MULTI MUSCLE PATTERNS IN POST SURGICAL TOTAL KNEE ARTHROPLASTY

INTRODUCTION Total Knee Arthroplasty (TKA) is a common surgical intervention for end stage Osteoarthritis (OA).  It is implemented for pain relief and joint function restoration. There is an increasing expectation from patients concerning post-operative performance [1]. However, TKA patients often experience functional impairment such as movement, loading, and muscle activation pattern abnormalities [2]. This project focused on identifying temporal differences in muscle activations in lower limb electromyographs (EMG) between healthy persons and TKA patients, using wavelet patterns and machine learning classification. METHODS Ten post-surgical female TKA patients (TKA: 19±3 months post-surgery; 61.9±8.8 yrs; BMI 28.0±5.3) and 9 healthy age matched female controls (CON: 61.4±7.4 yrs; BMI 25.6±2.4) participated in this study. EMGs for 7 lower leg muscles during both level walking and stair climbing were collected. Muscles of interest were: tibialis anterior (TA), gastrocnemius (GAS), semitendinosus (SEM), biceps femoris (BF), rectus femoris (RF), vastus medialis (VM) and vastus lateralis (VL). Five acceptable EMG trials per subject were selected for analysis, normalized in time (stance phase ±30%) and processed in Matlab using a wavelet transform [3]. EMG data were normalized to the total signal intensity. Support Vector Machine (SVM) classification was performed for all subjects using an iterative thresholding approach and leave-one-out cross-validation. Rates of classification (called recognition rates) were deemed significant if they were greater than or equal to 68.4% (according to binomial test). SVM discriminants were visualized to aid in the identification of functional differences between CON and TKA groups. RESULTS Mean multi muscle patterns (MMPs) for walking (Figure 1) demonstrate substantive differences between groups. The muscles that gave significant recognition rates in level walking were: VM (68.4%) and BF (73.7%); and in stair climbing were: BF (84.2%), SEM (73.7%), GAS (68.4%), and TA (68.4%). DISCUSSION Application of a SVM with iterative thresholding provided significant recognition rates between groups, for both walking and stair-stepping tasks. The stepping task was characterized by a greater number of muscles with significant recognition rates, as well as the highest recognition rates. Temporal activation differences, indicative of muscle co-activation by TKA subjects, were observable in the discriminant pattern. In walking, BF and VM were active in mid-stance, illustrated as a red activity pattern in the discriminant. BF activity shifted from pre-HS to post-HS to coincide with the main activity in the quadriceps muscles (VL, VM, RF). Similarly, in stair climbing, TA displayed a co-activation with GAS at mid-stance. Further, SEM and BF displayed pronounced activation patterns at mid-stance and at TO and early swing. This may indicate an activation strategy to assist in hip extension for the TKA group. CONCLUSION The analysis approach chosen in this study identified functional differences between healthy subjects and TKA patients. There is evidence for the employment of co-activation strategy by the TKA group in both walking and stair climbing

