Pelvic tilt compensates for increased acetabular anteversion.

PURPOSE: Pelvic tilt determines functional orientation of the acetabulum. In this study, we investigated the interaction of pelvic tilt and functional acetabular anteversion (AA) in supine position. METHODS: Pelvic tilt and AA of 138 individuals were measured by computed tomography (CT). AA was calculated in relation to the anterior pelvic plane (APP) and relative to the table plane. We analysed these parameters for gender-specific and age-related differences. RESULTS: The mean pelvic tilt was -0.1 ± 5.5°. Pelvic sagittal rotation displayed no gender nor age related differences. Females showed higher angles of AA compared with males (20.0° vs 17.2°, p < 0.001; AA relative to the APP). Anterior tilting of the pelvis positively correlated with AA and individuals with high AA had a higher anterior pelvic tilt compared with those with low AA (p < 0.0001; AA relative to the APP). CONCLUSIONS: AA has to be calculated regarding pelvic sagittal rotation for correct acetabular orientation. Pelvic tilt is dependent on acetabular orientation and compensates for increased AA.

Kinematic differences between gender specific and traditional knee implants.

In the ongoing debate about gender-specific (GS) vs. traditional knee implants, there is limited information about patella-specific outcomes. GS femoral component features should provide better patellar tracking, but techniques have not existed previously to test this accurately. Using novel computed tomography and radiography imaging protocols, 15 GS knees were compared to 10 traditional knees, for the 6 degrees of freedom of the patellofemoral and tibiofemoral joints throughout the range of motion, plus other geometric measures and quality of life (QOL). Significant differences were found for patellar medial/lateral shift, where the patella was shifted more laterally for the GS femoral component. Neither group demonstrated patellar maltracking. There were no other significant differences in this well-functioning group.

Deep Learning Based Centerline-Aggregated Aortic Hemodynamics: An Efficient Alternative to Numerical Modeling of Hemodynamics.

Image-based patient-specific modelling of hemodynamics are gaining increased popularity as a diagnosis and outcome prediction solution for a variety of cardiovascular diseases. While their potential to improve diagnostic capabilities and thereby clinical outcome is widely recognized, these methods require considerable computational resources since they are mostly based on conventional numerical methods such as computational fluid dynamics (CFD). As an alternative to the numerical methods, we propose a machine learning (ML) based approach to calculate patient-specific hemodynamic parameters. Compared to CFD based methods, our approach holds the benefit of being able to calculate a patient-specific hemodynamic outcome instantly with little need for computational power. In this proof-of-concept study, we present a deep artificial neural network (ANN) capable of computing hemodynamics for patients with aortic coarctation in a centerline aggregated (i.e., locally averaged) form. Considering the complex relation between vessels shape and hemodynamics on the one hand and the limited availability of suitable clinical data on the other, a sufficient accuracy of the ANN may however not be achieved with available data only. Another key aspect of this study is therefore the successful augmentation of available clinical data. Using a statistical shape model, additional training data was generated which substantially increased the ANN's accuracy, showcasing the ability of ML based methods to perform in-silico modelling tasks previously requiring resource intensive CFD simulations.

AutoImplant 2020-First MICCAI Challenge on Automatic Cranial Implant Design.

The aim of this paper is to provide a comprehensive overview of the MICCAI 2020 AutoImplant Challenge. The approaches and publications submitted and accepted within the challenge will be summarized and reported, highlighting common algorithmic trends and algorithmic diversity. Furthermore, the evaluation results will be presented, compared and discussed in regard to the challenge aim: seeking for low cost, fast and fully automated solutions for cranial implant design. Based on feedback from collaborating neurosurgeons, this paper concludes by stating open issues and post-challenge requirements for intra-operative use. The codes can be found at https://github.com/Jianningli/tmi.

VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images.

Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VerSe: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VerSe content and code can be accessed at: https://github.com/anjany/verse.

Characterization of the Airflow within an Average Geometry of the Healthy Human Nasal Cavity.

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.

Method for quantitative assessment of acetabular bone defects.

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 mm3 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.

Changes in knee shape and geometry resulting from total knee arthroplasty.

