Simple Methods for Scanner Drift Normalization Validated for Automatic Segmentation of Knee Magnetic Resonance Imaging:with data from the Osteoarthritis Initiative

Scanner drift is a well-known magnetic resonance imaging (MRI) artifact characterized by gradual signal degradation and scan intensity changes over time. In addition, hardware and software updates may imply abrupt changes in signal. The combined effects are particularly challenging for automatic image analysis methods used in longitudinal studies. The implication is increased measurement variation and a risk of bias in the estimations (e.g. in the volume change for a structure). We proposed two quite different approaches for scanner drift normalization and demonstrated the performance for segmentation of knee MRI using the fully automatic KneeIQ framework. The validation included a total of 1975 scans from both high-field and low-field MRI. The results demonstrated that the pre-processing method denoted Atlas Affine Normalization significantly removed scanner drift effects and ensured that the cartilage volume change quantifications became consistent with manual expert scores

MECHANICAL METRICS OF THE PROXIMAL FEMUR ARE PRECISE AND ASSOCIATED WITH HIP MUSCLE PROPERTIES: A MAGNETIC RESONANCE BASED FINITE ELEMENT STUDY

Proximal femoral (hip) fractures are a life-threatening injury which affects 30,000 Canadians annually. Improved muscle and bone strength assessment methods may reduce fracture occurrence rates in the future. Magnetic resonance (MR) imaging has potential to assess proximal femoral bone strength in vivo through usage of finite element (FE) modeling. Though, to precisely assess bone strength, knowledge of a technique’s measurement error is needed. Hip muscle properties (e.g., lean muscle and fat area) are intrinsically linked to proximal femoral bone strength; however, it is unclear which muscles and properties are most closely associated with bone strength. This thesis is focused on MR-based FE modeling (MR-FE) of the proximal femur and surrounding muscle properties (e.g., hip abductor fat area, hip extensor muscle area). The specific objectives of this research were 1) to characterize the short-term in vivo measurement precision of MR-FE outcomes (e.g., failure load) of the proximal femur for configurations simulating fall and stance loading, and 2) explore associations between upper thigh muscle and fat properties (e.g., hip abductor fat area, knee extensor muscle area) with MR-FE failure loads of the proximal femur. In vivo precision errors (assessed via root mean square coefficient of variation, CV%RMS from repeated measures) of MR-FE outcomes ranged from 3.3-11.8% for stress and strain outcomes, and 6.0-9.5% for failure loads. Hip adductor muscle area and total muscle area correlated with failure load of the fracture-prone neck and intertrochanteric region under both fall and stance loading (correlation coefficients ranged from 0.416-0.671). This is the first study to report the in vivo short-term precision errors of MR-FE outcomes at the proximal femur. Also, this is the first study to relate upper-thigh muscle and fat properties with MR-FE derived failure loads. Results indicate that MR-FE outcomes have comparable precision to computed tomography (CT) based FE outcomes and are related to hip muscle area