Automatic Rigid and Deformable Medical Image Registration

by Yu, Hongliang.

Advanced imaging techniques have been widely used to study the anatomical structure and functional metabolism in medical and clinical applications. Images are acquired from a variety of scanners (CT/MR/PET/SPECT/Ultrasound), which provide physicians with complementary information to diagnose and detect specific regions of a patient. However, due to the different modalities and imaging orientations, these images rarely align spatially. They need to be registered for consistent and repeatable analyses. Therefore, image registration is a critical component of medical imaging applications. Since the brains of rodent animal mostly behave in the rigid manner, their alignments may be generally described by a rigid model without local deformation. Mutual information is an excellent strategy to measure the statistical dependence of image from mono-modality or multi-modalities. The registration system with rigid model was developed to combine with mutual information for functional magnetic resonance (fMRI) analysis, which has five components: (1) rigid body and affine transformation, (2) mutual information as the similarity measure, (3) partial volume interpolation, (4) multidimensional optimization techniques, and (5) multi-resolution acceleration. In this research three innovative registration systems were designed with the configurations of the mutual information and optimization technique: (1) mutual information combined with the downhill simplex method of optimization. (2) the derivative of mutual information combined with Quasi-Newton method. (3) mutual i information combined with hybrid genetic algorithm (large-space random search) to avoid local maximum during the optimization. These automatic registration systems were evaluated with a variety of images, dimensions and voxel resolutions. Experiments demonstrate that registration system combined with mutual information and hybrid genetic algorithm can provide robust and accurate alignments to obtain a composite activation map for functional MRI analysis. In addition, deformable models (elastic and viscous fluid) were applied to describe the physical behavior of the soft tissues (female breast cancer images). These registration methods model the movement of image as an elastic or viscous fluid object with material attributes corresponding to the constitution of specific tissues. In these two models the physical behavior of deformable object is governed by Navier linear elastic equation or Navier-Stokes equation. The gradient of image intensity was selected as the driving force for the registration process. The equations were solved using finite difference approach with successive over-relaxation (SOR) solver. Soft tissue and synthetic images were used to verify the registration method. All of these advancements enhanced and facilitated the research on functional MR images for rodent animals and female breast cancer detection. ii