Browsing by Author "Manske, Sarah"

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    The Application of the Reference Finite Helical Axis for Characterizing Knee Joint Kinematics
    (2022-01) Bugajski, Tomasz; Ronsky, Janet; Manske, Sarah; Johnston, Kelly
    Altered knee kinematics are an important biomechanical marker for the development of tibiofemoral osteoarthritis (OA). They are associated with altered cartilage contact areas, resulting in forces acting on unadapted cartilage that may degrade over time. The conventional approach to quantify knee kinematics is with Cardan angles, but the uncommon helical axis (HA) approach may provide supplementary information. However, the HA is susceptible to stochastic errors when angular displacements are small. To alleviate this error, a reference position may be used that permits larger angular displacements. However, more assessments are required to determine the utility of this reference finite helical axis (rFHA) method to provide biomechanical markers of tibiofemoral OA. The purpose of this thesis was to technically evaluate the rFHA and demonstrate its ability to distinguish knee kinematics of high tibiofemoral OA risk individuals. Technical evaluations consisted of 1) determining the effect of different smoothing techniques on rFHA accuracy, 2) assessing the sensitivity of the rFHA to reference position misalignments, and 3) comparing rFHA measures between an optical motion camera system (OMCS) and highspeed biplanar videoradiography system (HSBV). The utility of the rFHA was demonstrated by applying it to high tibiofemoral OA risk populations, specifically anterior cruciate ligament repaired (ACLR) knees and older knees. A spline filter with outlier removal process was the top performing smoothing technique for rFHA accuracy, providing a 72.2-80.1% improvement in rotational speed differences. Substantial differences of the rFHA measures were determined with misaligned reference positions, ranging from 1.17-19.53 mm and 0.77-5.45 deg. rFHA measure differences were also found between the OMCS and HSBV, ranging from 10.19-58.03 mm and 3.39-13.63 deg. Finally, kinematic trends were found in ACLR knees during a vertical drop jump, showing greater magnitudes of rFHA dispersion and helical internal rotation than healthy knees (dispersion: 0.46 deg; helical internal rotation: 2.18 deg). Additionally, significantly different rFHA path lengths were found between older and younger asymptomatic knees during walking (10.60 mm, p = 0.01). These findings demonstrate the utility of the rFHA in biomechanics, providing a supplementary method of characterizing knee kinematics and distinguishing the movement patterns of healthy individuals from tibiofemoral OA prone individuals.
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    Assessment of the Efficacy of MRI for Detectionof Changes in Bone Morphology in a MouseModel of Bone Injury
    (Wiley, 2013-07-11) Taha, May A; Manske, Sarah; Kristensen, Erika; Taiani, Jaymi; Krawetz, Roman; Wu, Ying; Ponjevic, Dragana; Matyas, John; Boyd, Steven; Rancourt, Derrick; Dunn, Jeffrey F.
    Purpose To determine whether magnetic resonance imaging (MRI) could be used to track changes in skeletal morphology during bone healing using high-resolution micro-computed tomography (μCT) as a standard. We used a mouse model of bone injury to compare μCT with MRI. Materials and Methods Surgery was performed to induce a burr hole fracture in the mouse tibia. A selection of biomaterials was immediately implanted into the fractures. First we optimized the imaging sequences by testing different MRI pulse sequences. Then changes in bone morphology over the course of fracture repair were assessed using in vivo MRI and μCT. Histology was performed to validate the imaging outcomes. Results The rapid acquisition with relaxation enhancement (RARE) sequence provided sufficient contrast between bone and the surrounding tissues to clearly reveal the fracture. It allowed detection of the fracture clearly 1 and 14 days postsurgery and revealed soft tissue changes that were not clear on μCT. In MRI and μCT the fracture was seen at day 1 and partial healing was detected at day 14. Conclusion The RARE sequence was the most suitable for MRI bone imaging. It enabled the detection of hard and even soft tissue changes. These findings suggest that MRI could be an effective imaging modality for assessing changes in bone morphology and pathobiology.
