3D Non-Rigid Registration for Abdominal PET-CT and MR Images Using Mutual Information and Independent Component Analysis

The aim of this study is to develop a 3D registration algorithm for positron emission tomography/computed tomography (PET/CT) and magnetic resonance (MR) images acquired from independent PET/CT and MR imaging systems. Combined PET/CT images provide anatomic and functional information, and MR images have high resolution for soft tissue. With the registration technique, the strengths of each modality image can be combined to achieve higher performance in diagnosis and radiotherapy planning. The proposed method consists of two stages: normalized mutual information (NMI)-based global matching and independent component analysis (ICA)-based refinement. In global matching, the field of view of the CT and MR images are adjusted to the same size in the preprocessing step. Then, the target image is geometrically transformed, and the similarities between the two images are measured with NMI. The optimization step updates the transformation parameters to efficiently find the best matched parameter set. In the refinement stage, ICA planes from the windowed image slices are extracted and the similarity between the images is measured to determine the transformation parameters of the control points. B-spline–based freeform deformation is performed for the geometric transformation. The results show good agreement between PET/CT and MR images.

[1]  W P Segars,et al.  Realistic CT simulation using the 4D XCAT phantom. , 2008, Medical physics.

[2]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[3]  D. Bottomley,et al.  The use of CT-MR image registration to define target volumes in pelvic radiotherapy in the presence of bilateral hip replacements. , 2005, The British journal of radiology.

[4]  Thomas Martin Deserno,et al.  Survey: interpolation methods in medical image processing , 1999, IEEE Transactions on Medical Imaging.

[5]  Rafael C. González,et al.  Digital image processing, 3rd Edition , 2008 .

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

[7]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

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

[9]  Claude E. Shannon,et al.  A mathematical theory of communication , 1948, MOCO.

[10]  Michael Unser,et al.  Fast parametric elastic image registration , 2003, IEEE Trans. Image Process..

[11]  M. Viergever,et al.  Medical image matching-a review with classification , 1993, IEEE Engineering in Medicine and Biology Magazine.

[12]  Max A. Viergever,et al.  Mutual-information-based registration of medical images: a survey , 2003, IEEE Transactions on Medical Imaging.

[13]  Anand Rangarajan,et al.  A new point matching algorithm for non-rigid registration , 2003, Comput. Vis. Image Underst..

[14]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[15]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[16]  R. Shekhar,et al.  Automated 3-dimensional elastic registration of whole-body PET and CT from separate or combined scanners. , 2005, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[17]  X. Pennec,et al.  3D non-rigid registration by gradient descent on a Gaussian-windowed similarity measure using convolutions , 2000, Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis. MMBIA-2000 (Cat. No.PR00737).

[18]  Yong Choi,et al.  A small animal PET based on GAPDs and charge signal transmission approach for hybrid PET-MR imaging , 2011 .

[19]  Hany Farid,et al.  Medical image registration with partial data , 2006, Medical Image Anal..

[20]  M. Bhattacharya,et al.  Multi resolution medical image registration using maximization of mutual information & optimization by genetic algorithm , 2007, 2007 IEEE Nuclear Science Symposium Conference Record.

[21]  Yutaka Satoh,et al.  Using selective correlation coefficient for robust image registration , 2003, Pattern Recognit..