaging and quantitative workflow establishes a reproducible and scalable framework for simultaneous multi-tissue dentoalveolar analysis, which can particularly enable quantitative comparisons between physiologic and pathologic spontaneous and transgenic mouse models of disease.