PRINCIPAL COMPONENT ANALYSIS OF A SINGLE LEG SQUAT

INTRODUCTION Single leg squat (SLS) is a clinical test that is useful in assessing the biomechanical performance of the lower limb. In particular, SLS may provide indicators of muscle strength and balance of a patient [1]. In order to measure the movements present in the single leg squat, a motion capture system is typically used in conjunction with a set of reflective markers. This use of motion capture enables the study of the kinetics and kinematics of the squat for the chosen sample population. Since the use of motion capture in recording the SLS results in a continuous dataset, it is helpful to compress the data to compare the results. Principal component analysis (PCA) is a technique that may be used to reduce the dimensionality in a given dataset so fewer comparisons need to be made. In addition, by interpreting these principal components, any significant differences found can be related back to the biomechanics of the squat. Therefore, the main objective of this study is to run PCA on the SLS data, and look for kinematic differences between the injured and non-injured populations. A secondary objective is to interpret the principal components to find a meaning for any differences found. METHODS The SLS was performed by 50 subjects with intra-articular knee injuries and 50 healthy controls (age: 21.3±2.9, BMI: 24.4±3.7). Each subject performed 3 trials consisting of 5 squats to approximately 45° knee flexion each.  The data was collected using a motion capture system (Motion Analysis, Santa Rosa, CA) at a sampling frequency of 240 Hz. The 3D motion capture data was imported into MATLAB, and the knee FE angle was computed according to [2]. For each subject, the squat that reached closest to 45° knee flexion was chosen for the PCA. PCA was computed on the knee flexion angles of all subjects with respect to time. The resulting principal components (PCs) represent the variation between subjects accounted for by each time point. The first 3 principal components were selected for further analysis, in order to account for 95% of the total variance in the data. In order to determine the meaning of the selected PCs, patient waveforms corresponding to the highest and lowest 5% of PC scores were compared and interpreted, according to Deluzio [3]. The subject data was reduced to 3 dimensions by plotting the FE angle along the selected PC axes. To study the difference between the injured and non-injured subjects, a student t-test was performed on the PC scores for each of the three components, with p value at 0.05. RESULTS High values along the first PC axis were found to correspond to a leftward shift in the subject waveform. A high second PC was found to correspond to a more gradual FE curve, and a high third PC corresponded to a larger downward slope compared to the upward component. When performing a t-test, the PC 1 values for the non-injured group were found to be statically lower (p < 0.0164) than the injured group. No significant difference was noticed along the PC 2 or PC 3 axes. DISCUSSION AND CONCLUSIONS The interpretation of the first PC was taken to be the shift in time of the squat from the average pattern. Based on the results of the t-test, the non-injured group shows a lower PC 1. Based on the interpretation above, the non-injured group appears to spend a larger proportion of the total time in the downward portion of the squat when compared to the non-injured group. This may indicate a deficiency in controlling the flexion descent for subjects suffering from knee injury, which may in turn indicate a possibility for future therapeutic intervention

Examining the relationship between biomechanics and GMFCS level in children with cerebral palsy

INTRODUCTION Cerebral palsy (CP) is a non-progressive lesion of the developing central nervous system that affects the development of posture and motor control [1]. The Gross Motor Function Classification System (GMFCS) is a clinical tool used to categorize children with CP based on their functional competence. It consists of five levels indicating increasing functional disability. Due to the wide range of motor outcomes in CP, some children may not fit the mould of one of the levels and the classification becomes subjective. Biomechanics provides a quantitative approach that may allow for more specific functional classification [2]. Quantifying biomechanics adaptations may support patient-specific clinical disability classification, and inform longitudinal assessment of the efficacy of therapy intervention. The aim of this study was to determine the relationship between GMFCS levels and subject-specific gait biomechanics in children with CP. It was hypothesized that joint angles and moments differ between participants with GMFCS levels 1 and 2.METHODS Gait biomechanics of 24 children with hemiplegic or diplegic CP were analyzed as part of a secondary data analysis approved by the local ethics committee. Participants were classified according to GMFCS: Level 1 (n=12) - 12.2±1.9 yrs, 1.54±0.07 m, 46.4±12.5 kg; Level 2 (n=12) - 13.6±1.6 yrs, 1.56±0.03 m, 47.8±10.5 kg. All data were collected as part of a clinical consult over the past seven years. The participants had reflective markers placed according to the Helen-Hayes set up while they walked barefoot at their preferred speed on a raised wooden walkway.Data were processed in Visual 3D (C-Motion, USA) using subject-specific lower limb models. These models created local coordinate systems for each of the segments, which were then used to calculate the kinematics (segment motions) and kinetics (forces and moments) for the hip, knee, and ankle joints. Joint angle and moment time curves for the left leg were computed using standard approaches. All data were normalized to stance phase from heel-strike to toe-off (101 data points).  Joint moments were normalized to body mass.Statistical analyses of kinematic and kinetic waveforms were conducted in MATLAB (MathWorks, USA) using statistical parametric mapping (spm1d.org). This analysis method performs statistical tests over a range of values to determine where two sets of waveforms are different from each other. Differences in gait velocity were assessed using Student’s t-test in SPSS (IBM, USA).RESULTS  Figure 1. Left hip adductor/abductor moment. The x-axis represents the stance phase from heel strike (HS) to toe off (TO) and the y-axis is the moment in Nm/kg. The blue lines represent GMFCS Level I participants (12) and red are Level II (12).  The thin lines indicate individual participants and the thick lines denote the mean of the corresponding GMFCS level.In examining the three lower extremity joints biomechanics, two significant differences in hip joint moments were identified with respect to GMFCS levels. GMFCS level 1 participants displayed significantly greater hip abductor (p=0.002, t-test, Figure 1) and hip internal rotation (p=0.047, t-test) moments between 17-26% and 18-21% of stance phase respectively. No significant differences were observed for the knee or ankle kinetics. The kinematics showed no significant differences in any of the three joints. Further, Level 1 participants walked significantly faster (p=0.009, Student’s t-test, level 1 1.1±0.1ms-1, level 2 0.9±0.2 ms-1). DISCUSSION The results of this investigation partially supported the hypothesis, demonstrating few between-group differences in gait biomechanics. The differences found in the hip abductor and internal rotation moments could be due to a number of contributing factors. They could be related to greater abductor muscle weakness in participants with lower functional competence, the differences in walking speeds found, or due to the effects of performing movements with spasticity. Spasticity is commonly seen in children with CP and is increased muscle tone that causes resistance to movement. Its influence on the resulting kinematics and kinetics of the participants in this study has not been determined.ImplicationsInterestingly, most kinematic and kinetic measures in the lower extremities are not significantly different according to GMFCS levels.  The lack of differences may be explained by the substantial variability of biomechanical measures across GMFCS groups. The variability of biomechanics outcomes between participants supports the view that GMFCS classification is likely not sensitive to child-specific function.Future Directions In order to address this shortcoming, further research will be conducted to determine the relationship between biomechanical outcomes and alternative clinical measures of functional capacity (e.g., spasticity and fatigue). Research questions to address in future research include: What is the association of spasticity and gait biomechanics abnormality? Do children with CP display distinct biomechanical clusters? Non-supervised machine-learning may be used to identify associations of biomechanical and clinical data to explore the second question. Such groupings may be beneficial for use as clinical diagnostics and therapy progression monitoring.ACKNOWLEDGEMENTS The NSERC Undergraduate Student Research Award provided funding support for this project.  Funding is acknowledged from the Vi Riddell Pediatric Rehabilitation Research Program, (Alberta Children’s Hospital Foundation) and Alberta Innovates Technology Futures.REFERENCESPalisano et al. DMCN 1997; 39:214-223.Dziuba et al. Acta Bioeng Biomech 2013; Vol. 15, No. 2

