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Joseph D Towles - One of the best experts on this subject based on the ideXlab platform.

  • towards a realistic Biomechanical Model of the thumb the choice of kinematic description may be more critical than the solution method or the variability uncertainty of musculoskeletal parameters
    Journal of Biomechanics, 2003
    Co-Authors: Francisco J Valerocuevas, Elise M Johanson, Joseph D Towles
    Abstract:

    A Biomechanical Model of the thumb can help researchers and clinicians understand the clinical problem of how anatomical variability contributes to the variability of outcomes of surgeries to restore thumb function. We lacka realistic Biomechanical Model of the thumb because of the variability/uncertainty of musculoskeletal parameters, the multiple proposed kinematic descriptions and methods to solve the muscle redundancy problem, and the paucity of data to validate the Model with in vivo coordination patterns and force output. We performed a multi-stage validation of a Biomechanical computer Model against our measurements of maximal static thumbtip force and fine-wire electromyograms (EMG) from 8 thumb muscles in each of five orthogonal directions in key and opposition pinch postures. A low-friction point-contact at the thumbtip ensured that subjects did not produce thumbtip torques during force production. The 3-D, 8-muscle Biomechanical thumb Model uses a 5-axis kinematic description with orthogonal and intersecting axes of rotation at the carpometacarpal and metacarpophalangeal joints. We represented the 50 musculoskeletal parameters of the Model as stochastic variables based on experimental data, and ran Monte Carlo simulations in the ''inverse'' and ''forward'' directions for 5000 random instantiations of the Model. Two inverse simulations (predicting the distribution of maximal static thumbtip forces and the muscle activations that maximized force) showed that: the Model reproduces at most 50% of the 80 EMG distributions recorded (eight muscle excitations in 5 force directions in two postures); and well-directed thumbtip forces of adequate magnitude are predicted only if accompanied by unrealistically large thumbtip torques (0.6470.28 N m). The forward simulation (which fed the experimental distributions of EMG through random instantiations of the Model) resulted in misdirected thumbtip force vectors (within 74.3724.5 from the desired direction) accompanied by doubly large thumbtip torques (1.3270.95 N m). Taken together, our results suggest that the variability and uncertainty of musculoskeletal parameters and the choice of solution method are not the likely reason for the unrealistic predictions obtained. Rather, the kinematic description of the thumb we used is not representative of the transformation of net joint torques into thumbtip forces/torques in the human thumb. Future efforts should focus on validating alternative kinematic descriptions of the thumb. r 2003 Elsevier Science Ltd. All rights reserved.

  • towards a realistic Biomechanical Model of the thumb the choice of kinematic description may be more critical than the solution method or the variability uncertainty of musculoskeletal parameters
    Journal of Biomechanics, 2003
    Co-Authors: Francisco J Valerocuevas, Elise M Johanson, Joseph D Towles
    Abstract:

    A Biomechanical Model of the thumb can help researchers and clinicians understand the clinical problem of how anatomical variability contributes to the variability of outcomes of surgeries to restore thumb function. We lack a realistic Biomechanical Model of the thumb because of the variability/uncertainty of musculoskeletal parameters, the multiple proposed kinematic descriptions and methods to solve the muscle redundancy problem, and the paucity of data to validate the Model with in vivo coordination patterns and force output. We performed a multi-stage validation of a Biomechanical computer Model against our measurements of maximal static thumbtip force and fine-wire electromyograms (EMG) from 8 thumb muscles in each of five orthogonal directions in key and opposition pinch postures. A low-friction point-contact at the thumbtip ensured that subjects did not produce thumbtip torques during force production. The 3-D, 8-muscle Biomechanical thumb Model uses a 5-axis kinematic description with orthogonal and intersecting axes of rotation at the carpometacarpal and metacarpophalangeal joints. We represented the 50 musculoskeletal parameters of the Model as stochastic variables based on experimental data, and ran Monte Carlo simulations in the "inverse" and "forward" directions for 5000 random instantiations of the Model. Two inverse simulations (predicting the distribution of maximal static thumbtip forces and the muscle activations that maximized force) showed that: the Model reproduces at most 50% of the 80 EMG distributions recorded (eight muscle excitations in 5 force directions in two postures); and well-directed thumbtip forces of adequate magnitude are predicted only if accompanied by unrealistically large thumbtip torques (0.64+/-0.28Nm). The forward simulation (which fed the experimental distributions of EMG through random instantiations of the Model) resulted in misdirected thumbtip force vectors (within 74.3+/-24.5 degrees from the desired direction) accompanied by doubly large thumbtip torques (1.32+/-0.95Nm). Taken together, our results suggest that the variability and uncertainty of musculoskeletal parameters and the choice of solution method are not the likely reason for the unrealistic predictions obtained. Rather, the kinematic description of the thumb we used is not representative of the transformation of net joint torques into thumbtip forces/torques in the human thumb. Future efforts should focus on validating alternative kinematic descriptions of the thumb.

