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Diffusion-Tensor Imaging Versus Digitization in Reconstructing the Masseter Architecture.

Cristina Falcinelli1, Zhi Li2, Wilfred W Lam3

  • 1Orthopaedic Biomechanics Laboratory, Sunnybrook Research Institute, 2075 Bayview Avenue, Toronto, ON M4N 3M5, Canada e-mails: .

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Summary
This summary is machine-generated.

Magnetic resonance diffusion-tensor imaging (DTI) accurately captures muscle fiber orientation for craniomaxillofacial finite element models. However, fiber bundle length measurements require further refinement for precise physiological loading simulations.

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Area of Science:

  • Biomechanics
  • Medical Imaging
  • Computational Modeling

Background:

  • Accurate craniomaxillofacial (CMF) finite element (FE) models require detailed representation of skeletal geometry, material properties, and physiological loading.
  • Current CMF FE models often simplify muscle representation, neglecting fiber bundles (FBs) and their differential activation.
  • Magnetic resonance diffusion-tensor imaging (DTI) offers potential for reconstructing 3D muscle FB arrangements, but quantitative validation is limited.

Purpose of the Study:

  • To compare the 3D muscle FB arrangement, specifically pennation angle (PA) and fiber bundle length (FBL), derived from DTI with manual digitization in a human masseter muscle.
  • To assess the suitability of DTI-generated FB data for patient-specific CMF FE modeling of physiological muscle loading.

Main Methods:

  • Acquisition of CT, MR-proton density, and MR-DTI images from a single human cadaveric specimen.
  • Reconstruction of bone and masseter muscle surfaces from CT and MR-proton density images, respectively.
  • Estimation of PA and FBL from DTI-derived FBs using streamline tracking (STT) (n=193) and comparison with manually digitized FBs (n=181) via Mann-Whitney test.

Main Results:

  • DTI-derived PAs showed no significant difference compared to manual digitization (p=0.411), indicating DTI's capability to represent FB orientation.
  • A significant difference was found in FBL between DTI and manual digitization (p<0.01), potentially due to tractography stopping criteria causing early termination and length variability.
  • Despite FBL discrepancies, DTI-derived FB orientation data is suitable for simulating the directionality of physiological muscle forces in CMF FE models.

Conclusions:

  • MR-DTI can effectively reconstruct muscle FB orientation, crucial for accurately simulating force transmission in CMF FE models.
  • Further optimization of tractography algorithms is needed to improve the accuracy of DTI-derived FBL measurements.
  • DTI-based muscle FB data holds significant promise for enhancing the physiological realism of patient-specific CMF FE models.