The aim of this study was to verify the feasibility and accuracy of a contour registration-based augmented reality (AR) system in jaw surgery. An AR system was developed to display the interaction between virtual planning and images of the surgical site in real time. Several trials were performed with the guidance of the AR system and the surgical guide. The postoperative cone beam CT (CBCT) data were matched with the preoperatively planned data to evaluate the accuracy of the system by comparing the deviations in distance and angle. All procedures were performed successfully. In nine model trials, distance and angular deviations for the mandible, reconstructed fibula, and fixation screws were 1.62 ± 0.38 mm, 1.86 ± 0.43 mm, 1.67 ± 0.70 mm, and 3.68 ± 0.71°, 5.48 ± 2.06°, 7.50 ± 1.39°, respectively. In twelve animal trials, results of the AR system were compared with the surgical guide. Distance deviations for the bilateral condylar outer poles were 0.93 ± 0.63 mm and 0.81 ± 0.30 mm, respectively (p = 0.68). Distance deviations for the bilateral mandibular posterior angles were 2.01 ± 2.49 mm and 2.89 ± 1.83 mm, respectively (p = 0.50). Distance and angular deviations for the mandible were 1.41 ± 0.61 mm, 1.21 ± 0.18 mm (p = 0.45), and 6.81 ± 2.21°, 6.11 ± 2.93° (p = 0.65), respectively. Distance and angular deviations for the reconstructed tibiofibular bones were 0.88 ± 0.22 mm, 0.84 ± 0.18 mm (p = 0.70), and 6.47 ± 3.03°, 6.90 ± 4.01° (p = 0.84), respectively. This study proposed a contour registration-based AR system to assist surgeons in intuitively observing the surgical plan intraoperatively. The trial results indicated that this system had similar accuracy to the surgical guide.
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http://dx.doi.org/10.1016/j.jcms.2023.05.007 | DOI Listing |
Clin Transl Oncol
January 2025
Department of Radiation Oncology, HM Hospitales, Madrid, Spain.
Introduction: SRS for the treatment of limited brain metastases (BM) is widely accepted, but there are still limitations in the management of numerous BM. Frameless single-isocenter multitarget SRS is a novel technique that allows for rapid treatment delivery to multiple BM. We report our preliminary clinical, dosimetric, and patient´s shifts outcomes with this technique.
View Article and Find Full Text PDFBioengineering (Basel)
August 2024
Department of Stomatology, the Fourth Medical Center, Chinese PLA General Hospital, 51 Fucheng Road, Haidian District, Beijing 100048, China.
Background: Intraoral scans (IOS) provide precise 3D data of dental crowns and gingival structures. This paper explores an application of IOS in human identification.
Methods: We propose a dental biometrics framework for human identification using 3D dental point clouds based on machine learning-related algorithms, encompassing three stages: data preprocessing, feature extraction, and registration-based identification.
Phys Med Biol
May 2024
Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia.
Fast and accurate deformable image registration (DIR), including DIR uncertainty estimation, is essential for safe and reliable clinical deployment. While recent deep learning models have shown promise in predicting DIR with its uncertainty, challenges persist in proper uncertainty evaluation and hyperparameter optimization for these methods. This work aims to develop and evaluate a model that can perform fast DIR and predict its uncertainty in seconds.
View Article and Find Full Text PDFRadiother Oncol
May 2024
Department of Medical Physics, Leeds Teaching Hospitals NHS Trust, Leeds, UK; Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, UK.
Background And Purpose: Safe reirradiation relies on assessment of cumulative doses to organs at risk (OARs) across multiple treatments. Different clinical pathways can result in inconsistent estimates. Here, we quantified the consistency of cumulative dose to OARs across multi-centre clinical pathways.
View Article and Find Full Text PDFPhys Eng Sci Med
June 2024
Department of Radiation Oncology, Washington University, 63110, St. Louis, MO, USA.
MRI-guided radiotherapy systems enable beam gating by tracking the target on planar, two-dimensional cine images acquired during treatment. This study aims to evaluate how deep-learning (DL) models for target tracking that are trained on data from one fraction can be translated to subsequent fractions. Cine images were acquired for six patients treated on an MRI-guided radiotherapy platform (MRIdian, Viewray Inc.
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