Francisco J Valerocuevas - One of the best experts on this subject based on the ideXlab platform.

  • towards a realistic Biomechanical Model of the thumb the choice of kinematic description may be more critical than the solution method or the variability uncertainty of musculoskeletal parameters
    Journal of Biomechanics, 2003
    Co-Authors: Francisco J Valerocuevas, Elise M Johanson, Joseph D Towles
    Abstract:

    A Biomechanical Model of the thumb can help researchers and clinicians understand the clinical problem of how anatomical variability contributes to the variability of outcomes of surgeries to restore thumb function. We lacka realistic Biomechanical Model of the thumb because of the variability/uncertainty of musculoskeletal parameters, the multiple proposed kinematic descriptions and methods to solve the muscle redundancy problem, and the paucity of data to validate the Model with in vivo coordination patterns and force output. We performed a multi-stage validation of a Biomechanical computer Model against our measurements of maximal static thumbtip force and fine-wire electromyograms (EMG) from 8 thumb muscles in each of five orthogonal directions in key and opposition pinch postures. A low-friction point-contact at the thumbtip ensured that subjects did not produce thumbtip torques during force production. The 3-D, 8-muscle Biomechanical thumb Model uses a 5-axis kinematic description with orthogonal and intersecting axes of rotation at the carpometacarpal and metacarpophalangeal joints. We represented the 50 musculoskeletal parameters of the Model as stochastic variables based on experimental data, and ran Monte Carlo simulations in the ''inverse'' and ''forward'' directions for 5000 random instantiations of the Model. Two inverse simulations (predicting the distribution of maximal static thumbtip forces and the muscle activations that maximized force) showed that: the Model reproduces at most 50% of the 80 EMG distributions recorded (eight muscle excitations in 5 force directions in two postures); and well-directed thumbtip forces of adequate magnitude are predicted only if accompanied by unrealistically large thumbtip torques (0.6470.28 N m). The forward simulation (which fed the experimental distributions of EMG through random instantiations of the Model) resulted in misdirected thumbtip force vectors (within 74.3724.5 from the desired direction) accompanied by doubly large thumbtip torques (1.3270.95 N m). Taken together, our results suggest that the variability and uncertainty of musculoskeletal parameters and the choice of solution method are not the likely reason for the unrealistic predictions obtained. Rather, the kinematic description of the thumb we used is not representative of the transformation of net joint torques into thumbtip forces/torques in the human thumb. Future efforts should focus on validating alternative kinematic descriptions of the thumb. r 2003 Elsevier Science Ltd. All rights reserved.

