In the early 1990s, researchers developed a technique called optical coherence tomography (OCT). Tomography means taking a 2-dimensional image of a 3-dimensional tissue. By scanning an OCT device across a tissue, say the retina, it is possible to measure the resulting optical reflection and thus detect a refractive change within the tissue that is indicative of developing disease. Over the past decade, scientists have adapted polarization sensitive (or PS, meaning reflection from the ground is eliminated) OCT to dental enamel and the pit-and-fissure topography of the tooth. The hope is the greater sensitivity of PS-OCT will allow for earlier detection of mineral loss, better characterization of incipient lesions, and precision in defining the exact depth and dimensions of established decay. Recently, National Institute of Dental and Craniofacial Research (NIDCR) grantees reported preliminary success integrating near-infrared imaging and PS-OCT with an automated, computer-guided laser ablation system. The result: A high-contrast geometric image of the decayed surface and enamel-sparring precision to remove it. Now, as Le, et al have published in the January issue of Lasers in Surgery and Medicine, the scientists have developed 2 automated methods to allow PS-OCT to assess the depth and severity of mineral loss within seconds. In the study, the scientists used 10 extracted teeth that were in various phases of decay. The automated methods demonstrated they could accurately detect differences in lesion depth from tooth to tooth and differentiate between healthy and decayed enamel. In previous PS-OCT studies, the severity of lesion areas was accessed by manual integration of single (one-dimensional) a-scans, which is slow and labor intensive.
“Better discrimination of lesion areas is possible if the entire lesion cross-section area or volume is integrated…since such lesions are seldom uniform,” noted the authors. “Since this approach involves large volumes of data, it is only practical with the development of algorithms for automated processing,” which the scientists developed to power this study.
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