Ultrasound Characterization of Oral Soft Tissues in vivo Using the Burr Speckle Model

Abstract

Periodontal (gum) diseases, reportedly affect 4 out of 10 adults 30 years of age or older in the USA. The standard of care for clinical assessments of these diseases is bleeding on probing, which is invasive, subjective and semi-qualitative. Thus, research on proposing alternative 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 (power-law b and scale factor 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 of all four oral quadrants in each swine, resulting in 80 scans. US scan data was acquired at the transit/receive frequency of 24 MHz using a toothbrush-sized transducer. Parametric imaging of Burr parameters was created using a sliding kernel method with linear interpolations. The kernel size and overlap ratio was 10 wavelengths and 70%, respectively. No statistically significant difference was reported for estimated parameters when interpolation was performed (p-value>0.01). Results at both oral sites (molar 1 and molar 2) showed that the difference between the two tissue types using Burr parameters were statistically significant (p-value<0.0001). The average Burr b was reported to be higher in attached gingiva while the average Burr l was lower compared to mucosa. Visual comparison of Masson Trichrome histology images of these tissues showed denser color density in gingiva. The color thresholding of these images further confirmed that the percent of blue, which stains collagenous regions, was at least two times higher in gingiva than alveolar mucosa, based on threshold values. Comparing histology and QUS in characterizing the two tissue types, it was suggested that the elevated Burr b (related to potential scatterer densities) in gingiva could be aligned with findings from Masson's Trichrome histology. This study showed a promising potential of QUS for periodontal soft tissue characterization.

Keywords: Burr model; color thresholding; histology; periodontal soft tissues; quantitative ultrasound; speckle statistics.

Fig. 1.

A schematic illustration of oral soft and hard tissues.

Fig. 2.

Parametric imaging of Burr b for a phantom scan. (a) without interpolations. (b) with the linear interpolation.

Fig 3.

QQ plots comparing the effect of interpolation on parametric imaging in a phantom scan. (a) Burr b and (b): Burr l.

Fig. 4.

(a) US image of periodontal tissues at left mandibular molar 2 site. (b) and (c) shows parametric images of Burr b and Burr l, respectively.

Fig. 5.

Comparison of gingiva versus alveolar mucosa using Burr parameters (b and l) for two oral sites of molar 1 ((a) and (b)) and molar 2 ((c) and (d)). P-values are also reported.

Fig. 6.

(a) Masson’s Trichrome histology image of swine oral tissues. (b) and (c): high-resolution microscopy imaging of gingiva and mucosa, respectively.

Fig. 7.

Comparing gingiva (first column) vs. mucosa (third column) histology images using RGB thresholding. Threshold values: top row: 100, bottom row: 140. The second and fourth columns show blue channel masks for segmenting gingival and mucosal collagenous regions, respectively. % of blue obtained from RGB thresholding are also reported. ROIs from Fig. 6 are used.