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The objective of the study was to suggest a novel quantitative assessment of acetabular bone defects based on a statistical shape model, validate the method, and present preliminary results. Two exemplary CT‐data sets with acetabular bone defects were segmented to obtain a solid model of each defect pelvis . The pathological areas around the acetabulum were excluded and a statistical shape model was fitted to the remaining healthy bone structures. The excluded areas were extrapolated such that a solid model of the native pelvis per specimen resulted (i.e., each pelvis without defect). The validity of the reconstruction was tested by a leave‐one‐out study. Validation results showed median reconstruction errors of 3.0 mm for center of rotation, 1.7 mm for acetabulum diameter, 2.1° for inclination, 2.5° for anteversion, and 3.3 mm 3 for bone volume around the acetabulum. By applying Boolean operations on the solid models of defect and native pelvis , bone loss and bone formation in four different sectors were assessed. For both analyzed specimens, bone loss and bone formation per sector were calculated and were consistent with the visual impression. In specimen_1 bone loss was predominant in the medial wall (10.8 ml; 79%), in specimen_2 in the posterior column (15.6 ml; 46%). This study showed the feasibility of a quantitative assessment of acetabular bone defects using a statistical shape model‐based reconstruction method. Validation results showed acceptable reconstruction accuracy, also when less healthy bone remains. The method could potentially be used for implant development, pre‐clinical testing, pre‐operative planning, and intra‐operative navigation. © 2018 The Authors. Journal of Orthopaedic Research ® Published by Wiley Periodicals, Inc. on behalf of Orthopaedic Research Society. J Orthop Res 9999:1–9, 2018.

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This study's objective was the generation of a standardized geometry of the healthy nasal cavity. An average geometry of the healthy nasal cavity was generated using a statistical shape model based on 25 symptom-free subjects. Airflow within the average geometry and these geometries was calculated using fluid simulations. Integral measures of the nasal resistance, wall shear stresses (WSS) and velocities were calculated as well as cross-sectional areas (CSA). Furthermore, individual WSS and static pressure distributions were mapped onto the average geometry. The average geometry featured an overall more regular shape that resulted in less resistance, reduced WSS and velocities compared to the median of the 25 geometries. Spatial distributions of WSS and pressure of the average geometry agreed well compared to the average distributions of all individual geometries. The minimal CSA of the average geometry was larger than the median of all individual geometries (83.4 vs. 74.7 mm²). The airflow observed within the average geometry of the healthy nasal cavity did not equal the average airflow of the individual geometries. While differences observed for integral measures were notable, the calculated values for the average geometry lay within the distributions of the individual parameters. Spatially resolved parameters differed less prominently.The nose is the main passageway for respired air to flow from ambient to the lungs and vice versa. Air flowing through the nose is humidified, tempered and cleansed from particles, which could harm the intricate structures of the lungs. While the anatomy of the nose can easily be investigated from the outside using either a speculum or an endoscope, the nature of healthy nasal airflow is not yet fully understood. Nonetheless, there were extensive efforts as well as remarkable progress to better understand the complex airflow within the nose.While investigation of the airflow within the nasal cavity began with in-vitro experiments using either cadaver castings or upscaled models 1-3 , numerical simulation of nasal airflow became the quasi standard within the last decade and is now widely used 4-6 . Here, the patient-specific geometry of the nasal cavity is reconstructed using computed tomography (CT) scans.Recently, first studies were able to reveal correlations between numerically calculated flow parameters and the perceived nasal patency of a patient. Zhao et al. were able to show, that a significant correlation between cooling of the mucosal layer, a process that is associated with trigeminal function, is correlated with patency ratings of the nose 7,8 . In more recent studies, they were able to reveal differences in wall shear stress and heat flux between symptomatic and asymptomatic patients with septal perforations 9 and they were also able to show, that numerical simulations might help to understand complex relationships between surgical procedures and the development of empty nose syndrome 10 . Sanmiguel-Rojas et al. proposed another approach, where two non-dimensional par...

We propose a novel GPU-based approach to render virtual X-ray projections of deformable tetrahedral meshes. These meshes represent the shape and the internal density distribution of a particular anatomical structure and are derived from statistical shape and intensity models (SSIMs). We apply our method to improve the geometric reconstruction of 3D anatomy (e.g. pelvic bone) from 2D X-ray images. For that purpose, shape and density of a tetrahedral mesh are varied and virtual X-ray projections are generated within an optimization process until the similarity between the computed virtual X-ray and the respective anatomy depicted in a given clinical X-ray is maximized. The OpenGL implementation presented in this work deforms and projects tetrahedral meshes of high resolution (200.000+ tetrahedra) at interactive rates. It generates virtual X-rays that accurately depict the density distribution of an anatomy of interest. Compared to existing methods that accumulate X-ray attenuation in deformable meshes, our novel approach significantly boosts the deformation/projection performance. The proposed projection algorithm scales better with respect to mesh resolution and complexity of the density distribution, and the combined deformation and projection on the GPU scales better with respect to the number of deformation parameters. The gain in performance allows for a larger number of cycles in the optimization process. Consequently, it reduces the risk of being stuck in a local optimum. We believe that our approach will improve treatments in orthopedics, where 3D anatomical information is essential.