Meeting Banner
Abstract #0396

Image Registration of Perfusion MRI Using Deep Learning Networks

Zongpai Zhang1, Huiyuan Yang1, Yanchen Guo1, Lijun Yin1, David C. Alsop2, and Weiying Dai1
1State University of New York at Binghamton, Binghamton, NY, United States, 2Beth Israel Deaconess Medical Center & Harvard Medical School, Boston, MA, United States

Convolutional neural network (CNN) has demonstrated its accuracy and speed in image registration of structural MRI. We designed an affine registration network (ARN), based on CNN, to explore its feasibility on image registration of perfusion fMRI. The six affine parameters were learned from the ARN using both simulated and real perfusion fMRI data and the transformed images were generated by applying the transformation matrix derived from the affine parameters. The results demonstrated that our ARN markedly outperforms the iteration-based SPM algorithm both in simulated and real data. The current ARN is being extended for deformable fMRI image registration.

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

Join Here