Non-rigid Registration of the Liver in Consecutive CT Studies for Assessment of Tumor Response to Radiofrequency Ablation

This paper introduces a non-rigid registration approach for tracking tumor response to radiofrequency ablation (RFA) across consecutive imaging studies. The method described here exploits the combined power of global and local alignment of pre- and post-treatment CT liver images for a given patient. The distinguishing characteristics of the system is that it can infer volumetric deformation based upon surface displacements using a linearly elastic finite element model (FEM). This technique may provide valuable information that could be beneficial in a range of surgical interventions as well as for the purposes of monitoring tissue response and therapy planning.

[1]  Luc Soler,et al.  Portal Vein Registration for the Follow-Up of Hepatic Tumours , 2004, MICCAI.

[2]  Miss A.O. Penney (b) , 1974, The New Yale Book of Quotations.

[3]  J. Bruix,et al.  Intention‐to‐treat analysis of surgical treatment for early hepatocellular carcinoma: Resection versus transplantation , 1999, Hepatology.

[4]  D. Yan,et al.  Reducing uncertainties in volumetric image based deformable organ registration. , 2003, Medical physics.

[5]  Michael Garland,et al.  Surface simplification using quadric error metrics , 1997, SIGGRAPH.

[6]  Carlo Bartolozzi,et al.  Early-stage hepatocellular carcinoma in patients with cirrhosis: long-term results of percutaneous image-guided radiofrequency ablation. , 2005, Radiology.

[7]  S Nahum Goldberg,et al.  Image-guided tumor ablation: standardization of terminology and reporting criteria. , 2005, Journal of vascular and interventional radiology : JVIR.

[8]  John C. Platt,et al.  Elastically deformable models , 1987, SIGGRAPH.

[9]  K. Paulsen,et al.  A computational model for tracking subsurface tissue deformation during stereotactic neurosurgery , 1999, IEEE Transactions on Biomedical Engineering.

[10]  Ron Kikinis,et al.  Registration of 3-d intraoperative MR images of the brain using a finite-element biomechanical model , 2000, IEEE Transactions on Medical Imaging.

[11]  D. Hill,et al.  Medical image registration , 2001, Physics in medicine and biology.

[12]  Berthold K. P. Horn,et al.  Closed-form solution of absolute orientation using orthonormal matrices , 1988 .

[13]  A. D'Amico,et al.  Evaluation of three-dimensional finite element-based deformable registration of pre- and intraoperative prostate imaging. , 2001, Medical physics.

[14]  D L McShan,et al.  Inclusion of organ deformation in dose calculations. , 2003, Medical physics.

[15]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Max A. Viergever,et al.  A survey of medical image registration , 1998, Medical Image Anal..

[17]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[18]  J. Z. Zhu,et al.  The finite element method , 1977 .

[19]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[20]  Ron Kikinis,et al.  Registration of 3D Intraoperative MR Images of the Brain Using a Finite Element Biomechanical Model , 2001, IEEE Trans. Medical Imaging.

[21]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[22]  Jonathan S. Lewin,et al.  Semiautomatic 3-D image registration as applied to interventional MRI liver cancer treatment , 2000, IEEE Transactions on Medical Imaging.

[23]  D. Hill,et al.  Non-rigid image registration: theory and practice. , 2004, The British journal of radiology.

[24]  S Nahum Goldberg,et al.  Image-guided tumor ablation: standardization of terminology and reporting criteria. , 2005, Radiology.

[25]  Gene H. Golub,et al.  Optimal Surface Smoothing as Filter Design , 1996, ECCV.

[26]  Klaus Gärtner,et al.  Meshing Piecewise Linear Complexes by Constrained Delaunay Tetrahedralizations , 2005, IMR.