Muscle-tendon paths vary between individuals, and musculoskeletal models show sensitivity to these variations when estimating muscle and joint forces. Accurate estimations of these internal forces allow for a more comprehensive approach to researching debilitating musculoskeletal pathologies such as osteoarthritis. However, defining subject-specific muscle-tendon paths is labour-intensive and requires expert knowledge, which is not as repeatable. Therefore, in this study, the accuracy of determining subject-specific muscle points and volumes based on lower limb Magnetic Resonance (MR) scans with the nnU-net is evaluated. Two models are trained using the open-source pipeline, referred to as the point and volume model. The volume model aims to segment muscle-tendon volumes and is trained on the open-source augmented dataset of Henson et al. (2023). The point model localises attachment and via points describing muscle action lines for a subset of relevant muscles of the volume model. Since U-net is not designed for predicting points, a workaround is introduced by creating cubes around the points as labels. The 3D volume model scored a median Dice Similarity Coecient of 92.7 % and shows some generalisation capability. The 3D point model scored a median Euclidean error of 5.1 mm. Compared to intra-operator variability for attachment points, this approach yields lower and more repeatable accuracy without required manual intervention. The nnU-net pipeline is capable of producing accurate models that can define subject-specific muscle-tendon paths based on MR scans.

Knee osteoarthritis (KOA) is one of the most prevalent joint disease worldwide. Musculoskeletal modeling (MSM) provides a useful tool to investigate knee joint biomechanics and potential development and progression of KOA. However, for clinical usage, it is crucial to understand how patient-specific properties affect MSM outcomes. This study investigated how uncertainty in the tendon slack length (TSL) of lower limb muscles affect knee joint reaction forces (JRFs) in gait analysis. Two sensitivity analyses were performed, one approach used reported TSL variation in literature and the other approach used a percentage TSL variation. Anatomical variation was investigated through a literature review. The JRF estimations of both approaches together resulted in a maximum variation of 0.27 x BW in AP direction, 2 x BW in SI direction and 0.13 x BW in ML direction. Maximum JRF estimations resulting from reported TSL variation were larger in the loading response in all direction. Maximum JRF estimations in ML direction were resulting from percentage variation. TSL of muscles that were found to be most influential in JRF estimations were medial gastrocnemius, rectus femoris, vastus lateralis and psoas. This research confirmed that within patient-specific MSM for clinical purposes, uncertainty in TSL should be limited. Future research should aim to investigate how this influences AC degeneration for KOA.

Knee osteoarthritis is a common joint disease, affecting the knee joint. To help slow down the degeneration of cartilage, changing people's gait is a known solution. The most common modification is teaching people to walk toeing-in or toeing-out, changing their foot progression angle (FPA). However, it is still unclear what the effect is of changing the FPA on other gait parameters and lower joint kinetics. Changing the FPA, also changes gait parameters such as gait speed, step width, stride length, trunk lean, and medial thrust. It is hypothesized that lower joint kinetics are negatively affected when altering the FPA. This thesis focuses on the effect on the lower joint kinetics and on the gait parameters when changing the FPA.

Tests are done on healthy subjects with four FPA alterations besides normal gait, a small and large angle toeing-in and toeing-out. A haptic feedback device is used that contains vibrating motors located at the medial and lateral side of the calf to teach participants to walk in the desired angle. First participants are asked about the ease of use and comfort of the haptic feedback system. Second, the knee adduction moment (KAM) and knee flexion moment (KFM) are measured for each participant to evaluate the effect of the different FPA's. Third, the moments in the lower joints, hip and ankle, are measured to research the effect of different FPA's on the loading within these joints. This load is measured at peak value and as area under the graph. Lastly, there is researched what gait parameters participants alter when changing the FPA. The thesis mainly focuses on the effect on the lower joint kinetics since literature still shows a gap here.

The haptic feedback resulted comfortable for most participants, the ease to achieve a high amount of correct steps within the asked FPA appeared more difficult. Changing the FPA to reduce the KAM, showed for the participants different angles that maximally reduces the KAM. Highlighting the importance of a personalized approach. The hip adduction moment and ankle moment both increase overall for the participants. While the hip rotation and flexion moment show a mean reduction. Making it important to focus most on the hip adduction moment and ankle moment, and how to lower these increases in moment. Otherwise this could result in hip osteoarthritis. Different FPA's also change the impact of the gait parameters for the participants. Indicating participants to change their entire posture when adapting different angles. Resulting more aspects to focus on when learning participants to walk differently.

