Multimodal Medical Image Registration and Fusion in 3D Conformal Radiotherapy Treatment Planning
Bin Li · InTech eBooks · 2011
Image Fusion 392 which reflects the definition.Therefore, the local standard deviation and energy standard are selected as the activity measure of the coefficients here.In computer vision, multi-sensor image fusion is the process of combining relevant information from two or more images into a single image.The resulting image will be more informative than any of the input images.For multimodal medical images, the important thing is the fusion of multimodal images, while the registration is the basis for image fusion.Given two image sets acquired from the same patient but at different times or with different devices, image registration is the process of finding a geometric transformation between the two respective image-based coordinate systems that maps a point in the first image set to the point in the second set that has the same patient-based coordinates, i.e. represents the same anatomic location (David M. et al., 2003).This notion presupposes that the anatomy is the same in the two image sets, an assumption that may not be precisely true if, for example, the patient has had a surgical resection between the two acquisitions.The situation becomes more complicated if two image sets that reflect different tissue characteristics [e.g.computed tomography (CT) and positron emission tomography (PET)] are to be registered.The idea can still be used that, if a candidate registration matches a set of similar features in the first image to a set of features in the second image that are also mutually similar, it is probably correct.For example, according to the principle of mutual information, homogeneous regions of the first image set should generally map into homogeneous regions in the second set (David M. et al., 2003).Usually there are several registration methods for different organs or tissues, such as rigid registration, affine registration and elastic registration(M.Betke et al., 2003) (Maintz J.B.A. et al., 1998) (T. Blaffert et al., 2004).In clinical diagnosis, the application of registration methods are just a compromise among the calculation time, accuracy and robustness.Up to now, it is still a major challenge to develop a rapid and automatic registration method whose accuracy can reach to that of manual guided registration (David M. et al., 2003) (Stefan Klein et al., 2007).For the moving organs, non-rigid registration methods are needed because the position, size and shape of internal organs and tissues are affected by the involuntary and other physiological movements of patient.Among the nonrigid registration methods, the Free-Form Deformation(FFD) method (Bardinet E et al., 1996) based on B-splines can control local deformation and change of the control points.For hierarchical B-splines is more smooth and accurate than the common B-splines, so good performance can be achieved if it is applied for floating image deformation (Lee Seungyong et al., 1997) (Ruechert D. et al., 1999) (Ino Fumihiko et al., 2005) (Zhiyong Xie et al., 2004).Thus, the presented automatic fine registration method is designed based on the hierarchical B-splines in this chapter.In 3D CRTP, the key problem for the non-rigid registration method of medical image is that it is a task of very time-consuming calculation process, which is unable to meet the clinical requirement to real-time process.In the mean time, the image data sets in 3D CRTP are so mass that it is very difficult to fuse the information of multimodal sequence images in real time.Thus some optimization measures should be taken.In this chapter, the FFD and maximum mutual information algorithm used in the presented registration method are both non-linear algorithms, so it can be taken as a multiobjective nonlinear problem.Here, the gradient descent algorithm and maximum mutual information entropy criterion are used to accelerate the searching speed for FFD coefficients.Moreover, parallel computing (Yasuhiro K. et al., 2004) (S.K.Warfield et al., 1998) can potentially further increase matching and fusion efficiency, so the parallel matching and fusion technique based on high performance computation is used in this chapter.