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    Bone and 3D Joint Space Width Analysis in Knee Osteoarthritis using Weight Bearing CT
    (2024-01-26) Waungana, Tadiwa Hanson; Manske, Sarah; Edwards, William Brent; Boyd, Steven
    Osteoarthritis (OA) is the most common type of joint disorder in the world and a major cause of disability in the adult population. Globally, it is one of the fastest-growing health conditions and OA prevalence is expected to continue rising due to an aging global population. The knee is the most affected joint in OA, accounting for approximately 80% of the global OA burden, making it an important joint to consider in the context of OA. Knee OA is characterized by bone and joint structural changes, such as changes in the apparent bone mineral density, thickening of the subchondral bone plate and joint space narrowing. Detection of these changes has traditionally been considered the reserve of X-ray radiography, which is challenged by inherent anatomical overlap of a 2D imaging modality. Weight bearing computed tomography (WBCT) has recently been utilized to image and investigate OA-related structural changes in the knee, as it provides a 3D visualization of the joint whilst in a functionally relevant loaded stance. In this thesis, the utility of WBCT in measuring bone mineral density (BMD) was investigated. Next, methods to measure joint space width (JSW) subchondral bone plate thickness (SBP.Th) were implemented and tested on end-stage knee OA cohort with age- and sex- matched healthy controls. The first study showed that BMD measurement accuracy is influenced by the apparent BMD at the measurement site, with greater accuracy in trabecular bone regions. The second study demonstrated that the JSW and SBP.Th measurement methods implemented in this thesis were comparable to existing methods and able to distinguish between healthy and OA knees. A narrower JSW and thicker lateral SBP.Th in the load-bearing region of the femur was found in OA knees compared to control knees. These results show that WBCT and the implemented analysis methods may be used to measure OA-related bone and joint changes in vivo at the knee.
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    Brain Magnetic Resonance Spectroscopy: Advances and Applications to Chronic Pain in Knee Osteoarthritis
    (2024-06-24) Leech, Samantha; Manske, Sarah; Harris, Ashley; Dunn, Jeffrey; Ng, Richard; Goodyear, Bradley; Dydak, Ulrike
    This dissertation investigates advancements in brain proton magnetic resonance spectroscopy (1H-MRS) measures and their application to chronic pain in knee osteoarthritis. 1H-MRS measures proton signals, which can be converted into absolute concentrations using the properties of water, brain tissue, and neurochemicals. These concentrations serve as markers of brain health or dysfunction. Current methods to quantify absolute neurochemical concentrations assume an equal distribution of neurochemicals between white matter (WM) and gray matter (GM), an assumption not thoroughly examined. To address this, I determined the distribution of six neurochemicals between WM and GM to establish correction factors to replace assumptions with calculated values. After validation using an independent dataset, I created an open-source tool to implement the calculated correction factors, improving 1H-MRS accuracy by 30-55%. I used quantitative synthetic imaging to measure water properties — relaxation rates (T1 and T2) and proton density (PD) — in different brain tissues of healthy adults. I assessed the impact of inter-individual differences in T1, T2, and PD on neurochemical concentration measures by comparing concentrations calculated using literature-based constants (as is typically performed) to concentrations calculated using individual measures from quantitative maps. In a young, healthy population, individual measures contributed to subtle yet significant variations in calculated neurochemical concentrations, suggesting that using uniform literature values may not be accurate for every individual. Sensitivity analyses indicated that these inaccuracies are likely greater across a wider age range or in individuals with clinical disorders. Applying 1H-MRS, I identified potential neurochemicals and brain regions associated with chronic pain in knee osteoarthritis to understand the brain’s role in this condition. Knee osteoarthritis is a leading cause of chronic pain, with limited research on the specific neurochemicals and brain regions involved. I compared neurochemical levels and their association with pain measures in four brain regions between patients with knee osteoarthritis and healthy controls as well as longitudinally in patients three months after total knee replacement surgery. I found opposing relationships in brain regions associated with pain's sensory and affective dimensions. This dissertation enhances the accuracy of neurochemical concentration quantification and refines the understanding of the brain's contribution to knee osteoarthritis pain.
