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Periodontal diseases, reportedly affect 4 out of 10 adults in the USA. The standard of care for clinical assessments of these diseases is invasive, subjective and semi-qualitative. Thus, research on proposing alternate noninvasive modalities for clinical assessments of periodontal tissues is crucial. Quantitative Ultrasound (QUS) has shown promises in noninvasive assessments of various diseases in soft biological tissues; however, it has not been employed in periodontology. Here as the first step, we focused on QUS-based characterization of two very adjacent oral soft tissues of alveolar mucosa and attached gingiva in an in vivo animal study. We investigated first order ultrasonic speckle statistics using the two-parameter Burr model (b and l). Our QUS analysis was compared with the Masson$'$s Trichrome histology images of the two oral tissue types quantitatively using the RGB color thresholding. QUS study included 10 swine and US scanning was performed at the first and second molars, resulting in 80 scans. US scan data was acquired at the frequency of 24 MHz. Results at both oral sites (molar 1 and molar 2) showed that difference between the two tissue types using Burr parameters were statistically significant (p$<0.0001$). The average Burr b was reported higher in attached gingiva while the average Burr l was lower compared to mucosa. The color thresholding of these images confirmed that the percent of blue, which stains collagenous regions, was at least two times higher in gingiva compared with alveolar mucosa. It was suggested that the elevated Burr b (related to potential scatterer densities) in gingiva could be aligned with findings from histology. This study showed a promising potential of QUS for clinical assessment of periodontal soft tissues.

Periodontal diseases affect 45.9\% of adults aged 30 or older in the United States. Current diagnostic methods for clinical assessment of these diseases are visual examination and bleeding on probing that are subjective, qualitative, and/or invasive. Thus, there is a critical need for research on noninvasive modalities for periodontal tissue characterization. Quantitative Ultrasound (QUS) has shown promising results in noninvasive characterization of various soft tissues; however, it has not been used in periodontics. This study is among initial investigations into the application of QUS for periodontal tissue characterization in the literature. Here, QUS analysis of oral soft tissues (alveolar mucosa and gingiva) is performed in an in vivo animal study including 10 swine. US scanning was performed at the first molar of all four oral quadrants, resulting in a total of 40 scans. We investigated first order speckle statistics of oral tissues by using the two-parameter Burr (power-law b and scale factor l) and Nakagami models (shape factor m and scale factor $\alpha$). Parametric imaging of these parameters was created using a sliding kernel method sweeping regions of interest with a kernel size of 10 wavelengths from a phantom study. Results showed that the difference between gingiva and alveolar mucosa were statistically significant using Burr and Nakagami parameters ($p-value<0.0001$). The Burr b and Nakagami m were higher in gingiva while the Burr l and Nakagami {$\alpha$} were higher in alveolar mucosa. Findings from QUS analyses agreed with observation from histology that showed denser stains for gingiva. Linear classifications of these tissues using 2D parameter spaces of the Burr and Nakagami models resulted in a segmentation accuracy of 93.51\% and 90.91\%, respectively. We propose that QUS holds promising potentials as an augmented tool for disease diagnosis in periodontology.