A deformable model for tracking tumors across consecutive imaging studies
Gabriela Niculescu1, John L Nosher, M D Benjamin Schneider
1Center for Biomedical Imaging and Informatics, The Cancer Institute of New Jersey, New Brunswick, NJ, USA. gnicules@eden.rutgers.edu
Objective:
A deformable registration technique was developed and evaluated to track and quantify tumor response to radiofrequency ablation for patients with liver malignancies.
Materials And Methods:
The method uses the combined power of global and local alignment of pre- and post-treatment computed tomography image data sets. The strategy of the algorithm is to infer volumetric deformation based upon surface displacements using a linearly elastic finite element model (FEM). Using this framework, the major challenge for tracking tumor location is not the tissue mechanical properties for FEM modeling but rather the evaluation of boundary conditions. Three different methods were systematically investigated to automatically determine the boundary conditions defined by the correspondences on liver surfaces.
Results:
Using both 2D synthetic phantoms and imaged 3D beef liver data we performed gold standard registration while measuring the accuracy of non-rigid deformation. The fact that the algorithms could support mean displacement error of tumor deformation up to 2 mm indicates that this technique may serve as a useful tool for surgical interventions. The method was further demonstrated and evaluated using consecutive imaging studies for three liver cancer patients.
Conclusion:
The FEM-based surface registration technique provides accurate tracking and monitoring of tumor and surrounding tissue during the course of treatment and follow-up.
Insights
A new deformable registration technique accurately tracks liver tumors after radiofrequency ablation. This finite element model (FEM) based method aids surgical interventions by quantifying tumor response to treatment.
Area of Science:
- Medical imaging
- Computational anatomy
- Surgical oncology
Background:
- Radiofrequency ablation (RFA) is a common treatment for liver malignancies.
- Accurate monitoring of tumor response to RFA is crucial for effective treatment planning and patient outcomes.
- Existing methods for tracking tumor changes post-RFA may lack precision in quantifying volumetric deformation.
Purpose of the Study:
- To develop and evaluate a deformable registration technique for tracking and quantifying tumor response to RFA in liver cancer patients.
- To assess the accuracy of a finite element model (FEM) based approach for inferring volumetric deformation from surface displacements.
- To investigate methods for automatically determining boundary conditions for FEM modeling in liver surface registration.
Main Methods:
- The technique combines global and local image alignment of pre- and post-treatment computed tomography (CT) datasets.
- A linearly elastic finite element model (FEM) infers volumetric deformation from surface displacements.
- Three methods were explored to automatically determine boundary conditions using liver surface correspondences.
Main Results:
- The technique demonstrated accurate non-rigid deformation measurement on synthetic phantoms and 3D beef liver data.
- The algorithm achieved a mean displacement error of up to 2 mm for tumor deformation.
- Validation was performed on consecutive imaging studies from three liver cancer patients.
Conclusions:
- The FEM-based surface registration technique offers accurate tracking and monitoring of tumors and surrounding tissues during RFA treatment and follow-up.
- This method shows potential as a valuable tool for surgical interventions in liver cancer management.
- The findings support the clinical utility of advanced image registration for precision oncology.