COMPARISON OF TWO OPTICAL IMAGING SYSTEMS TO REDUCE RADIATION IN ADOLESCENTS WITH SCOLIOSIS

INTRODUCTION Adolescent Idiopathic Scoliosis (AIS) is a three-dimensional (3D) deformity of the spine characterized by abnormal lateral curvature and vertebral rotation affecting 2-3% of adolescents [1]. The current clinical diagnostic and monitoring method consists of full torso X-rays where the Cobb angle, a measure of spinal deviation from the vertical, is used to determine the magnitude of the deformity. Two major limitations are associated with this approach.  First, the routine exposure to radiation has been linked to an increased risk of cancer in scoliotic patients [2]. Second, the Cobb angle is inadequate to fully define the deformity because it is a two-dimensional measure. A holistic approach to define the deformity and reduce radiation exposure is needed. Changes in spinal curvature alter torsal shape making the use of surface topography (ST) a potential alternative to detect and monitor AIS progression in 3D [3] as well as reduce periodic radiation exposure. The majority of recent attempts to validate ST for clinical implementation have used commercial fringe topographic (FT) methods, which are expensive and take prolonged captures. A novel low-cost photogrammetric system that takes instantaneous captures has been developed to remove errors resulting from movement during a capture and increase torso reconstruction accuracy [4]. The effect of the improved accuracy on the ST measures in the new system is not yet understood. The aim of this study was to compare FT and photogrammetric data, thereby providing context for ST measures resulting from the new system. METHODS Models of four AIS (1M, 3F) and four normal (1M, 3F) subjects between the ages of 9-16 were reconstructed via FT (InSpeck Inc, Montreal; now owned by Creaform, Lévis) and photogrammetric methods in order to compare ST measures in three regions, i.e. upper (T7), middle (T12) and lower (L4). Captures from the two systems were taken consecutively while subjects were in a positioning frame to reduce movement artifacts between systems. MeshLab was used to generate meshes from the photogrammetric point clouds. A custom scoliosis code [5] calculated ST measures from meshes between T1 and S1 (Fig 1.). Anatomical landmarks determined each individual’s fixed reference frame. RESULTS A repeated measures multivariate analysis of variability compared 11 distinct ST indices calculated from torsal cross-sections (Fig. 1) [6]. There were two subject groups, normal and scoliosis; two optical methods, FT and photogrammetry; and three analyzed levels, T7, T12 and L4. Statistically significant (SS) differences were found in ST measures between methods (p < 0.001) and spinal levels (p = 0.032). Further tests revealed SS difference in both the normal (p = 0.006) and scoliosis (p = 0.002) groups ST measures from the two methods. DISCUSSION AND CONCLUSIONS The photogrammetry method produced different ST measures from the FT method. Further method comparison includes distorting photogrammetry data until it matches FT data. Increasing sample size will provide SS information on interaction effects and the effects of improved accuracy and repeatability of the novel system vs. InSpeck (accuracy: 0.3 mm vs. 1.29+/- 0.45mm; repeatability: 0.19mm vs. 1.4mm; [4,6]) on ST measures

