A deformable model for tracking tumors across consecutive imaging studies

• Published in: International journal for computer assisted radiology and surgery Vol. 4; no. 4; pp. 337 - 347
• Main Authors: Niculescu, Gabriela, Nosher, John L., Schneider, M. D. Benjamin, Foran, David J.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.06.2009
• Subjects: Carcinoma, Hepatocellular - diagnostic imaging; Carcinoma, Hepatocellular - surgery; Catheter Ablation - methods; Computer Imaging; Computer Science; Computer Simulation; Diagnosis, Differential; Finite Element Analysis; Follow-Up Studies; Health Informatics; Humans; Imaging; Imaging, Three-Dimensional - methods; Liver Neoplasms - diagnostic imaging; Liver Neoplasms - surgery; Medicine; Medicine & Public Health; Original Article; Pattern Recognition and Graphics; Phantoms, Imaging; Radiology; Reproducibility of Results; Surgery; Tomography, X-Ray Computed - methods; Vision; Radiofrequency ablation; Finite element modeling; Deformable registration
• ISSN: 1861-6410; 1861-6429; 1861-6429
• DOI: 10.1007/s11548-009-0298-x

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.