Title :
A general technique for interstudy registration of multifunction and multimodality images
Author :
Lin, Kang-Ping ; Huang, Sung-Cheng ; Baxter, Lewis R. ; Phelps, Michael E.
Author_Institution :
Dept. of Electr. Eng., Chung Yuan Univ., Taiwan
Abstract :
A technique that can register anatomic/structural brain images (e.g., MRI) with various functional images (e.g., PET-FDG and PET-FDOPA) of the same subject has been developed. The procedure of this technique includes the following steps: (1) segmentation of MRI brain images into gray matter (GM), white matter (WM), cerebral spinal fluid (CSF), and, muscle (MS) components, (2) assignment of appropriate radio-tracer concentrations to various components depending on the kind of functional image that is being registered, (3) generation of simulated functional images to have a spatial resolution that is comparable to that of the measured ones, (4) alignment of the measured functional images to the simulated ones that are based on MRI images. A self-organization clustering method is used to segment the MRI images. The image alignment is based on the criterion of least squares of the pixel-by-pixel differences between the two sets of images that are being matched and on the Powell´s algorithm for minimization. The technique was applied successfully for registering the MRI, PET-FDG, and PET-FDOPA images. This technique offers a general solution to the registration of structural images to functional images and to the registration of different functional images of markedly different distributions.<>
Keywords :
biomedical NMR; brain; image registration; image segmentation; medical image processing; muscle; positron emission tomography; MRI; PET-FDG; PET-FDOPA; Powell´s minimization algorithm; anatomic brain images; cerebral spinal fluid; functional images; gray matter; image alignment; image spatial resolution; interstudy registration; medical diagnostic imaging; multifunction images; multimodality images; pixel-by-pixel differences; radiotracer concentration; self-organization clustering method; structural brain images; white matter; Brain modeling; Clustering algorithms; Clustering methods; Image segmentation; Least squares methods; Magnetic resonance imaging; Minimization methods; Muscles; Pixel; Spatial resolution;
Journal_Title :
Nuclear Science, IEEE Transactions on