Browsing by Author "Peloquin, John M."
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Item Global and local identifiability analysis of a nonlinear biphasic constitutive model in confined compression(Journal of the Royal Society Interface, 2024-11-13) Peloquin, John M.; Elliott, Dawn M.Application of biomechanical models relies on model parameters estimated from experimental data. Parameter non-identifiability, when the same model output can be produced by many sets of parameter values, introduces severe errors yet has received relatively little attention in biomechanics and is subtle enough to remain unnoticed in the absence of deliberate verification. The present work develops a global identifiability analysis method in which cluster analysis and singular value decomposition are applied to vectors of parameter–output variable correlation coefficients. This method provides a visual representation of which specific experimental design elements are beneficial or harmful in terms of parameter identifiability, supporting the correction of deficiencies in the test protocol prior to testing physical specimens. The method was applied to a representative nonlinear biphasic model for cartilaginous tissue, demonstrating that confined compression data does not provide identifiability for the biphasic model parameters. This result was confirmed by two independent analyses: local analysis of the Hessian of a sum-of-squares error cost function and observation of the behaviour of two optimization algorithms. Therefore, confined compression data are insufficient for the calibration of general-purpose biphasic models. Identifiability analysis by these or other methods is strongly recommended when planning future experiments.Item MRI-based measurement of in vivo disc mechanics in a young population due to flexion, extension, and diurnal loading(JOR Spine, 2023-01-09) Meadows, Kyle D.; Peloquin, John M.; Newman, Harrah R.; Cauchy, Peter J. K.; Vresilovic, Edward J.; Elliott, Dawn M.Background: Intervertebral disc degeneration is often implicated in low back pain; however, discs with structural degeneration often do not cause pain. It may be that disc mechanics can provide better diagnosis and identification of the pain source. In cadaveric testing, the degenerated disc has altered mechanics, but in vivo, disc mechanics remain unknown. To measure in vivo disc mechanics, noninvasive methods must be developed to apply and measure physiological deformations. Aim: Thus, this study aimed to develop methods to measure disc mechanical function via noninvasive MRI during flexion and extension and after diurnal loading in a young population. This data will serve as baseline disc mechanics to later compare across ages and in patients. Materials & Methods: To accomplish this, subjects were imaged in the morning in a reference supine position, in flexion, in extension, and at the end of the day in a supine position. Disc deformations and vertebral motions were used to quantify disc axial strain, changes in wedge angle, and anterior–posterior (A-P) shear displacement. T2 weighted MRI was also used to evaluate disc degeneration via Pfirrmann grading and T2 time. All measures were then tested for effect of sex and disc level. Results: We found that flexion and extension caused level-dependent strains in the anterior and posterior of the disc, changes in wedge angle, and A-P shear displacements. Flexion had higher magnitude changes overall. Diurnal loading did not cause level-dependent strains but did cause small level-dependent changes in wedge angle and A-P shear displacements. Discussion: Correlations between disc degeneration and mechanics were largest in flexion, likely due to the smaller contribution of the facet joints in this condition. Conclusion: In summary, this study established methods to measure in vivo disc mechanical function via noninvasive MRI and established a baseline in a young population that may be compared to older subjects and clinical disorders in the future.Item Neural network segmentation of disc volume from magnetic resonance images and the effect of degeneration and spinal level(JOR Spine, 2024-09-04) Markhali, Milad I.; Peloquin, John M.; Meadows, Kyle D.; Newman, Harrah R.; Elliott, Dawn M.Background Magnetic resonance imaging (MRI) noninvasively quantifies disc structure but requires segmentation that is both time intensive and susceptible to human error. Recent advances in neural networks can improve on manual segmentation. The aim of this study was to establish a method for automatic slice-wise segmentation of 3D disc volumes from subjects with a wide range of age and degrees of disc degeneration. A U-Net convolutional neural network was trained to segment 3D T1-weighted spine MRI. Methods Lumbar spine MRIs were acquired from 43 subjects (23–83 years old) and manually segmented. A U-Net architecture was trained using the TensorFlow framework. Two rounds of model tuning were performed. The performance of the model was measured using a validation set that did not cross over from the training set. The model version with the best Dice similarity coefficient (DSC) was selected in each tuning round. After model development was complete and a final U-Net model was selected, performance of this model was compared between disc levels and degeneration grades. Results Performance of the final model was equivalent to manual segmentation, with a mean DSC = 0.935 ± 0.014 for degeneration grades I–IV. Neither the manual segmentation nor the U-Net model performed as well for grade V disc segmentation. Compared with the baseline model at the beginning of round 1, the best model had fewer filters/parameters (75%), was trained using only slices with at least one disc-labeled pixel, applied contrast stretching to its input images, and used a greater dropout rate. Conclusion This study successfully trained a U-Net model for automatic slice-wise segmentation of 3D disc volumes from populations with a wide range of ages and disc degeneration. The final trained model is available to support scientific use.