Near infrared (NIR) multispectral imaging is a novel noninvasive technique that maps and quantifies dental caries. The technique has the ability to reduce the confounding effect of stain present on teeth. The aim of this study was to develop and validate a quantitative NIR multispectral imaging system for caries detection and assessment against a histological reference standard. The proposed technique is based on spectral imaging at specific wavelengths in the range from 1000 to 1700 nm. A total of 112 extracted teeth (molars and premolars) were used and images of occlusal surfaces at different wavelengths were acquired. Three spectral reflectance images were combined to generate a quantitative lesion map of the tooth. The maximum value of the map at the corresponding histological section was used as the NIR caries score. The NIR caries score significantly correlated with the histological reference standard (Spearman's Coefficient = 0.774, p<0.01). Caries detection sensitivities and specificities of 72% and 91% for sound areas, 36% and 79% for lesions on the enamel, and 82% and 69% for lesions in dentin were found. These results suggest that NIR spectral imaging is a novel and promising method for the detection, quantification, and mapping of dental caries.
Near-infrared (NIR) is preferred for caries detection compared to visible light imaging because it exhibits low absorption by stain and deeper penetration into teeth. Hyperspectral images from 1000 to 2500 nm have been obtained for a total of 12 extracted teeth (premolars and molars) with different degrees of natural lesion. Analysis of the reflectance spectra suggests that light scattering by porous enamel and absorption by water in dentin can be used to quantify the lesion severity and generate a NIR caries score. Teeth were ground for histological examination after the measurements. The NIR caries score obtained correlates significantly (Spearman's correlation of 0.89, p<0.01) with the corresponding histological score. Results yield a sensitivity of >99% and a specificity of 87.5% for enamel lesions and a sensitivity of 80% and a specificity >99% for dentine lesions. The nature of the technique offers significant advantages, including the ability to map the lesion distribution rather than obtaining single-point measurements, it is also noninvasive, noncontact, and stain insensitive. These results suggest that NIR spectral imaging is a potential clinical technique for quantitative caries diagnosis and can determine the presence of occlusal enamel and dentin lesions.
Erythema is a reaction of the skin and oral soft tissues commonly associated with inflammation and an increase in blood flow. Diffuse reflection spectroscopy is a powerful tool for the assessment of skin inflammation where erythema has been linked to the relative concentration of oxygenated hemoglobin and blood perfusion. Here we demonstrate the applicability of a spectral imaging method for the quantification of gingival inflammation by looking at the gingival margin and papillary tip erythema. We present a longitudinal study on 22 healthy volunteers divided in two groups. The first was allowed to have normal oral hygiene and the second was subjected to an induced gingivitis for two weeks by cessation of oral hygiene. The spectral reflectance ratio at 615 and 460 nm, R(615)/R(460), was proposed as a method to quantify and map the erythema spatial distribution. These wavelengths represent spectral absorption crossovers observed between oxygenated and deoxygenated hemoglobin. The spectral method presented shows a significant separation (p<0.01) between the groups when gingivitis was induced and correlates significantly (p<0.05) with the clinical gingival index scores. We believe that these investigations could contribute to the development of functional imaging methods for periodontal disease detection and monitoring.
The study presented demonstrates the ability of the self-mixing interferometry configuration with a laser diode
to measure flow through scattering media. A flow model has been used based on a 1.5mm-diameter tube covered
with layers of scattering media. The laser intensity power spectrum has been obtained when changing the flow
value and the thickness of the layer placed on top of the tube. Different data processing algorithms have been
compared including first moment, normalized first moment, RMS, and Lorentzian and exponential curve fitting
parameters. It was found that an exponential curve in semi-logarithm scale well describe the spectrum and can
be best used when monitoring flow under layers of up to 1.1mm in thickness.