Ulrike Gerken1, Felix Esser2, Stephan C Möhlhenrich3,4, Alexander K Bartella5, Frank Hölzle3, Horst Fischer2, Stefan Raith3,2, Timm Steiner3. 1. Department of Oral and Maxillofacial Surgery, RWTH Aachen University Hospital, Pauwelsstraße 30, 62074, Aachen, Germany. ulli.gerken@gmail.com. 2. Department of Dental Materials and Biomaterials Research, RWTH Aachen University Hospital, Pauwelsstraße 30, 52074, Aachen, Germany. 3. Department of Oral and Maxillofacial Surgery, RWTH Aachen University Hospital, Pauwelsstraße 30, 62074, Aachen, Germany. 4. Department of Orthodontics, University Witten/Herdecke, Alfred-Herrhausen-Straße 45, 58448, Witten, Germany. 5. Department of Maxillofacial and Facial Plastic Surgery, University Leipzig, Liebigstraße 12, 04103, Leipzig, Germany.
Abstract
OBJECTIVES: Atrophic resorption of the maxillary alveolar ridge is a complication that makes implantological rehabilitation critical. Our aim was to develop a novel computer aided procedure for the accurate quantitative assessment of maxillary residual ridge resorption including pneumatisation of the maxillary sinus that goes beyond previously described approaches and to apply it to a large dataset. MATERIALS AND METHODS: To develop and refine the method, we performed a retrospective analysis using computed tomography data from 405 patients to generate segmented, three-dimensional models of zygomaticomaxillary bones and maxillary sinuses. Using anatomical landmarks and orientation lines or planes, all models were aligned automatically to subsequently generate cross-sectional images (n = 2835), enabling the classification of atrophy as well as the quantification of volumes and caudal extensions of the maxillary sinuses. RESULTS: We developed and implemented an accurate and reproducible workflow for the semi-automated analysis of volumetric maxillary images. Comprehensive statistical analysis of the large quantitative dataset revealed various correlations of maxillary process heights and sinus volumes with atrophy class, age and region and identified conjectural trends over the patient group. CONCLUSIONS: The method was used successfully to process a large dataset to classify atrophy, to measure alveolar height parameters, and to quantify maxillary sinus volume, bottom volume and pneumatisation. CLINICAL RELEVANCE: Apart from the anthropometric value of the generated dataset, the method could be applied to provide additional and more accurate data to assess the necessity of bone augmentation in the context of three-dimensional planning before implantation.
OBJECTIVES: Atrophic resorption of the maxillary alveolar ridge is a complication that makes implantological rehabilitation critical. Our aim was to develop a novel computer aided procedure for the accurate quantitative assessment of maxillary residual ridge resorption including pneumatisation of the maxillary sinus that goes beyond previously described approaches and to apply it to a large dataset. MATERIALS AND METHODS: To develop and refine the method, we performed a retrospective analysis using computed tomography data from 405 patients to generate segmented, three-dimensional models of zygomaticomaxillary bones and maxillary sinuses. Using anatomical landmarks and orientation lines or planes, all models were aligned automatically to subsequently generate cross-sectional images (n = 2835), enabling the classification of atrophy as well as the quantification of volumes and caudal extensions of the maxillary sinuses. RESULTS: We developed and implemented an accurate and reproducible workflow for the semi-automated analysis of volumetric maxillary images. Comprehensive statistical analysis of the large quantitative dataset revealed various correlations of maxillary process heights and sinus volumes with atrophy class, age and region and identified conjectural trends over the patient group. CONCLUSIONS: The method was used successfully to process a large dataset to classify atrophy, to measure alveolar height parameters, and to quantify maxillary sinus volume, bottom volume and pneumatisation. CLINICAL RELEVANCE: Apart from the anthropometric value of the generated dataset, the method could be applied to provide additional and more accurate data to assess the necessity of bone augmentation in the context of three-dimensional planning before implantation.