PhD Thesis Hip Joint Finite Element Modeling

Population-based finite element analysis of hip joint models enables us to study the effect of inter-subject variability on the simulation results. This approach is the dawn of computerized clinical trials and offers various contributions to improving current clinical treatment, the design of surgical simulators for virtual training, and optimizing hip implants. Despite the broad application of population-based analyses, developing large-scale subject-specific models is a challenging task and requires extensive manual effort. Thus, most state-of-the-art studies are limited in the number of subjects, and the anatomical representations are often subjected to simplifications. For instance, the geometry of the hip joint area is replaced with simple shapes, or the bilateral variation in this area is ignored. Such assumptions may limit the reliability of the predicted results. This thesis provides novel methods for developing finite element models that enable large-scale population-based assessment of hip joint behavior. We benefit from the power of computer science to improve the quality and automate the standard modeling approaches. We further employ our new pipeline to generate multiple subject-specific finite element models, including the bones and cartilages in the hip joint area. The subject-specific finite element models are clinically validated and have high-quality discretization with accurate geometries. These subject-specific models demonstrate different mechanical behavior across subjects within the same simulation scenario. Additionally, the simulation results vary between the left-hand and right-hand side of the body in each subject. Our work is one of the largest model repositories concerning the number of subjects and regions of interest. We aim to empower researchers with free access to verified and reproducible computational models. Thus, our detailed research data, including the clinical images, the segmentation label maps, the finite element models, and software tools, are openly accessible on https://diku-dk.github. io/libhip/ . In future work, we aim to add additional structures to our models and upscale the population size as a direct benefit of our approach. Abstract. Accurate geometry representation is essential in developing finite element models. Although generally good, deep-learning segmentation approaches with only few data have difficulties in accurately segmenting fine features, e.g., gaps and thin structures. Subsequently, segmented geometries need labor-intensive manual modifications to reach a quality where they can be used for simulation purposes. We propose a strategy that uses transfer learning to reuse datasets with poor segmentation combined with an active learning step where fine-tuning of the data results in anatomically accurate segmentations suitable for simulations. We use a modified MultiPlanar UNet that is pre-trained using inferior hip joint segmentation combined with a dedicated loss function to learn the gap regions and post-processing to correct some tiny inaccuracies on symmetric classes due to rotational invariance. We demonstrate this robust yet conceptually simple approach applied with clinically validated results on publicly available computed tomography scans of hip joints. work our approach to generating subject-specific hip joint cartilage without directly segmenting them from clinical images. We introduce a specialized geometry processing pipeline that uses the bone geometry information around the hip joint and generates the two articular cartilages in the hip joint. These cartilages are subject-specific and have non-uniform thickness across their geometry. Further, our modeling approach guarantees conforming interfaces with their underlying bones and congruent interfaces between the articular interfaces. These cartilage models were clinically verified, and their anatomical measurements agree with the data reported in the literature. We tested these models in a finite element simulation setup, and the results produced smooth stress distribution with no convergence issues related to the discretized geometry. The research data, including the modeling pipeline, is available on Github ( ) for public use. The number of available datasets in this work limited our experiments, and we only managed to test this pipeline on ten hip joints. We further tested the compatibility of this approach on other joints, such as the shoulder and the knee joint, but we left further investigation on such joints for future work. Abstract. We present a method to generate subject-speciﬁc cartilage for the hip joint. Given bone geometry, our approach is agnostic to image modality, creates conforming interfaces, and is well suited for ﬁnite element analysis. We demonstrate our method on ten hip joints showing anatomical shape consistency and well-behaved stress patterns. Our method is fast and may assist in large-scale biomechanical population studies of the hip joint when manual segmentation or training data is not feasible. Abstract Background and Objective: Population-based finite element analysis of hip joints allows us to understand the e ff ect of inter-subject variability on simulation results. Developing large subject-specific population models is challenging and requires extensive manual e ff ort. Thus, the anatomical representations are often subjected to simplification, and the bilateral variations are often neglected. The discretized geometries do not guarantee conformity in shared interfaces, leading to complications in setting up simulations. Additionally, these models are not openly accessible, challenging reproducibility. Our work provides multiple subject-specific hip joint finite element models, and a novel semi-automated modeling workflow. Methods: We reconstruct 11 healthy subject-specific models, including the sacrum, the paired hip bones, the paired proximal femurs, the paired hip joints, the paired sacroiliac joints, and the pubic symphysis. The bones are derived from CT scans, and the cartilages are generated from the bone geometries. We generate the whole complex’s volume mesh with conforming interfaces. Our models are evaluated using both mesh quality metrics and simulation experiments. Results: All models are clinically validated and show high-quality discretization with accurate geometries. The simulations produce smooth stress patterns, and the variance among the subjects demonstrates the e ff ect of inter-subject variability and asymmetry in the predicted results. Conclusions: Our work is one of the largest model repositories with respect to number of subjects and regions of interest in the hip joint area. Our detailed research data, including the clinical images, the segmentation label maps, the finite element models, and software tools, are openly accessible on GitHub. Our aim is empowering clinical researchers to have free access to verified and reproducible models. As future work, we aim for adding additional structures to our models. as the length of the connecting the most proximal point of the femur to the mid-point of the most distal part of the same bone; Width of the pelvis is as the distance between the most lateral point of the hip bones to the center; separation (IHS) is assumed as the distance between the paired hip joint centers; The height of ilium (IH) is the vertical distance between the most superior part of the hip bones and the hip joint center; width of ilium is defined by distance between the hip joint center and the most lateral point of hip We the cartilages will come into contact after applying a load in the FE simulation. The joint congruency level is measured using the distance between the acetabular and This paper presents a robust simulation pipeline, which starts from clinical images and results in the simulation of contact between multiple bodies. The input computational models in this approach are derived from CT scans and encoded in 3D arrays of voxels. The contribution of this thesis was to provide the input data for this pipeline, which included the hip and femur surface mesh segmented from CT images. This work was our initial attempt to use our segmented bone models for simulation purposes. The advantage of this collaboration was to pre-investigate the conventional finite element modeling workflow and figure out the existing bottlenecks in semi-automated bone segmentations. Throughout this work, we gained insights into some of the critical and labor-intensive parts of finite element modeling and identified how computer science could accelerate this approach. We have decided to exclude the manuscript of this paper from this thesis to keep the dissertation concise. We refer the reader to [4] for more details.