  Images from Positron Emission Tomography (PET) deliver functional data such
as perfusion and metabolism. On the other hand, images from Magnetic Resonance
Imaging (MRI) provide information describing anatomical structures. Fusing the
complementary information from the two modalities is helpful in oncology. In
this project, we implemented a complete tool allowing semi-automatic MRI-PET
registration for small animal imaging in the preclinical studies. A two-stage
hierarchical registration approach is proposed. First, a global affine
registration is applied. For robust and fast registration, principal component
analysis (PCA) is used to compute the initial parameters for the global affine
registration. Since, only the low intensities in the PET volume reveal the
anatomic information on the MRI scan, we proposed a non-uniform intensity
transformation to the PET volume to enhance the contrast of the low intensity.
This helps to improve the computation of the centroid and principal axis by
increasing the contribution of the low intensities. Then, the globally
registered image is given as input to the second stage which is a local
deformable registration (B-spline registration). Mutual information is used as
metric function for the optimization. A multi-resolution approach is used in
both stages. The registration algorithm is supported by graphical user
interface (GUI) and visualization methods so that the user can interact easily
with the process. The performance of the registration algorithm is validated by
two medical experts on seven different datasets on abdominal and brain areas
including noisy and difficult image volumes.
