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A data-driven approach for real-time soft tissue deformation prediction using nonlinear presurgical simulations.

Haolin Liu1, Ye Han1, Daniel Emerson1

  • 1Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.

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This study introduces a novel method for accurately registering preoperative 3D models with intraoperative ultrasound data by predicting soft tissue deformations using fiducial marker tracking and neural networks, improving surgical guidance.

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Area of Science:

  • Medical imaging
  • Computational anatomy
  • Surgical robotics

Background:

  • Preoperative high-fidelity imaging (MRI, CT) provides detailed 3D models for minimally invasive surgery planning.
  • Intraoperative soft tissue deformations can cause significant mismatches between preoperative models and real-time ultrasound data, compromising surgical accuracy.
  • Accurate registration of preoperative models with intraoperative data is crucial for effective image-guided surgery.

Purpose of the Study:

  • To develop a fast and accurate method for predicting intraoperative soft tissue deformations.
  • To enable precise registration of high-fidelity preoperative models with low-fidelity intraoperative ultrasound images.
  • To enhance the reliability and success rates of image-guided minimally invasive surgeries.

Main Methods:

  • A novel approach using fiducial marker displacements to predict 3D soft tissue deformations.
  • Finite element method (FEM) to generate realistic deformation fields under various boundary conditions.
  • Autoencoder neural networks to reduce the dimensionality of deformation fields and map marker displacements to a latent space.

Main Results:

  • The proposed method accurately predicts intraoperative soft tissue deformations, achieving prediction errors as low as 0.5 mm.
  • The system demonstrates rapid prediction times, with each prediction taking less than 0.5 seconds.
  • Computational tests on head and neck tumor, kidney, and aorta models validate the approach's efficacy.

Conclusions:

  • The developed method offers a clinically relevant solution for accurate soft tissue deformation tracking during image-guided surgery.
  • Fast and precise registration of preoperative and intraoperative data can significantly improve surgical outcomes.
  • This approach has the potential to enhance the safety and effectiveness of minimally invasive surgical procedures.