Intraoral ultrasonography image registration for evaluation of partial edentulous ridge: A methodology and validation...

Ultrasound (US) reveals details for diagnosing soft- and hard-tissue dimensions around teeth, implants, and the edentulous ridge, not seen in 2D radiographs. Co-registering free-hand US scans with other 3D modalities presents reliability challenges. This study first aims to develop and validate a registration method to longitudinally reproduce US images of the jawbone on a simulator. In addition, it also evaluates the degree of the anatomical match in humans between US images acquired by the proposed registration method and the commonly used freehand acquisitions in comparison to cone beam computed tomography (CBCT) and intra-oral optical scan (IOS), used as references.

A previously introduced ultrasound phantom was employed as a CBCT-US hybrid, suitable for training and technique development of US guides in edentulous ridges. After establishing feasibility in the phantom, the methodology was validated in a cohort of 24 human subjects (26 cases). Soft tissues were delineated on US and IOS, and hard tissues on US and CBCT. US accuracy and repeatability from both guided and freehand scans (non-guided) was assessed as the average distance between US and the references.

Guided US images resembled the references more closely than freehand (non-guided) scans. Notably, delineation of soft and hard tissues was significantly more accurate when employing guides. In the phantom, guided scans exhibited an absolute mean deviation of 81.8 µm for gingiva and 90.4 µm for bone, whereas non-guided scans showed deviations of 150.4 µm and 177.2 µm, respectively. Similarly, in vivo, guided US outperformed non-guided US, with gingiva deviations of 125 µm and 196 µm, and bone deviations of 354 µm and 554 µm, respectively.

By using a registration method, guided US scans improved repeatability and accuracy of mapping hard and soft tissue of the edentulous ridge when compared to non-guided scans.

This guided US imaging method could lay the foundation for longitudinal evaluation of tissue behavior and dimensional changes with improved accuracy.