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    Characterizing the structure-function relationship of hand osteoarthritis using dynamic and high resolution CT imaging
    (2024-03-27) Kuczynski, Michael Tadeusz; Manske, Sarah; Ronsky, Janet; Edwards, W. Brent; Schneider, Prism
    Osteoarthritis (OA) is the most common form of arthritis and affects the trapeziometacarpal (TMC) joint. While the etiology of OA is still not fully understood, it is a multifactorial disease with biomechanical factors associated in its development. The thumb is estimated to account for over 40% of the hand’s entire function, largely due to the TMC joint. A better understanding of structural and functional changes in TMC OA may improve our understanding of this degenerative joint disease. A recent advancement in computed tomography (CT) now allows for imaging moving joints in vivo. This technique, termed dynamic CT, provides a unique opportunity to quantify joint biomechanics in vivo. In this thesis, novel methodologies are presented that improve processing of dynamic and high-resolution peripheral quantitative CT (HR-pQCT) scans. These methodologies allow for semi-automated quantification of joint space and bone mineral density (BMD) in HR-pQCT scans and biomechanical outcomes from dynamic CT. The methodologies developed in this dissertation drastically reduce processing time for dynamic CT scans compared to previous literature. A cross-sectional study is presented that utilizes HR-pQCT to measure joint space (JS) changes in hand OA, the first of its kind. Maximum JS was significantly greater in OA than controls in the second and third distal interphalangeal (DIP2, DIP3) joints (DIP2: 2.07 mm vs. 1.88 mm; DIP3: 2.01 mm vs. 1.86 mm), and decreased hand function and increased hand disability were associated with increased radiographic TMC OA. A study was conducted to characterize normal TMC joint biomechanics in vivo using the presented methodologies. BMD was computed in anatomical quadrants of the TMC joint, and it was found that the radial-volar quadrant of the first metacarpal (426 mg HA/cm3) and ulnar-volar quadrant of the trapezium (373 mg HA/cm3) were significantly greater than other quadrants. When compared with proximity maps from dynamic CT, areas with high contact corresponded to quadrants with higher BMD. The results from this dissertation provide methodologies to analyze bone and joint changes with HR-pQCT and dynamic CT to better understand hand osteoarthritis.
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    Differences in Kinetic Variables Between Injured and Uninjured Rearfoot Runners: A Hierarchical Cluster Analysis
    (2022-09) Senevirathna, Benthara Hettiarachchige Angela Madushani; Ferber, Reed; Edwards, W. Brent; Jordan, Maththew; Manske, Sarah
    Running is a popular form of physical activity with a surprisingly high incidence of running-related injuries. While the relationship between running related injuries and ground reaction forces has been investigated, a limitation of previous research is that the heterogeneity of movement patterns within a control group creates confounding factors between variables. A potential solution is to use unsupervised cluster-based analyses to group individuals with similar ground reaction force features and thus investigate differences between identified clusters. Thus, the aim of this study was to investigate whether homogenous clusters exist within a large cohort of injured and healthy runners. The results show that two homogeneous clusters were identified using hierarchical cluster analysis and no significant differences in demographic variables were observed, nor were the proportion of injured and healthy runners between the two clusters. Thus, while there appears to be evidence for two distinct homogeneous kinetic clusters within our large sample of injured and healthy runners, there is no association between these kinetic clusters and running-related injuries.
