Elastography is a technique utilizing continuum mechanics and optical, ultrasonic or magnetic resonance imaging modalities to determine viscoelastic properties within soft tissue. It has shown promise in many clinical applications due to the wide range in values between diseased and healthy tissue. Within anisotropic tissues under varying pre-stressed conditions, such as striated muscle, it has historically been difficult to accurately determine viscoelastic properties. This is because most reconstruction (post-processing) algorithms ignore non-homogenous boundary conditions, such as a pre-stress, and assume the tissue is linearly viscoelastic and isotropic. The use of finite element analysis (FEA) in the reconstruction process may help solve this problem by providing a forward based solution that can be matched to measured displacement maps through a minimization process. We propose a method to develop anatomically accurate anisotropic models of muscle by combining diffusion tensor imaging (DTI) and anatomic MR images through solving an image registration problem and then discretizing the model for use in ANSYS finite element software. 

A model of the human soleus was created by segmenting the lower limb of anatomic MR images using ITK-SNAP and then smoothing in Mudbox. STL files were then imported into MATLAB where we developed an algorithm to reorganize nodes to a predetermined resolution and slice thickness.  DTI images were subjected to a similar process by using DSI Studio to segment and create a fiber orientation matrix. Averages of each fiber orientation were determined and applied at each node as the main direction of the anisotropic voxel. Voxel information was then overlaid on the nodal model, where direction informed a discretization algorithm on how to connect each node and form elements. [Research supported by NSF Grant No. 1852691 and NIH Grant No. AR071162.]

Creating more accurate anisotropic computational models through combined DTI/MRI for use in finite element modeling in muscle elastography

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