Figure 1. Example of a finite element analysis on the femoral component of a total knee arthroplasty.

Total knee arthroplasty (TKA) is a widely used surgical procedure to restore the knee joint after cartilage disorders as osteoarthritis. During this surgical procedure, the damaged cartilage layer is removed and the bones are cut in the correct alignment. The following step is the implantation of the TKA components in the knee. One of the major causes of failure after implantation of the TKA components in the knee is aseptic loosening. Since the fixation of the implant has an important role in this aseptic loosening, it is very important to test this beforehand. Implant fixation can be modelled and tested by doing finite element simulations and cadaveric experiments. During these experiments, the loading profiles of multiple activities will be applied to 3D models of the bone and the implant.

The aim of this project is to investigate the implant fixation of a PEEK total knee arthroplasty. This is done with the development of 3D models of the knee and a PEEK knee prosthesis. To create a proper model, the implant characteristics and patient variability need to be taken into account.

Researcher for this project: Corine Post.

Finite element analysis and cadaveric experiments to investigate the fixation of two types of implants with different interference fit. Total Knee Arthroplasty (TKA) is the most common and successful surgical procedure for a diseased knee joint, offering pain relief and improvement of knee function, and providing a better quality of life for patients. The surgery involves the removal of damaged cartilage and bone, then the TKA components are implanted in the knee. The cementless implants fixation depends on the difference between the internal cuts of the bones and the implant dimensions, which creates an interference fit.
Total Knee Artroplasty
Ideally, a higher interference should provide a better fixation, however, it could also cause bone abrasion and permanent deformation during implantation. Therefore, in this study finite element analysis and cadaveric experiments will be performed to investigate the fixation of two types of implants with different interference fit during two loading conditions by measuring the displacement of the implant relative to the bone (also known as micromotions) at the bone-implant interface.
Finite element modeling
Finite element modeling
Experimental setup

Researcher for this project: Esther Sanchez Garza.

Accurate fracture risk prediction is important, because it is difficult for clinicians to differentiate between high and low fracture risk lesions.

Patients with advanced cancer often develop bone metastases, and in approximately ten percent, these lesions occur in the femur. Femoral metastases may cause pain and can lead to pathological fractures, which severely affect the quality of life. Local treatment of patients with femoral metastases is based on the expected fracture risk: patients with a low fracture risk are treated conservatively with for example radiotherapy to decrease pain, whereas patients with a high fracture risk are considered for stabilizing surgery to prevent a fracture from occurring. Therefore, accurate fracture risk prediction is important. However, it is difficult for clinicians to differentiate between high and low fracture risk lesions, leading to considerable numbers of under and over treatment. To improve fracture risk prediction, we developed the BOne Strength score, or BOS score; a patient-specific finite element (FE) computer model that calculates bone strength based on patient-specific anatomy and bone quality, obtained from CT scans.

Previously, we demonstrated that the BOS score improved fracture risk prediction in patients, compared to current clinical guidelines. The goal of this project is to initiate clinical implementation of the BOS score by determining whether the BOS score aids in fracture risk prediction and treatment decision making for patients with bone metastases in the femur.

Researcher for this project: Florieke Eggermont.

Develop and implement a highly patient specific decision aid for patients with hip- or knee osteoarthritis

Hip- and knee osteoarthritis are leading causes of disability resulting in joint pain and stiffness. Total hip- and knee arthroplasty (THA and TKA) are a recommended intervention when conservative management appears to be ineffective and disability is significant. Due to this surgical intervention, quality of life rises while the functionality of the joints is ameliorated extensively. Although THA and TKA are considered safe, complications can interfere with a successful outcome or can even put patients in a life-threatening situation. Study findings show that comorbid diseases and medication use are known to influence complication risk and patient outcome.

Decision aids are commonly used to inform patients and to support the patient and physician to think about the level of experienced pain and discomfort, the personal circumstances and the intended type of surgery. These factors and the complication risk can influence which treatment fits best in the patients individual situation.
Until now, decision aids provide general information about treatment options and complication risk. Since one of the main challenges for physicians is to inform the individual patient by providing personal information about the complication risk, we aim to update the decision aid with a pre-operative risk estimation tool which takes the comorbid diseases and medication use of the individual patient into account.  

The aim of this project is to develop and implement a decision aid for patients with hip- or knee osteoarthritis to facilitate shared decision making by providing predicted outcomes which are highly patient specific.

Researcher for this project: Lieke Sweerts.

Bone is one of the most invaded tissues by the metastases of breast, prostate, lung, kidney, and thyroid cancer. Patients with bone metastases are treated based on fracture risk: patients with a high fracture risk are considered for prophylactic surgery to prevent pathological fracture, whereas patients with low fracture risk are treated with conservative treatment such as radiotherapy for pain management.
Figure 1: Arrows indicate osteolytic (A), osteoblastic (B), and mixed (C) bone metastases located in the femur.
Figure 1: Arrows indicate osteolytic (A), osteoblastic (B), and mixed (C) bone metastases located in the femur.
Finite element (FE) models can be used to evaluate metastatic bone fracture risk. However, currently, the FE models are not yet applicable to femurs affected with osteoblastic metastases. To determine the mechanical properties within the FE model, calibrated bone densities from CT scans are obtained and since osteoblastic metastases appear very dense on CT scans this results in strong mechanical properties of the metastatic lesions although they are weaker in real life. A potential solution is to assign more appropriate material properties to the osteoblastic lesions to better simulate the mechanical behaviour of the weakened tissue. For this purpose, exact segmentation of the metastatic lesions is important.
Figure 1: Arrows indicate osteolytic (A), osteoblastic (B), and mixed (C) bone metastases located in the femur.

Manual segmentation is a time-consuming task and it may be difficult to accurately segment every abnormality in the bone structure. The main goal of this study, therefore, is to automate the segmentation procedure using deep convolutional neural networks.

Researcher for this project: Ali Ataei.

Patients with metastatic bone disease can suffer from severe pain, pathological fractures of long bones or even spinal cord compression. For these patients, the main focus is on improving their quality of life by providing pain relief and preserving function. At the moment, in order to destroy these lesions, there are two methods available: external beam radiotherapy and open surgery. Radiotherapy doesn’t always succeed in providing pain relief and when it does, it is not immediate.

With the current surgical procedure, pain relief is almost immediate and it eliminates the risk for complications like the ones mentioned above. However, it’s a demanding procedure for a patient who is in a debilitated state. In order to address this, the project focus on developing a minimally invasive alternative treatment. This involves the design of a prototype tool system that can remove the metastatic lesion effectively, reduce risk of tumor reoccurrence and provide structural support to the targeted area. Moreover, it will also include fracture risk predictions and simulations of the impact of the tool to the human bone and surrounding structures for a comprehensive understanding of the tool’s performance.

Researcher for this project: Patricia Caetano de Almeida Rodriguez.