OCT-Based Periodontal Inspection Framework.

Periodontal diagnosis requires discovery of the relations among teeth, gingiva (i.e., gums), and alveolar bones, but alveolar bones are inside gingiva and not visible for inspection. Traditional probe examination causes pain, and X-ray based examination is not suited for frequent inspection. This work develops an automatic non-invasive periodontal inspection framework based on gum penetrative Optical Coherence Tomography (OCT), which can be frequently applied without high radiation. We sum up interference responses of all penetration depths for all shooting directions respectively to form the shooting amplitude projection. Because the reaching interference strength decays exponentially with tissues' penetration depth, this projection mainly reveals the responses of the top most gingiva or teeth. Since gingiva and teeth have different air-tissue responses, the gumline, revealing itself as an obvious boundary between teeth and gingiva, is the basis line for periodontal inspection. Our system can also automatically identify regions of gingiva, teeth, and alveolar bones from slices of the cross-sectional volume. Although deep networks can successfully and possibly segment noisy maps, reducing the number of manually labeled maps for training is critical for our framework. In order to enhance the effectiveness and efficiency of training and classification, we adjust Snake segmentation to consider neighboring slices in order to locate those regions possibly containing gingiva-teeth and gingiva-alveolar boundaries. Additionally, we also adapt a truncated direct logarithm based on the Snake-segmented region for intensity quantization to emphasize these boundaries for easier identification. Later, the alveolar-gingiva boundary point directly under the gumline is the desired alveolar sample, and we can measure the distance between the gumline and alveolar line for visualization and direct periodontal inspection. At the end, we experimentally verify our choice in intensity quantization and boundary identification against several other algorithms while applying the framework to locate gumline and alveolar line in vivo data successfully.

Interactive OCT-Based Tooth Scan and Reconstruction.

Digital dental reconstruction can be a more efficient and effective mechanism for artificial crown construction and period inspection. However, optical methods cannot reconstruct those portions under gums, and X-ray-based methods have high radiation to limit their applied frequency. Optical coherence tomography (OCT) can harmlessly penetrate gums using low-coherence infrared rays, and thus, this work designs an OCT-based framework for dental reconstruction using optical rectification, fast Fourier transform, volumetric boundary detection, and Poisson surface reconstruction to overcome noisy imaging. Additionally, in order to operate in a patient's mouth, the caliber of the injector is small along with its short penetration depth and effective operation range, and thus, reconstruction requires multiple scans from various directions along with proper alignment. However, flat regions, such as the mesial side of front teeth, may not have enough features for alignment. As a result, we design a scanning order for different types of teeth starting from an area of abundant features for easier alignment while using gyros to track scanned postures for better initial orientations. It is important to provide immediate feedback for each scan, and thus, we accelerate the entire signal processing, boundary detection, and point-cloud alignment using Graphics Processing Units (GPUs) while streamlining the data transfer and GPU computations. Finally, our framework can successfully reconstruct three isolated teeth and a side of one living tooth with comparable precisions against the state-of-art method. Moreover, a user study also verifies the effectiveness of our interactive feedback for efficient and fast clinic scanning.