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Multiphysics modeling toward enhanced guidance in hepatic microwave ablation: a preliminary framework.

Jarrod A CollinsJon S HeiselmanLogan W ClementsDaniel B BrownMichael I Miga
Published in: Journal of medical imaging (Bellingham, Wash.) (2019)
We compare a surface-driven, model-based deformation correction method to a clinically relevant rigid registration approach within the application of image-guided microwave ablation for the purpose of demonstrating improved localization and antenna placement in a deformable hepatic phantom. Furthermore, we present preliminary computational modeling of microwave ablation integrated within the navigational environment to lay the groundwork for a more comprehensive procedural planning and guidance framework. To achieve this, we employ a simple, retrospective model of microwave ablation after registration, which allows a preliminary evaluation of the combined therapeutic and navigational framework. When driving registrations with full organ surface data (i.e., as could be available in a percutaneous procedure suite), the deformation correction method improved average ablation antenna registration error by 58.9% compared to rigid registration (i.e., 2.5 ± 1.1    mm , 5.6 ± 2.3    mm of average target error for corrected and rigid registration, respectively) and on average improved volumetric overlap between the modeled and ground-truth ablation zones from 67.0 ± 11.8 % to 85.6 ± 5.0 % for rigid and corrected, respectively. Furthermore, when using sparse-surface data (i.e., as is available in an open surgical procedure), the deformation correction improved registration error by 38.3% and volumetric overlap from 64.8 ± 12.4 % to 77.1 ± 8.0 % for rigid and corrected, respectively. We demonstrate, in an initial phantom experiment, enhanced navigation in image-guided hepatic ablation procedures and identify a clear multiphysics pathway toward a more comprehensive thermal dose planning and deformation-corrected guidance framework.
Keyphrases
  • radiofrequency ablation
  • catheter ablation
  • minimally invasive
  • computed tomography
  • big data
  • electronic health record
  • ultrasound guided
  • cross sectional
  • artificial intelligence
  • deep learning
  • data analysis