  • towards a realistic Biomechanical Model of the thumb the choice of kinematic description may be more critical than the solution method or the variability uncertainty of musculoskeletal parameters
    Journal of Biomechanics, 2003
    Co-Authors: Francisco J Valerocuevas, Elise M Johanson, Joseph D Towles
    Abstract:

    A Biomechanical Model of the thumb can help researchers and clinicians understand the clinical problem of how anatomical variability contributes to the variability of outcomes of surgeries to restore thumb function. We lack a realistic Biomechanical Model of the thumb because of the variability/uncertainty of musculoskeletal parameters, the multiple proposed kinematic descriptions and methods to solve the muscle redundancy problem, and the paucity of data to validate the Model with in vivo coordination patterns and force output. We performed a multi-stage validation of a Biomechanical computer Model against our measurements of maximal static thumbtip force and fine-wire electromyograms (EMG) from 8 thumb muscles in each of five orthogonal directions in key and opposition pinch postures. A low-friction point-contact at the thumbtip ensured that subjects did not produce thumbtip torques during force production. The 3-D, 8-muscle Biomechanical thumb Model uses a 5-axis kinematic description with orthogonal and intersecting axes of rotation at the carpometacarpal and metacarpophalangeal joints. We represented the 50 musculoskeletal parameters of the Model as stochastic variables based on experimental data, and ran Monte Carlo simulations in the "inverse" and "forward" directions for 5000 random instantiations of the Model. Two inverse simulations (predicting the distribution of maximal static thumbtip forces and the muscle activations that maximized force) showed that: the Model reproduces at most 50% of the 80 EMG distributions recorded (eight muscle excitations in 5 force directions in two postures); and well-directed thumbtip forces of adequate magnitude are predicted only if accompanied by unrealistically large thumbtip torques (0.64+/-0.28Nm). The forward simulation (which fed the experimental distributions of EMG through random instantiations of the Model) resulted in misdirected thumbtip force vectors (within 74.3+/-24.5 degrees from the desired direction) accompanied by doubly large thumbtip torques (1.32+/-0.95Nm). Taken together, our results suggest that the variability and uncertainty of musculoskeletal parameters and the choice of solution method are not the likely reason for the unrealistic predictions obtained. Rather, the kinematic description of the thumb we used is not representative of the transformation of net joint torques into thumbtip forces/torques in the human thumb. Future efforts should focus on validating alternative kinematic descriptions of the thumb.

Karol Miller - One of the best experts on this subject based on the ideXlab platform.

  • patient specific Biomechanical Model as whole body ct image registration tool
    Medical Image Analysis, 2015
    Co-Authors: Revanth Reddy Garlapati, Grand Roman Joldes, Ron Kikinis, Karol Miller, Barry J Doyle, Adam Wittek
    Abstract:

    Whole-body computed tomography (CT) image registration is important for cancer diagnosis, therapy planning and treatment. Such registration requires accounting for large differences between source and target images caused by deformations of soft organs/tissues and articulated motion of skeletal structures. The registration algorithms relying solely on image processing methods exhibit deficiencies in accounting for such deformations and motion. We propose to predict the deformations and movements of body organs/tissues and skeletal structures for whole-body CT image registration using patient-specific non-linear Biomechanical Modelling. Unlike the conventional Biomechanical Modelling, our approach for building the Biomechanical Models does not require time-consuming segmentation of CT scans to divide the whole body into non-overlapping constituents with different material properties. Instead, a Fuzzy C-Means (FCM) algorithm is used for tissue classification to assign the constitutive properties automatically at integration points of the computation grid. We use only very simple segmentation of the spine when determining vertebrae displacements to define loading for Biomechanical Models. We demonstrate the feasibility and accuracy of our approach on CT images of seven patients suffering from cancer and aortic disease. The results confirm that accurate whole-body CT image registration can be achieved using a patient-specific non-linear Biomechanical Model constructed without time-consuming segmentation of the whole-body images.

