Motion Estimation with Finite-Element Biomechanical Models and Tracking Constraints from Tagged MRI.

Noninvasive measurements of tissue deformation provide bio-mechanical insights of an organ, which can be used as clinical functional biomarkers or experimental data for validating computational simulations. However, acquisition of 3D displacement information is susceptible to experimental inconsistency and limited scan time. In this research, we describe the process of tracking tagged magnetic resonance imaging (MRI) as enforcing harmonic phase conservation in finite-element (FE) models. This concept is demonstrated as a tool for motion estimation in a brain motion phantom, the heart, and the tongue. Our results demonstrate that the new methodology offers robustness to edge and large-displacement artifacts, and that it can be seamlessly coupled with numerical simulations for estimating fiber stretch in residually stressed tissue, or for inverse identification of muscle activation.

[1]  Aart J. Nederveen,et al.  Validation of continuously tagged MRI for the measurement of dynamic 3D skeletal muscle tissue deformation. , 2012, Medical physics.

[2]  E. H. Clayton,et al.  Quantitative imaging methods for the development and validation of brain biomechanics models. , 2012, Annual review of biomedical engineering.

[3]  K. Bathe Finite Element Procedures , 1995 .

[4]  Aaron T. Hess,et al.  Tracking Myocardial Motion From Cine DENSE Images Using Spatiotemporal Phase Unwrapping and Temporal Fitting , 2007, IEEE Transactions on Medical Imaging.

[5]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[6]  Jerry L. Prince,et al.  Imaging heart motion using harmonic phase MRI , 2000, IEEE Transactions on Medical Imaging.

[7]  Hongqiang Guo,et al.  An augmented Lagrangian method for sliding contact of soft tissue. , 2012, Journal of biomechanical engineering.

[8]  Dimitris N. Metaxas,et al.  Three-dimensional motion reconstruction and analysis of the right ventricle using tagged MRI , 2000, Medical Image Anal..

[9]  Gerard A. Ateshian,et al.  Multigenerational interstitial growth of biological tissues , 2010, Biomechanics and modeling in mechanobiology.

[10]  R. D. Wood,et al.  Nonlinear Continuum Mechanics for Finite Element Analysis , 1997 .

[11]  Benjamin J. Ellis,et al.  FEBio: finite elements for biomechanics. , 2012, Journal of biomechanical engineering.

[12]  E Kuhl,et al.  Heterogeneous growth-induced prestrain in the heart. , 2015, Journal of biomechanics.

[13]  Alexander I. Veress,et al.  Strain Measurement in the Left Ventricle During Systole with Deformable Image Registration , 2007, FIMH.

[14]  E. H. Ibrahim,et al.  Myocardial tagging by Cardiovascular Magnetic Resonance: evolution of techniques--pulse sequences, analysis algorithms, and applications , 2011, Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance.

[15]  Jerry L. Prince,et al.  Incompressible Deformation Estimation Algorithm (IDEA) From Tagged MR Images , 2012, IEEE Transactions on Medical Imaging.

[16]  Jonghye Woo,et al.  Subject-specific biomechanical modelling of the oropharynx with application to speech production , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[17]  Andrew K Knutsen,et al.  Improved measurement of brain deformation during mild head acceleration using a novel tagged MRI sequence. , 2014, Journal of biomechanics.

[18]  K. T. Ramesh,et al.  An axonal strain injury criterion for traumatic brain injury , 2012, Biomechanics and modeling in mechanobiology.