Efficient image registration for the analysis of different phases of contrast-enhanced liver CT data

This paper describes the image registration block developed in the hepatic planner HepaPlan. The proposed method is intended to support clinical decisions about treatment of liver pathologies. The initial stage is the segmentation of the liver tissue as well as its internal structures and tumours in contrast-enhanced CT volumes. The second stage is non-rigid motion compensation due to CT data are acquired at different times, in arterial phase and venous phase. This image registration is necessary in order to fusion contrast-enhanced CT data and then to ease 3D volumetric measures, visualization of the liver and tumour, and to make comparisons with studies of the same patient at earlier times.

[1]  Brian B. Avants,et al.  Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge , 2011, IEEE Transactions on Medical Imaging.

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

[3]  Rafael Verdú,et al.  Frequency implementation of the Euler-Lagrange equations for variational image registration , 2008, IEEE Signal Process. Lett..

[4]  P M Schlag,et al.  Registration of different phases of contrast‐enhanced CT/MRI data for computer‐assisted liver surgery planning: Evaluation of state‐of‐the‐art methods , 2005, The international journal of medical robotics + computer assisted surgery : MRCAS.

[5]  ndrea,et al.  Liver transplantation for the treatment of small hepatocellular carcinomas in patients with cirrhosis. , 1996, The New England journal of medicine.

[6]  Kemal Tuncali,et al.  Intra-operative Multimodal Non-rigid Registration of the Liver for Navigated Tumor Ablation , 2009, MICCAI.

[7]  Nicholas Ayache,et al.  The Correlation Ratio as a New Similarity Measure for Multimodal Image Registration , 1998, MICCAI.

[8]  A. Khotanzad,et al.  A physics-based coordinate transformation for 3-D image matching , 1997, IEEE Transactions on Medical Imaging.

[9]  Rafael Verdú,et al.  A Fourier Domain Framework for Variational Image Registration , 2008, Journal of Mathematical Imaging and Vision.

[10]  Luc Soler,et al.  Liver Registration for the Follow-Up of Hepatic Tumors , 2005, MICCAI.

[11]  Jan Modersitzki,et al.  FLIRT: A Flexible Image Registration Toolbox , 2003, WBIR.

[12]  Max A. Viergever,et al.  elastix: A Toolbox for Intensity-Based Medical Image Registration , 2010, IEEE Transactions on Medical Imaging.

[13]  Jan Modersitzki,et al.  Numerical Methods for Image Registration , 2004 .

[14]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[15]  Luis Ibáñez,et al.  The ITK Software Guide , 2005 .

[16]  J. Modersitzki,et al.  A unified approach to fast image registration and a new curvature based registration technique , 2004 .

[17]  Martin Styner,et al.  Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets , 2009, IEEE Transactions on Medical Imaging.