Changes in knee shape and geometry resulting from total knee arthroplasty can affect patients in numerous important ways: pain, function, stability, range of motion, and kinematics. Quantitative data concerning these changes have not been previously available, to our knowledge, yet are essential to understand individual experiences of total knee arthroplasty and thereby improve outcomes for all patients. The limiting factor has been the challenge of accurately measuring these changes. Our study objective was to develop a conceptual framework and analysis method to investigate changes in knee shape and geometry, and prospectively apply it to a sample total knee arthroplasty population. Using clinically available computed tomography and radiography imaging systems, the three-dimensional knee shape and geometry of nine patients (eight varus and one valgus) were compared before and after total knee arthroplasty. All patients had largely good outcomes after their total knee arthroplasty. Knee shape changed both visually and numerically. On average, the distal condyles were slightly higher medially and lower laterally (range: +4.5 mm to -4.4 mm), the posterior condyles extended farther out medially but not laterally (range: +1.8 to -6.4 mm), patellofemoral distance increased throughout flexion by 1.8-3.5 mm, and patellar thickness alone increased by 2.9 mm (range: 0.7-5.2 mm). External femoral rotation differed preop and postop. Joint line distance, taking cartilage into account, changed by +0.7 to -1.5 mm on average throughout flexion. Important differences in shape and geometry were seen between pre-total knee arthroplasty and post-total knee arthroplasty knees. While this is qualitatively known, this is the first study to report it quantitatively, an important precursor to identifying the reasons for the poor outcome of some patients. Using the developed protocol and visualization techniques to compare patients with good versus poor clinical outcomes could lead to changes in implant design, implant selection, component positioning, and surgical technique. Recommendations based on this sample population are provided. Intraoperative and postoperative feedback could ultimately improve patient satisfaction.

Influence of pelvic tilt on functional acetabular orientation.

BACKGROUND: Pelvic tilt influences acetabular orientation (AO). Anatomical AO can be measured in relation to the anterior pelvic plane (APP), functional AO can be calculated relative to table's plane. OBJECTIVE: To assess to what extent functional AO is determined by pelvic tilt and if APP and table plane give equal information for correct AO. METHODS: AO was evaluated by computed tomography (CT) scans of 138 patients. Pelvic tilt, anatomical and functional AO were measured, differences between the two reference planes were calculated. RESULTS: Anatomical and functional acetabular anteversion (AA) were found to be different in 21% of individuals with an enhanced extent of pelvic tilt. Functional AA was increased compared to anatomical AA at high posterior pelvic tilt (p < 0.001). Enlarged anterior tilting of the pelvis reduced APP-related AA (p < 0.002). Anatomical AA positively correlated with pelvic tilt, particularly in females (p < 0.01, correlation coefficient = 0.698, R2 = 0.523). CONCLUSIONS: APP and table plane do not provide equal information about AO at enhanced pelvic tilt. Functional orientation of the acetabulum is dependent on pelvic tilt, which itself is influenced by anatomical AA and should therefore be analyzed for precise AO.

Fast generation of virtual X-ray images for reconstruction of 3D anatomy.

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.

A large scale finite element study of a cementless osseointegrated tibial tray.

The aim of this study was to investigate the performance of a cementless osseointegrated tibial tray (P.F.C. ® Sigma®, Depuy® Inc, USA) in a general population using finite element (FE) analysis. Computational testing of total knee replacements (TKRs) typically only use a model of a single patient and assume the results can be extrapolated to the general population. In this study, two statistical models (SMs) were used; one of the shape and elastic modulus of the tibia, and one of the tibiofemoral joint loads over a gait cycle, to generate a population of FE models. A method was developed to automatically size, position and implant the tibial tray in each tibia, and 328 models were successfully implanted and analysed. The peak strain in the bone of the resected surface was examined and the percentage surface area of bone above yield strain (PSAY) was used to determine the risk of failure of a model. Using an arbitrary threshold of 10% PSAY, the models were divided into two groups ('higher risk' and 'lower risk') in order to explore factors that may influence potential failure. In this study, 17% of models were in the 'higher risk' group and it was found that these models had a lower elastic modulus (mean 275.7MPa), a higher weight (mean 85.3kg), and larger peak loads, of which the axial force was the most significant. This study showed the mean peak strain of the resected surface and PSAY were not significantly different between implant sizes.