Meeting Banner
Abstract #2630

A knowledge-based linear registration for brain MRI morphology

Xinyuan Zhang1,2, Yanqiu Feng1, Qianjin Feng1, and Susumu Mori2,3

1Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China, 2Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, 3F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States

Linear registration is an essential first step for image registration. However, linear registration often fails when the brain shapes, locations, orientations of the target and template images are severely different. To solve this problem, we proposed a knowledge-based approach, in which a large number of MR images were prepared as intermediate images, which were semi-automatically registered to the template a priori to ensure accurate registration. A new target image was first registered to all intermediate images and best intermediate image was selected based on a goodness-of-fit metric. The final transformation was then calculated by combining the pre-determined intermediate-to-target transformation.

This abstract and the presentation materials are available to members only; a login is required.

Join Here