  • Biomechanical Model as a Registration Tool for Image-Guided Neurosurgery: Evaluation Against BSpline Registration
    Annals of Biomedical Engineering, 2013
    Co-Authors: Ahmed Mostayed, Revanth Reddy Garlapati, Grand Roman Joldes, Adam Wittek, Ron Kikinis, Simon K Warfield, Aditi Roy, Karol Miller
    Abstract:

    In this paper we evaluate the accuracy of warping of neuro-images using brain deformation predicted by means of a patient-specific Biomechanical Model against registration using a BSpline-based free form deformation algorithm. Unlike the BSpline algorithm, biomechanics-based registration does not require an intra-operative MR image which is very expensive and cumbersome to acquire. Only sparse intra-operative data on the brain surface is sufficient to compute deformation for the whole brain. In this contribution the deformation fields obtained from both methods are qualitatively compared and overlaps of Canny edges extracted from the images are examined. We define an edge based Hausdorff distance metric to quantitatively evaluate the accuracy of registration for these two algorithms. The qualitative and quantitative evaluations indicate that our biomechanics-based registration algorithm, despite using much less input data, has at least as high registration accuracy as that of the BSpline algorithm.

  • intra operative update of neuro images comparison of performance of image warping using patient specific Biomechanical Model and bspline image registration
    Intra-operative Update of Neuro-images: Comparison of Performance of Image Warping Using Patient-Specific Biomechanical Model and BSpline Image Regist, 2013
    Co-Authors: Ahmed Mostayed, Revanth Reddy Garlapati, Grand Roman Joldes, Adam Wittek, Ron Kikinis, Simon K Warfield, Karol Miller
    Abstract:

    This paper compares the warping of neuro-images using brain deformation predicted by means of patient-specific Biomechanical Model with the neuro-image registration using BSpline-based free form deformation algorithm. Deformation fields obtained from both algorithms are qualitatively compared and overlaps of edges extracted from the images are examined. Finally, an edge-based Hausdorff distance metric is defined to quantitatively evaluate the accuracy of registration for these two algorithms. From the results it is concluded that the patient-specific Biomechanical Model ensures higher registration accuracy than the BSpline registration algorithm.

Hans Forssberg - One of the best experts on this subject based on the ideXlab platform.

  • validation of a new Biomechanical Model to measure muscle tone in spastic muscles
    Neurorehabilitation and Neural Repair, 2011
    Co-Authors: Pavel G Lindberg, Johan Gaverth, Mominul Islam, Anders Fagergren, Jorgen Borg, Hans Forssberg
    Abstract:

    Background. There is no easy and reliable method to measure spasticity, although it is a common and important symptom after a brain injury. Objective. The aim of this study was to develop and validate a new method to measure spasticity that can be easily used in clinical practice. Methods. A Biomechanical Model was created to estimate the components of the force resisting passive hand extension, namely (a) inertia (IC), (b) elasticity (EC), (c) viscosity (VC), and (d) neural components (NC). The Model was validated in chronic stroke patients with varying degree of hand spasticity. Electromyography (EMG) was recorded to measure the muscle activity induced by the passive stretch. Results. The Model was validated in 3 ways: (a) NC was reduced after an ischemic nerve block, (b) NC correlated with the integrated EMG across subjects and in the same subject during the ischemic nerve block, and (c) NC was velocity dependent. In addition, the total resisting force and NC correlated with the modified Ashworth score...

  • validation of a new Biomechanical Model to measure muscle tone in spastic muscles
    Neurorehabilitation and Neural Repair, 2011
    Co-Authors: Pavel G Lindberg, Johan Gaverth, Mominul Islam, Anders Fagergren, Jorgen Borg, Hans Forssberg
    Abstract:

    Background. There is no easy and reliable method to measure spasticity, although it is a common and important symptom after a brain injury. Objective. The aim of this study was to develop and validate a new method to measure spasticity that can be easily used in clinical practice. Methods. A Biomechanical Model was created to estimate the components of the force resisting passive hand extension, namely (a) inertia (IC), (b) elasticity (EC), (c) viscosity (VC), and (d) neural components (NC). The Model was validated in chronic stroke patients with varying degree of hand spasticity. Electromyography (EMG) was recorded to measure the muscle activity induced by the passive stretch. Results. The Model was validated in 3 ways: (a) NC was reduced after an ischemic nerve block, (b) NC correlated with the integrated EMG across subjects and in the same subject during the ischemic nerve block, and (c) NC was velocity dependent. In addition, the total resisting force and NC correlated with the modified Ashworth score...