PROCESS VALIDATION IN CALCULATING MEDIAN PROXIMITY IN TIBIOFEMORAL CARTILAGE DEFORMATION UNDER FULL BODY LOADING

INTRODUCTION Knee osteoarthritis (OA) is characterized by progressive and irreversible degradation of tibiofemoral (TF) cartilages. Anterior cruciate ligament (ACL) rupture is a known risk factor for post-traumatic OA (PTOA) [1]. However, there are currently no in-vivo tests to diagnose pre-radiographic PTOA. Following injury, the cartilage macromolecular matrix weakens, cartilage swells and consequently cartilage softness increases [2]. This research investigates the in-vivo effects of ACL injury on cartilage deformation magnitude and rate under full body loading. The objective of this project was to determine the consequences of cartilage model mesh types and incremental mesh simplifications on the accuracy of resultant TF cartilage proximities. METHODS The affected knee of a 37 year old male PTOA subject (ACL deficient for 6 years) was imaged using Magnetic Resonance Imaging (FIESTA sequence; 3T GE Discovery 750). 3D TF bone and cartilage models were generated in Amira (VSG, Germany). The subject performed a 10 minute standing task in the Dual Fluoroscopic (DF) laboratory. DF images (32LP/mm) were collected at 6Hz. Bone alignments were reconstructed from DF images using AutoScoper (Brown University, USA) and cartilage models were co-registered. TF cartilage surface proximity was determined as the surface normal distance from each triangular mesh face onto the opposing cartilage. (Matlab, v2014b, The MathWorks, USA). The effects on surface proximities of three types of triangular cartilage surface meshes, generated in Amira, were analysed: 1) Basic Simplification - reducing face numbers with variable mesh size; 2) Remeshed Surface – isotropic mesh; 3) Iteratively Smoothed Remeshed Surface. Face numbers were reduced at 10% increments from the original surface for each surface type. RESULTS Median proximity errors for the Remeshed Surface were consistently smaller than the other mesh types across all four cartilage surface compartments. The medial tibial plateau displayed a rapid increase in error (Figure 1) indicating a high sensitivity to model simplification. This may have been due to its more complex surface geometry. The maximum acceptable error was chosen to match the minimum detectable displacement of 0.05mm for this DF system [3]. DISCUSSION AND CONCLUSIONS The findings of this investigation identified differences in the error of cartilage surface proximities under loading due to the use of different mesh types and simplifications. The smoothing technique used by Amira did not consistently converge to a surface and the variable triangle size in Basic Simplification affected the computation of proximity, resulting in unpredictable error spikes in cartilage surface proximity calculations. The results suggest that surface modeling parameters are surface geometry specific. The limiting case of the medial tibial plateau showed the optimal simplification was 0.594mm triangle mesh side length (40% of the original faces). These results inform ongoing work toward an in-vivo pre-radiographic diagnostic of PTOA

Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss

Recent advances in deep learning algorithms have led to significant benefits for solving many medical image analysis problems. Training deep learning models commonly requires large datasets with expert-labeled annotations. However, acquiring expert-labeled annotation is not only expensive but also is subjective, error-prone, and inter-/intra- observer variability introduces noise to labels. This is particularly a problem when using deep learning models for segmenting medical images due to the ambiguous anatomical boundaries. Image-based medical diagnosis tools using deep learning models trained with incorrect segmentation labels can lead to false diagnoses and treatment suggestions. Multi-rater annotations might be better suited to train deep learning models with small training sets compared to single-rater annotations. The aim of this paper was to develop and evaluate a method to generate probabilistic labels based on multi-rater annotations and anatomical knowledge of the lesion features in MRI and a method to train segmentation models using probabilistic labels using normalized active-passive loss as a "noise-tolerant loss" function. The model was evaluated by comparing it to binary ground truth for 17 knees MRI scans for clinical segmentation and detection of bone marrow lesions (BML). The proposed method successfully improved precision 14, recall 22, and Dice score 8 percent compared to a binary cross-entropy loss function. Overall, the results of this work suggest that the proposed normalized active-passive loss using soft labels successfully mitigated the effects of noisy labels

DYNAMIC VALIDATION OF TIBIOFEMORAL KINEMATICS MEASURED USING A DUAL FLUOROSCOPY SYSTEM: A MARKER-BASED APPROACH

INTRODUCTION Knee joint cartilage degeneration in post-traumatic osteoarthritis is initiated at the point of injury and progresses through abnormal movement mechanics [1]. Anterior cruciate ligament rupture influences the development and progression of osteoarthritis [1], however the specific in vivo effects of abnormal bone and joint kinematics and kinetics on human cartilage health remain largely unknown.  Quantifying in vivo knee kinematics with submillimeter accuracies may elucidate injurious movement alterations.  Dual Fluoroscopy (DF) allows for accurate, high-speed, and non-invasive skeletal kinematics assessment, but requires validation.  The aim of this project was to quantify the in vitro accuracy and precision of a high-speed dual fluoroscopy system for measuring 6 degree of freedom (DOF) knee kinematics obtained from a marker-less 2D-3D registration approach as compared to the gold standard marker-based method. For this preliminary work, we hypothesized that the precision of inter-bead 3D Euclidean distance measurement is less than or equal to 0.10 mm [2]. METHODS Upon approval by the local ethics committee, one female cadaveric human leg was obtained through the local body donation program. Four 3mm metal beads were surgically implanted in the distal femur and proximal tibia.  Thereafter, the limb was scanned using computed tomography (CT). Following imaging, the soft tissues of the proximal shaft of the femur were dissected to expose the bone and the femoral head was removed. The proximal shaft of the femur was then fixed in a custom-made metal cylinder using fixation screws and potted using polymethyl methacrylate (PMMA). The free end of the metal cylinder was in turn fixed to an articulated 6 DOF tripod mount (Manfrotto, Italy).  In the DF laboratory the limb was suspended in the DF field of view using a custom steel frame. A rope pulley system, fixed around the ankle joint, was used to manipulate the limb. DF images were acquired at 60 Hz during manipulation of the limb into knee flexion. All images were distortion corrected and calibrated using established procedures. Marker-based tracking was conducted on 75 DF frames using in-house software to determine the 2D coordinates of the bead centroids in each image pair.  Subsequently, a modified direct linear transform was applied to obtain the 3D bead centroid coordinates. Matlab (MathWorks, v2014b, USA) code was written in order to determine the Euclidean distance between beads. RESULTSTable 1: The mean distance between beads in the femur and tibia ± SD (mm) calculated over 75 DF frames.  Right: Camera 1 DF image demonstrating the numbering of beads.DISCUSSION AND CONCLUSIONS The data indicated inter-bead distance variabilities consistent with previously observed system errors (for static imaging), when investigating a moving limb (Table 1). The observed variations could be due to multiple contributors. A lack of bead sphericity and bead deformation, as a result of surgical bead implantation, may have caused erroneous bead centroid estimates. Further, DF image distortions may have persisted even after distortion correction, contributing to observed error. Future steps include improved image calibration using a sophisticated bundle adjustment algorithm to further reduce system errors [3]