Based On Polynomial Expansion Of The Diffusion Tensor Image Registration

In this dissertation, we proposed affine and deformable registration algorithms for diffusion tensor magnetic resonance images based on polynomial expansion model. Required by clinical research, then we proposed a geometric unbiased pop-ulation registration framework based on the pairwise registration method.Diffusion tensor imaging is an in-vivo magnetic resonance imaging method based on the diffusion of water molecule, which provides an insight for the white matter fiber of human brain. The comparison analysis of white matter fiber of brain function obstacle patients with healthy subjects is helpful for patients diagnosis. Since the scanning time and spatial coordinate is different, or the difference be-tween different subjects exists, registering the patients'image volume to the healthy subjects'volume is a basic step before analyzing the difference between them ac-curately. In application, researchers sometime use the atlas as the target volume replacing the healthy subjects'volume. For other cases, in order to find the common character of a special brain function obstacle disease, a group brain image volumes with the same disease symptom are required to be registered together.In the following work, we proposed pair-wise and group-wise registration algo-rithms focusing on the above cases required in clinical diagnosis. First, we proposed an affine and a deformable pair-wise registration algorithms based on quadratic poly-nomial expansion model. As the diffusion tensor images include a lot of information, we complete the registration of diffusion tensor images with two steps focusing on two characters which is the most important in clinical diagnosis. In the first step, we register the shape information of diffusion tensors, and then we reorient the ten-sor's orientation based on the first step's registration. Second, using the method developed for pair-wise registration, we further proposed unbiased population reg-istration for a group of volumes. Different with the former work on this problem using a selected volume as the target, which leads a bias to the selected target in registration, here we search for the geometric center position as the target and reg- ister all the volumes to the center, hereby we complete the registration without any bias.In a summary, we contribute several points in this thesis:First, we generalized the registration model based on polynomial expansion by proposing a more accurate displacement estimation formula, and then we developed a new affine registration algorithm for 3D medical images based on the proposed method. Second, we pro-posed a deformable registration algorithm named with multi-affine which aimed to register every local regions well. The output with an affine transform pixel by pixel is suitable for diffusion tensor reorientation. Third, we apply a pixel by pixel tensor reorientation strategy into diffusion tensor image registration based on the accurate and steady registration of multi-affine algorithm. Fourth, we further proposed an unbiased population registration for a group of image volumes, which is very useful in neuroscience, clinical research.