To conclude, changes in the FPA have effect on the KAM, gait parameters, and on the lower joint kinetics. The reduction on KAM differs per participant, indicating a personalized approach. Gait parameters change when applying FPA's, mainly gait speed, stride length and step width change for most participants. Measurements on the lower joint kinetics show interesting insights on the effect of changing the FPA. Highlighting the hip adduction moment and ankle moment to focus on to prevent possible excessive load and damage.

Image registration combines data from multiple imaging modalities or time points by aligning images. Existing 2D-3D registration methods often require manual intervention, introducing variability and increasing processing time, and rely on Computed Tomography (CT) imaging, which exposes patients to radiation. This study aims to develop an automated registration pipeline that ensures accurate registration between 3D Magnetic Resonance Imaging (MRI) and 2D fluoroscopic images by preprocessing techniques.

This study involved two registration experiments to evaluate the pipeline. The DiffDRR package was used with normalized cross-correlation (NCC) as similarity metric and gradient-based Adam optimizer for optimization. The first experiment validated the accuracy of registration between 3D MRI models (solid bone, empty shell, and filled shell) and 2D CT-derived digitally reconstructed radiographs (DRRs) with known rotations and translations as reference standard. The second experiment aimed to determine the most effective fluoroscopy preprocessing technique for aligning 3D MRI models with 2D fluoroscopic images. Preprocessing techniques for the fluoroscopic images included contrast enhancement via thresholding and normalization, and region of interest selection or segmentation.

Validation results showed smaller rotational and translational errors for both shell models compared to the solid bone model. In-plane errors for the shell models were 0.1-3.3mm and out-of-plane 15.8-55.0mm, while solid bone reported in-plane errors of 1.0-4.7mm and out-of-plane of 68.3-282.7mm. Registration between empty shell models and differently preprocessed fluoroscopic images showed that segmentation of the bones in fluoroscopic images yielded the highest NCC values. However, visual assessment revealed inconsistencies in plausible bone positioning.

This automated 2D-3D registration method shows potential for registration between 3D MRI and 2D fluoroscopic images of the knee joint. The current method requires further refinement, particularly in integrating soft tissue data, possibly via synthetic CTs, and adopting more anatomically sensitive metrics, such as mutual information or gradient correlation.

BACKGROUND: Research on the role of patellar biomechanics in patellofemoral (PF) joint pathologies requires accurate and reliable measurement of the patella relative to the femur (patellar tracking) but current methods have limitations. In vivo measurement of PF kinematics is done using medical imaging techniques but these are expensive, involve radiation exposure or only allow slow, supine motions. A widely used alternative is optical motion capture combined with musculoskeletal modeling. However, due to the large motion of the skin relative to the patella, the typical approach using anatomical calibrated skin-markers is unsuitable. As the shape of the patella is grossly visible underneath the skin, placement of a grid of skin-mounted markers on the skin covering the patella might offer an alternative for patellar tracking.
OBJECTIVE: The objective of this study was to develop a patellar tracking framework designed to measure in vivo PF translations using a grid of skin-mounted markers and to compare it to in vivo measures derived from dynamic CT images and musculoskeletal model estimates.
MATERIALS AND METHODS: Two participants performed a supine knee flexion-extension motion with a grid of 42 retroreflective markers placed on the right knee during four-dimensional computed tomography (4D-CT) measurement and optical motion capture. A patellar identification algorithm (PIA) was developed to estimate the PF translations in the medial – lateral (ML), superior- inferior (SI) and anterior – posterior (AP) directions. PF translations were determined by four different methods: CT bone geometry-based (CT_BONES), CT marker grid-based (CT_GRID), optical motion capture marker grid-based (OMC_GRID) and by musculoskeletal modeling (OMC_MODEL). To evaluate the feasibility of our method we first compared CT_BONES to CT_GRID. Next, results obtained by different measurement types (CT_GRID and OMC_GRID) and results obtained by two different marker-based methods (OMC_GRID and OMC_MODEL) were compared. To compare ranges of motions, patellar excursions relative to the femur were evaluated and compared for all knee angles (KA_ALL) and for knee angles > 20° (KA_ENGAGED).
RESULTS: Differences in patellar excursions given by CT_BONES and CT_GRID during KA_ENGAGED were less than 4 mm and RMSEs less than 3 mm. These differences were less than 5 mm between CT_GRID and OMC_GRID (RMSE: 5 mm) and less than 16 mm between OMC_GRID and OMC_MODEL (RMSE: 10 mm) for knee angles lower than 20°. Larger differences in patellar excursions and RMSEs were seen during KA_ALL.
CONCLUSION: PF translations could be measured using a marker grid with an accuracy within 3 mm for knee angles larger than 20° (i.e. during patellar engagement). However, our method performed worse for knee angles smaller than 20° flexion (an accuracy within 12 mm). The findings of this study provide valuable baseline knowledge for non-invasive, marker-based patellar tracking. Future studies should include larger sample sizes to allow for proper validation.

