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Image-Based Motion Estimation for Plaque Visualization in Coronary Computed Tomography Angiography

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X Zhang

X Zhang1*, A Sisniega2 , W Zbijewski3 , F Contijoch4 , E McVeigh5 , J Stayman6 , (1),(2),(3),(6) Johns Hopkins University, Balitmore, MD, (4),(5) University of California, San Diego, San Diego, CA


TH-CD-206-12 (Thursday, August 4, 2016) 10:00 AM - 12:00 PM Room: 206

Purpose: Visualization and quantification of coronary artery calcification and atherosclerotic plaque benefits from coronary artery motion (CAM) artifact elimination. This work applies a rigid linear motion model to a Volume of Interest (VoI) for estimating motion estimation and compensation of image degradation in Coronary Computed Tomography Angiography (CCTA).

Methods: In both simulation and testbench experiments, translational CAM was generated by displacement of the imaging object (i.e. simulated coronary artery and explanted human heart) by ~8 mm, approximating the motion of a main coronary branch. Rotation was assumed to be negligible. A motion degraded region containing a calcification was selected as the VoI. Local residual motion was assumed to be rigid and linear over the acquisition window, simulating motion observed during diastasis. The (negative) magnitude of the image gradient of the reconstructed VoI was chosen as the motion estimation objective and was minimized with Covariance Matrix Adaptation Evolution Strategy (CMA-ES).

Results: Reconstruction incorporated the estimated CAM yielded signification recovery of fine calcification structures as well as reduced motion artifacts within the selected local region. The compensated reconstruction was further evaluated using two image similarity metrics, the structural similarity index (SSIM) and Root Mean Square Error (RMSE). At the calcification site, the compensated data achieved a 3% increase in SSIM and a 91.2% decrease in RMSE in comparison with the uncompensated reconstruction.

Conclusion: Results demonstrate the feasibility of our image-based motion estimation method exploiting a local rigid linear model for CAM compensation. The method shows promising preliminary results for the application of such estimation in CCTA. Further work will involve motion estimation of complex motion corrupted patient data acquired from clinical CT scanner.

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