Pose estimation has promised to impact healthcare by enabling more practical methods to quantify nuances of human movement and biomechanics. However, despite the inherent connection between pose estimation and biomechanics, these disciplines have largely remained disparate. For example, most current pose estimation benchmarks use metrics such as Mean Per Joint Position Error, Percentage of Correct Keypoints, or mean Average Precision to assess performance, without quantifying kinematic and physiological correctness - key aspects for biomechanics. To alleviate this challenge, we develop OpenCapBench to offer an easy-to-use unified benchmark to assess common tasks in human pose estimation, evaluated under physiological constraints. OpenCapBench computes consistent kinematic metrics through joints angles provided by an open-source musculoskeletal modeling software (OpenSim). Through OpenCapBench, we demonstrate that current pose estimation models use keypoints that are too sparse for accurate biomechanics analysis. To mitigate this challenge, we introduce SynthPose, a new approach that enables finetuning of pre-trained 2D human pose models to predict an arbitrarily denser set of keypoints for accurate kinematic analysis through the use of synthetic data. Incorporating such finetuning on synthetic data of prior models leads to twofold reduced joint angle errors. Moreover, OpenCapBench allows users to benchmark their own developed models on our clinically relevant cohort. Overall, OpenCapBench bridges the computer vision and biomechanics communities, aiming to drive simultaneous advances in both areas.
Background: Rotator cuff related shoulder pain (RCRSP) is common among older adults. While exercise therapy is often recommended for RCRSP, the relationship between physical activity levels and self-reported outcomes, including pain and function, is unclear. The primary objective of this study was to investigate whether pain is related to subjective or objective physical activity levels in older adults with RCRSP. The secondary objectives were to study whether (i) self-reported outcome measures explain variance in physical activity levels, ii) whether those who participate in exercises that target their painful shoulder have better self-reported outcomes than those who report whole-body exercise.Methods: This study was a cross-sectional design.46 participants with RCRSP participated in this study from which 35 had complete datasets. Questionnaires were used to assess physical activity, pain, physical function, general health (including depressive symptoms) and self-efficacy. Physical activity levels were also measured objectively using an accelerometer. Findings: Neither pain nor other self-reported outcomes were related to subjective or objective physical activity levels. Participants that regularly completed shoulder-specific exercise had significantly higher exercise self-efficacy than those who completed non-specific exercise (P=0.011; d=0.91). Interpretation: This research did not find evidence of a relationship between pain or self-reported outcomes and physical activity levels in adults with RCRSP. Those who self-reported regularly exercising their injured shoulder had higher exercise self-efficacy than those who did not. These findings have clinical implications, suggesting that strategies to boost exercise self-efficacy may be important for older adults with RCRSP.
Analyzing anatomic shapes of tissues and organs is pivotal for accurate disease diagnostics and clinical decision-making. One prominent disease that depends on anatomic shape analysis is osteoarthritis, which affects 30 million Americans. To advance osteoarthritis diagnostics and prognostics, we introduce
The Osteoarthritis Initiative was a longitudinal study of osteoarthritis that prospectively collected a trove of imaging data including Multi-Echo Spin-Echo (MESE) data for cartilage T2 relaxation time assessment in one knee. While this data remains underutilized, several analyses have been performed over the past years to assess T2 sensitivity to OA exploiting the OAI dataset. However, fitting procedures to compute T2 maps from the MESE data in the OAI largely rely on mono-exponential modelling, which is inherently sub-optimal as it does not account for stimulated echoes produced by RF slice-profile and B1 inhomogeneities and it often fails to account for low SNR in longer TEs. To mitigate errors, a common practice is to drop the first echo and fit the remaining 6 echoes at the expense of discarding information and degrading SNR efficiency. T2 fitting of MESE data using Extended phase graph (EPG) modelling, whether based on nonlinear least square (NLSQ) dictionary matching (DM) or deep learning (DL), can account for stimulated echoes, and can potentially provide more accurate and robust fitting for T2 mapping in the OAI. This work proposes to 1) set up three EPG fitting approaches for T2 mapping in the OAI dataset (NLSQ-based, DM-based and DL-based), 2) assess methods for their performance in accuracy and robustness to noise using both simulations and in-vivo data, and 3) compare them against standard fitting methods based on mono-exponential methods. MESE simulations were performed in Matlab (R2022b) using the EPG formalism considering the sequence parameters of OAI data. Hanning-windowed Sinc pulses were used for slice-profile simulations. Three EPG-based fitting methods and three exponential (EXP)-based methods used in prior OAI literature were considered and are summarized in Figure 1. To investigate fitting accuracy and repeatability robustness to noise experiments with simulations as well as using in-vivo data from OAI database were performed. 