Premium
Temporally diffeomorphic cardiac motion estimation from three‐dimensional echocardiography by minimization of intensity consistency error
Author(s) -
Zhang Zhijun,
Ashraf Muhammad,
Sahn David J.,
Song Xubo
Publication year - 2014
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4867864
Subject(s) - artificial intelligence , computer vision , motion estimation , computer science , sonomicrometry , motion field , gradient descent , diffeomorphism , image registration , ground truth , imaging phantom , mathematics , algorithm , image (mathematics) , physics , medicine , mathematical analysis , ischemia , artificial neural network , optics , cardiology
Purpose: Quantitative analysis of cardiac motion is important for evaluation of heart function. Three dimensional (3D) echocardiography is among the most frequently used imaging modalities for motion estimation because it is convenient, real‐time, low‐cost, and nonionizing. However, motion estimation from 3D echocardiographic sequences is still a challenging problem due to low image quality and image corruption by noise and artifacts.Methods: The authors have developed a temporally diffeomorphic motion estimation approach in which the velocity field instead of the displacement field was optimized. The optimal velocity field optimizes a novel similarity function, which we call the intensity consistency error, defined as multiple consecutive frames evolving to each time point. The optimization problem is solved by using the steepest descent method.Results: Experiments with simulated datasets, images of an ex vivo rabbit phantom, images of in vivo open‐chest pig hearts, and healthy human images were used to validate the authors’ method. Simulated and real cardiac sequences tests showed that results in the authors’ method are more accurate than other competing temporal diffeomorphic methods. Tests with sonomicrometry showed that the tracked crystal positions have good agreement with ground truth and the authors’ method has higher accuracy than the temporal diffeomorphic free‐form deformation (TDFFD) method. Validation with an open‐access human cardiac dataset showed that the authors’ method has smaller feature tracking errors than both TDFFD and frame‐to‐frame methods.Conclusions: The authors proposed a diffeomorphic motion estimation method with temporal smoothness by constraining the velocity field to have maximum local intensity consistency within multiple consecutive frames. The estimated motion using the authors’ method has good temporal consistency and is more accurate than other temporally diffeomorphic motion estimation methods.