Alfredo De Goyeneche1, Shuyu Tang1, Nii Okai Addy1, Bob Hu1, William Overall1, and Juan Santos1
1HeartVista, Inc., Los Altos, CA, United States
1HeartVista, Inc., Los Altos, CA, United States
We developed a deep-learning-based approach for motion correction in quantitative cardiac MRI, including perfusion, T1 mapping, and T2 mapping. The proposed approach was faster and more accurate than a popular traditional registration method.
Representative image registration results of our proposed method. Our proposed methods are robust to the inconsistent contrast between the moving image and the reference image, whereas such inconsistency tends to cause distortions in ANTs results.
(a) Proposed framework for motion correction in cardiac MRI. The image registration neural network inputs are segmentation network outputs of the reference image and the moving image. The output of the registration network is the deformation field that is applied to the moving image to yield the registered image. (b) Registration neural network architecture.
