Reduction of Angularly-Varying-Data Truncation in C-Arm CBCT Imaging.

C-arm cone-beam computed tomography (CBCT) has been used increasingly as an imaging tool for yielding 3D anatomical information about the subjects in surgical and interventional procedures. In the clinical applications, the limited field-of-view (FOV) of C-arm CBCT can lead to significant data truncation, resulting in image artifacts that can obscure low contrast tumor embedded within soft-tissue background, thus limiting the utility of C-arm CBCT. The truncation issue can become serious as most of the surgical and interventional procedures would involve devices and tubes that are placed outside the FOV of C-arm CBCT and thus can engender angularly-varying-data truncation. Existing methods may not be adequately applicable to dealing with the angularly-varying truncation. In this work, we seek to reduce truncation artifacts by tailoring optimization-based reconstruction directly from truncated data, without performing pre-reconstruction data compensation, collected from physical phantoms and human subjects. The reconstruction problem is formulated as a constrained optimization program in which a data-derivative-ℓ2-norm fidelity is included for effectively suppressing image artifacts caused by the angularly-varying-data truncation, and the generic Chambolle-Pock algorithm is tailored to solve the optimization program. The results of the study suggest that an appropriately designed optimization-based reconstruction can be exploited for yielding images with reduced artifacts caused by angularly-varying-data truncation.

In vitro evaluation of new approach to digital dental model articulation.

PURPOSE: The purpose of the present study was to evaluate the accuracy of our newly developed approach to digital dental model articulation. MATERIALS AND METHODS: Twelve sets of stone dental models from patients with craniomaxillofacial deformities were used for validation. All the models had stable occlusion and no evidence of early contact. The stone models were hand articulated to the maximal intercuspation (MI) position and scanned using a 3-dimensional surface laser scanner. These digital dental models at the MI position served as the control group. To establish an experimental group, each mandibular dental model was disarticulated from its original MI position to 80 initial positions. Using a regular office personal computer, they were digitally articulated to the MI position using our newly developed approach. These rearticulated mandibular models served as the experimental group. Finally, the translational, rotational, and surface deviations in the mandibular position were calculated between the experimental and control groups, and statistical analyses were performed. RESULTS: All the digital dental models were successfully articulated. Between the control and experimental groups, the largest translational difference in mandibular position was within 0.2 mm ± 0.6 mm. The largest rotational difference was within 0.1° ± 1.1°. The averaged surface deviation was 0.08 ± 0.07. The results of the Bland and Altman method of assessing measurement agreement showed tight limits for the translational, rotational, and surface deviations. In addition, the final positions of the mandibular articulated from the 80 initial positions were absolutely agreed on. CONCLUSION: The results of our study have demonstrated that using our approach, the digital dental models can be accurately and effectively articulated to the MI position. In addition, the 3-dimensional surface geometry of the mandibular teeth played a more important role in digital dental articulation than the initial position of the mandibular teeth.

An automatic and robust algorithm of reestablishment of digital dental occlusion.

In the field of craniomaxillofacial (CMF) surgery, surgical planning can be performed on composite 3-D models that are generated by merging a computerized tomography scan with digital dental models. Digital dental models can be generated by scanning the surfaces of plaster dental models or dental impressions with a high-resolution laser scanner. During the planning process, one of the essential steps is to reestablish the dental occlusion. Unfortunately, this task is time-consuming and often inaccurate. This paper presents a new approach to automatically and efficiently reestablish dental occlusion. It includes two steps. The first step is to initially position the models based on dental curves and a point matching technique. The second step is to reposition the models to the final desired occlusion based on iterative surface-based minimum distance mapping with collision constraints. With linearization of rotation matrix, the alignment is modeled by solving quadratic programming. The simulation was completed on 12 sets of digital dental models. Two sets of dental models were partially edentulous, and another two sets have first premolar extractions for orthodontic treatment. Two validation methods were applied to the articulated models. The results show that using our method, the dental models can be successfully articulated with a small degree of deviations from the occlusion achieved with the gold-standard method.