To better understand and predict osteoarthritis, researchers are developing so-called coupled modelling workflows. Coupled workflows convert data from gait analysis studies to subject-specific tissue mechanical response estimations through the use of musculoskeletal and finite element models. The tissue mechanical response inside the joint is thought to play an integral role in the onset and progression of osteoarthritis. To study this, the design of coupled workflow was proposed. This design contained subject-specific gait data which was processed by a musculoskeletal model with a single degree of freedom knee joint. Musculoskeletal output was transferred through an adjusted generic finite element model of the knee to calculate maximum principal stress and shear strain values in the tibial cartilage of the knee. Proper marker placement is crucial for making an accurate assessment of the patient’s function in gait analysis studies. It has been claimed that marker misplacement is the main cause of measurement variability in gait analysis studies. It however remains unclear how potential marker misplacement propagates to the coupled modelling workflow results. To investigate this, in addition to the design of a coupled workflow, a sensitivity analysis was performed. With this sensitivity analysis we tried to answer the following question: How does marker placement of knee joint markers in gait analysis influence the tissue mechanical response calculated by a coupled workflow? For the sensitivity analysis, knee joint marker placements were virtually perturbed along anterior-posterior, proximal-distal and medial-lateral direction, to mimic marker misplacement. Corresponding knee biomechanics were estimated from the perturbed input data in the coupled workflow. Peak maximum principal stress values varied by up to 0.60MPa and peak shear strain varied by up to 0.08% as a result of perturbed knee marker placement. For cumulative stress levels, broader relative ranges were found. Moreover, the results showed that marker placement along the anterior-posterior direction had the greatest influence on corresponding tissue mechanical response estimations. In future studies a standard error of measurement margin is proposed in the assessment of coupled modelling worfklow results. In conclusion, the proposed workfow was relatively easy to build and provided similar tissue mechanical response result to those as reported by more complex models which were more computationally intensive. This implies that in the future, coupled modelling processes may very well be incorporated into the clinical decision-making process for musculoskeletal disorders like osteoarthritis.

Introduction: Amputees may be eligible to use a socket or osseointegrated (OI) prosthesis after losing a limb. A lot is known about the stump-socket interface in transtibial (TT) amputees. However, there are hardly any articles that explain the load transfers between the tibia and an OI prosthesis. For this reason, this study will focus on the load pattern at the bone-implant interface. The aim is to investigate how the osseointegrated loading for TT amputees is compared to able-bodied subjects. Method: Two generic musculoskeletal models were created in OpenSim: a transtibial osseointegrated (TTO) model and an able-bodied model. The able-bodied model includes all muscles in the lower limb, where the TTO model had no muscles around the tibia. Two datasets, consisting of five able-bodied participants (healthy control (HC)) and five transtibial amputees (TTA) with a socket prosthesis, were used. The internal reaction forces and moments were analysed for the affected side during the stance phase of the gait cycle. These normalized forces and moments were expressed in bodyweight (BW) and bodyweight x height (BWH), respectively. Results: The largest forces at the bone-implant interface were measured in the longitudinal direction (-6.92 BW) for the able-bodied model, the lowest in the mediolateral direction (-0.09 BW) for the TTO model. The loading differences were the highest between the able-bodied model and the TTO model with TTA data. Around the antero-posterior axis, the largest moments were found. The able-bodied model measured 0.027 BWH, where the TTO model with HC data found 0.023 BWH and 0.017 BWH for the TTA data. Conclusion: Transtibial amputees have decreased loading at the bone-implant interface compared to able-bodied subjects. The muscles play the most prominent role in comparison with the gait pattern.