A Convolutional Neural Network-based Deformable Image Registration Method for Cardiac Motion Estimation from Cine Cardiac MRI Images

Roshan Reddy Upendra1, Brian Jamison Wentz2, Suzanne M. Shontz2, Cristian Linte1
1Rochester Institute of Technology, 2University of Kansas


Introduction: Myocardial motion estimation is critical for studying regional heart function and entails finding an optical flow representation between consecutive 3D frames of a 4D cine cardiac magnetic resonance imaging (MRI) dataset. Here we describe an unsupervised deep learning framework for deformable registration of 3D CMRI images.

Methods: Our method leverages the VoxelMorph framework, which uses a 3D image pair as input to a convolutional neural network (CNN). The CNN outputs a deformation field, which, along with one of the input 3D images, serves as input to a spatial transformer network that yields a warped image. Before registration, the input 3D images are corrected for slice misalignment by regressing the centers of the left ventricle (LV) blood-pool from all image slices, from which the myocardium (MC) and right ventricle (RV) were also segmented using a U-Net model. The manual segmentations of LV, MC, and RV at end-diastole and end-systole are used to train the U-Net model to segment the other frames in the dataset.

Results: We conducted experiments using 100 4D CMRI data (75: training, 10: validation and 15: testing). We used the registration deformation field to warp the manually segmented LV, MC and RV labels from end-systole to end-diastole and estimated their similarity. We achieved a mean Dice score of 91.01% (LV), 73.63% (MC), and 80.72% (RV) compared to 83.52%, 61.51% and 71.88%, respectively, before registration. The LV, MC and RV labels from the remaining cardiac phases were also warped to end-diastole and yielded a mean Dice score of 93.29% (LV), 79.19% (MC) and 86.32% (RV) compared to 88.78%, 73.28% and 81.14%, respectively, before registration.

Conclusion: We described a CNN-based deformable registration to estimate heart motion from cine cardiac MRI. We will use this technique to build dynamic patient-specific myocardial models with associated fiber architecture for biomechanical cardiac simulations.