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    An Investigation of Local Microarchitecture Topology Changes in Long-Duration Spaceflight
    (2022-11-16) Mielczarek, Conrad; Boyd, Steven; Manske, Sarah; Federico, Salvatore
    Microgravity-related bone loss presents a challenge to astronauts undergoing long-duration spaceflight. Astronauts undergo a period of substantial bone apposition upon return to Earth, which provides a unique opportunity to examine the mechanisms of bone remodeling. The objective of this study was to detect new trabecular bone connections in the form of topological bridging and quantify anisotropy changes in astronaut bone returning from the International Space Station (ISS). Seventeen United States Orbital Segment (USOS) astronauts participating in ISS missions of varying lengths (3.5-7 months) had their distal tibia and radius imaged using high-resolution peripheral quantitative computed tomography (HR-pQCT) before spaceflight, at landing (R+0M), and at 12 months post-flight (R+12M). Bone images were three-dimensionally rigid registered (3DR) longitudinally. A skeletonization decomposed the R+12M images to their underlying structure, allowing superimposition to the R+0M image where the difference highlighted areas of bone apposition during recovery. Anisotropy changes were tracked using mean intercept length (MIL). To compare the sensitivity of topology and anisotropy changes in astronauts, a reference was established using same-day repeat HR-pQCT distal tibia (n=90) and radius (n=89) images from control participants. The topology and anisotropy difference significance was assessed using a Wilcoxon rank-sum test between astronauts and control, while the anisotropic precision was determined from control scan root-mean-square coefficient of variation (RMSCV%). Astronauts’ group median apposition site average size was 1.2 times larger in the tibia and the same in the radius when compared to controls (p<0.01, p=0.64 respectively). Qualitatively examining the astronaut apposition sites revealed instances of bone bridging the space between two adjacent structures, indicating trabecular topological reconnection. Estimated precision for anisotropy measures from the control scans ranged from 0.9 to 1.3%, while the astronauts’ change in anisotropy ranged from -2.9 to 6.4% (group median -0.02%) during in-flight loss and -5.0 to 6.8% (group median 0.1%) during post-flight recovery. Bone resorption and apposition varied considerably between astronauts, with evidence of new topological connections across all participants. Several astronauts demonstrated a substantial change in anisotropy, suggesting directional alterations are concurrent with topology adaptation that occurs upon recovery on Earth.
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    Maternal Metabolism and Pregnancy: Predicting Pregnancy Outcomes Using Metabolomic Assessment
    (2024-10-31) Han, Ying Chieh; Shearer, Jane; Slater, Donna; Manske, Sarah; Duggan, Gavin
    Despite the significant health risks preterm birth poses to both mothers and babies, assessing the risk remains challenging due to its heterogeneous nature. Common risk factors, such as personal health history or lifestyle habits, have had some success in identifying at-risk mothers. However, their effectiveness is limited in asymptomatic and niche populations that lack distinct, identifiable preterm traits. Recent efforts have shifted toward exploring metabolic biomarkers for preterm prediction, aiming to capture the metabolic changes occurring during gestation. Nevertheless, most targeted compounds in these studies have been chosen based on their association with other underlying conditions, such as obesity or uterine disorders. With advancements in computational methods, particularly in machine learning, prediction models based on high-dimensional data have emerged as a promising new approach. This project aims to explore metabolic signatures associated with preterm birth by leveraging machine learning and untargeted metabolomics. We analyzed third-trimester serum samples from primigravid participants—first-time mothers—enrolled in the All Our Families (AOF) Cohort. Significant reductions in acylcarnitines and amino acid derivatives were identified, with various acylcarnitines, particularly butenylcarnitine, notably reduced in preterm mothers. These metabolites proved effective in predicting preterm birth, as shown by receiver operating characteristic (ROC) analysis. Next, we compared the performance of six different machine learning models in predicting preterm birth across a broader population from the AOF cohort, using Shapley Additive Explanations (SHAP) analysis to evaluate feature importance. We also assessed the impact of resampling techniques, such as bootstrapping. Linear models, including PLS-DA and linear logistic regression, demonstrated moderate predictive performance, while non-linear models like XGBoost and artificial neural networks (ANN) showed a slight advantage. Bootstrapping improved model accuracy and predictive strength, with varying degrees of enhancement across different models. Among the models tested, the bootstrap-resampled XGBoost model was the top performer in predicting preterm birth. SHAP analysis consistently identified acylcarnitines as the most significant class of metabolites in preterm prediction, with kynurenic acid also emerging as a contributing metabolite in several models following bootstrapping. The observed alterations in acylcarnitines suggest possible disruptions in lipid transport and energy metabolism in preterm mothers. The modeling results underscored the complexity of preterm prediction, while resampling techniques proved effective in mitigating the overtraining and low-variance challenges posed by the small sample size. This project introduces a novel approach to predicting preterm birth based on a combination of metabolites. As preterm birth continues to present significant risks to fetal health, the development of newer and more effective prediction models could improve maternal and fetal outcomes.