2000 MESE signals were simulated with T2 ranging from 20 to 80 ms and B1 ranging from 0.9 to 1.1. Each method was used to fit T2 values after adding increasing levels of Gaussian noise. For each SNR, the procedure was repeated 10 times with re-sampling of noise. Accuracy was assessed using the mean percentage error (MPE) and mean absolute percentage error (MAPE), while repeatability was assessed with coefficient of variation (CV). MESE data from 5 subjects in the OAI database (1 in each KLG) were corrupted by injecting Gaussian noise to the MESE images twice with increasing variance. Method repeatability was assessed through Bland-Altman (BA) analysis. To assess agreement among fitting methods and how this affected inference of the presence of OA, 50 subjects were randomly selected from the OAI dataset: 10 subjects (5F & 5M) per KLG (0,1,2,3,4). Patellar (P) and Tibiofemoral (TF) cartilage T2 maps were computed pixel-wise with all the described fitting methods. Mean T2 was computed in 7 ROIs (P, MT, LT, central and posterior regions for the MF and LF) extracted using automatic segmentation of DESS images registered to MESE images. BA analysis was used to asses pair-wise agreement in mean T2 values using Limits of Agreement (LOA) and mean bias. The Lin's concordance coefficient (ρc) and CV were also used as metrics of agreement. A logistic regression model was then performed using OA presence (KLG≥2) as a dependent variable, T2 as independent variable and body mass index as covariate in the MT and the central MF regions. MPE, and CV for different fitting methods from the simulation experiment are reported in Fig. 2 (top panel) as a function of SNR. The EPG methods outperformed the exponential-based methods in terms of accuracy at all SNR levels. The EPG-DL approach had the best overall performance in terms of accuracy and repeatability. In-vivo analysis of LOA and CV as function of SNR (Fig. 2, bottom panel) showed that the EPG-based methods had higher repeatability than EXP-based procedures. The EPG-DL approach also had the best overall performance in in vivo data. T2 pair-wise method comparison in-vivo (Fig. 3) showed that overall, the EPG-based methods had higher inter-method agreement (- 0.1 ms < Bias < 0.05 ms, 0.2 < LOA < 1.13 ms, ρc ∼ 0.99) compared to exponential-based methods (-0.7 ms < Bias < 2 ms, 3.2 ms < LOA < 5.3 ms, 0.86 < ρc < 0.94). Poor agreement was found between EPG-based and exponential-based methods (0.34 < ρc < 0.44, Bias ∼ 10 ms and LOA ∼ 4 ms). With reference to Tab. 1, using the EPG-based methods resulted in higher T2-associated OA odd ratios than EXP-based methods in the MT region (EPG OR ∼ 1.19, 1.13 < EXP OR < 1.18). EPG-based T2 relaxation time fitting methods resulted in more accurate and repeatable T2 estimation than EXP-based approaches in simulations. Preliminary in-vivo experiments also suggest higher robustness to noise of EPG methods compared to EXP-based methods. Furthermore, the EPG-methods showed high inter-method agreement. The lower T2 inter-method agreement of EXP-based approaches greatly affected inference of OA severity. Despite the limited sample size, these results suggest that EPG-based methods to compute T2 maps in the OAI may result in low method-dependent variability. Among the EPG-based methods, the DL approach showed the highest repeatability. The high repeatability of EPG-DL paired with its computational efficiency may allow better exploitation of T2 information in the OAI dataset, especially when longitudinal analysis is involved. We plan to use the EPG-DL approach to compute T2 maps of the entire OAI dataset and make it publicly available for researchers to use it.
While ultrasound (US) measures of the subacromial space (SAS) have demonstrated excellent reliability, measurements are typically captured by experts with extensive ultrasound experience. Further, the agreement between US measured SAS width and other imaging modalities has not been explored. This research evaluated the agreement between SAS measures captured by novice and expert raters and between US and magnetic resonance imaging (MRI). This study also evaluated the effect of US transducer tilt on measured SAS.Nine men and nine women participated in this study. US images were captured by a novice and expert with the participant in both seated and supine positions. An inclinometer was fixed to the US probe to measure transducer tilt. SAS width was measured in real time from freeze framed images. MRI images were captured, and the humerus and acromion manually segmented. The SAS width was measured using a custom algorithm.Intraclass correlation coefficients (ICCs) between novice and expert raters were 0.74 and 0.63 for seated and supine positions, respectively. Intra-rater agreement was high for both novice (ICC = 0.83-0.84) and expert (ICC ≥ 0.94) raters. Agreement between US and MRI was poor (ICC = 0.21-0.49) but linearly related.Moderate agreement between novice and expert raters was demonstrated, while the agreement between US and MRI was poor. High intra-rater reliability within each rater suggests that US measures of the SAS may be completed by a novice with introductory training.