Adam Wittek - One of the best experts on this subject based on the ideXlab platform.

  • patient specific Biomechanical Model as whole body ct image registration tool
    Medical Image Analysis, 2015
    Co-Authors: Revanth Reddy Garlapati, Grand Roman Joldes, Ron Kikinis, Karol Miller, Barry J Doyle, Adam Wittek
    Abstract:

    Whole-body computed tomography (CT) image registration is important for cancer diagnosis, therapy planning and treatment. Such registration requires accounting for large differences between source and target images caused by deformations of soft organs/tissues and articulated motion of skeletal structures. The registration algorithms relying solely on image processing methods exhibit deficiencies in accounting for such deformations and motion. We propose to predict the deformations and movements of body organs/tissues and skeletal structures for whole-body CT image registration using patient-specific non-linear Biomechanical Modelling. Unlike the conventional Biomechanical Modelling, our approach for building the Biomechanical Models does not require time-consuming segmentation of CT scans to divide the whole body into non-overlapping constituents with different material properties. Instead, a Fuzzy C-Means (FCM) algorithm is used for tissue classification to assign the constitutive properties automatically at integration points of the computation grid. We use only very simple segmentation of the spine when determining vertebrae displacements to define loading for Biomechanical Models. We demonstrate the feasibility and accuracy of our approach on CT images of seven patients suffering from cancer and aortic disease. The results confirm that accurate whole-body CT image registration can be achieved using a patient-specific non-linear Biomechanical Model constructed without time-consuming segmentation of the whole-body images.

  • Biomechanical Model as a Registration Tool for Image-Guided Neurosurgery: Evaluation Against BSpline Registration
    Annals of Biomedical Engineering, 2013
    Co-Authors: Ahmed Mostayed, Revanth Reddy Garlapati, Grand Roman Joldes, Adam Wittek, Ron Kikinis, Simon K Warfield, Aditi Roy, Karol Miller
    Abstract:

    In this paper we evaluate the accuracy of warping of neuro-images using brain deformation predicted by means of a patient-specific Biomechanical Model against registration using a BSpline-based free form deformation algorithm. Unlike the BSpline algorithm, biomechanics-based registration does not require an intra-operative MR image which is very expensive and cumbersome to acquire. Only sparse intra-operative data on the brain surface is sufficient to compute deformation for the whole brain. In this contribution the deformation fields obtained from both methods are qualitatively compared and overlaps of Canny edges extracted from the images are examined. We define an edge based Hausdorff distance metric to quantitatively evaluate the accuracy of registration for these two algorithms. The qualitative and quantitative evaluations indicate that our biomechanics-based registration algorithm, despite using much less input data, has at least as high registration accuracy as that of the BSpline algorithm.

  • intra operative update of neuro images comparison of performance of image warping using patient specific Biomechanical Model and bspline image registration
    Intra-operative Update of Neuro-images: Comparison of Performance of Image Warping Using Patient-Specific Biomechanical Model and BSpline Image Regist, 2013
    Co-Authors: Ahmed Mostayed, Revanth Reddy Garlapati, Grand Roman Joldes, Adam Wittek, Ron Kikinis, Simon K Warfield, Karol Miller
    Abstract:

    This paper compares the warping of neuro-images using brain deformation predicted by means of patient-specific Biomechanical Model with the neuro-image registration using BSpline-based free form deformation algorithm. Deformation fields obtained from both algorithms are qualitatively compared and overlaps of edges extracted from the images are examined. Finally, an edge-based Hausdorff distance metric is defined to quantitatively evaluate the accuracy of registration for these two algorithms. From the results it is concluded that the patient-specific Biomechanical Model ensures higher registration accuracy than the BSpline registration algorithm.