Registration of 3D FMT and CT Images of Mouse Via Affine Transformation with Bayesian Iterative Closest Points

Authors:
Xia Zheng;Xiaobo Zhou;Youxian Sun;Stephen T. Wong
Affiliations:
Zhejiang University, National Laboratory of Industrial Control Technology, Hangzhou 310027, P.R. China;HCNR-CBI, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02215, USA and Functional Molecular Imaging Center, Brigham and Women's Hospital, MA 02115, USA;Zhejiang University, National Laboratory of Industrial Control Technology, Hangzhou 310027, P.R. China;HCNR-CBI, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02215, USA and Functional Molecular Imaging Center, Brigham and Women's Hospital, MA 02115, USA
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Year:
2007

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Abstract

It is difficult to directly co-register the 3D FMT (Fluorescence Molecular Tomography) image of a small tumor in a mouse whose maximal diameter is only a few mm with a larger CT image of the entire animal that spans about ten cm. This paper proposes a new method to register 2D flat and projected CT image first to facilitate the registration between small 3D FMT images and large CT images. And a novel algorithm Bayesian Iterative Closest Point (BICP) is introduced and validated in 2D affine registration. The visualization of the alignment of the 3D FMT and CT image through 2D registration shows promising results that would lead to automated 3D registration.