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Title: Motion Extraction of the Right Ventricle from 4D Cardiac Cine MRI Using A Deep Learning-Based Deformable Registration Framework
Cardiac Cine Magnetic Resonance (CMR) Imaging has made a significant paradigm shift in medical imaging technology, thanks to its capability of acquiring high spatial and temporal resolution images of different structures within the heart that can be used for reconstructing patient-specific ventricular computational models. In this work, we describe the development of dynamic patient-specific right ventricle (RV) models associated with normal subjects and abnormal RV patients to be subsequently used to assess RV function based on motion and kinematic analysis. We first constructed static RV models using segmentation masks of cardiac chambers generated from our accurate, memory-efficient deep neural architecture - CondenseUNet - featuring both a learned group structure and a regularized weight-pruner to estimate the motion of the right ventricle. In our study, we use a deep learning-based deformable network that takes 3D input volumes and outputs a motion field which is then used to generate isosurface meshes of the cardiac geometry at all cardiac frames by propagating the end-diastole (ED) isosurface mesh using the reconstructed motion field. The proposed model was trained and tested on the Automated Cardiac Diagnosis Challenge (ACDC) dataset featuring 150 cine cardiac MRI patient datasets. The isosurface meshes generated using the proposed pipeline were more » compared to those obtained using motion propagation via traditional non-rigid registration based on several performance metrics, including Dice score and mean absolute distance (MAD). « less
Authors:
; ; ; ; ; ;
Award ID(s):
1808553 1717894
Publication Date:
NSF-PAR ID:
10357908
Journal Name:
Proc. of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (EMBC 2021)
Page Range or eLocation-ID:
3795 to 3799
Sponsoring Org:
National Science Foundation
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