DocumentCode
1958856
Title
Left ventricular 2D flow pattern estimation of the heart by combining speckle tracking with Navier-Stokes based regularization
Author
Gao, Hang ; Kremer, Florence ; Choi, Hon Fai ; Voigt, Jens-Uwe ; Claus, Piet ; Hooge, Jan D.
Author_Institution
Dept. of Cardiovascular Diseases, Catholic Univ. of Leuven, Leuven, Belgium
fYear
2010
fDate
11-14 Oct. 2010
Firstpage
1068
Lastpage
1071
Abstract
The dynamics of the blood flow inside the left ventricle (LV) has an important effect on cardiac performance. Although ultrasound (US) methodologies based on speckle tracking after low-dose contrast injection have been proposed, the fast motion in combination with out-of-plane flow makes the tracking challenging. The aim of this study was therefore to develop a new estimation method by regularizing the speckle tracking results using the Navier-Stokes equations. This method was tested in a Computational Fluid Dynamics (CFD) based ultrasound simulation environment. A dynamic 3D anatomical model of the LV was combined with a realistic inflow velocity profile to build a CFD flow vector field (Fluent 12.1, ANSYS). The motion of injected bubbles (density = 500/ml) was simulated and used as input to an ultrasound simulator (COLE) to obtain a 2D B-mode image sequence (f = 4.5MHz; 50°opening angle; dynamic focusing; frame rate = 227Hz). On this sequence, block-matching was applied (kernel 0.34mm × 0.028rad; search region 15.4mm × 0.078rad; window overlap 40 ± 90%) using normalized cross-correlation as a similarity measure and spline-interpolation for subsample motion estimation. To regularize the velocity estimates, the difference between the measured and regularized velocity field was added as an external force to a finite difference implementation of the Navier-Stokes equations. The RMSE normalized for the maximal velocity (NRMSE) at each frame was calculated using the CFD velocity field as the ground truth. The NRMSE analysis showed that regularization improved the initial estimates mostly in the lateral direction for inflow velocities below 0.25 m/s (axial: 16.37 ± 2.89% vs. 16.67 ± 2.49%; lateral: 18.69 ± 3.57% vs. 23.27 ± 3.38%). However, for larger inflow velocities, regularization did not refine the estimates (axial: 22.83 ± 3.20% vs. 24.00 ± 4.37%; lateral: 17.58 ± 4.73% vs. 18.6- - 6 ± 4.32%), since the initial estimates were too noisy to be adequately regularized. In future work, the regularized velocities from low inflow velocity frames will be used as prior to improve the tracking results of the higher velocity frames.
Keywords
Navier-Stokes equations; biomedical ultrasonics; brain; cardiology; computational fluid dynamics; haemodynamics; image matching; image sequences; interpolation; mean square error methods; medical image processing; motion estimation; 2D B-mode image sequence; CFD flow vector field; CFD velocity field; NRMSE analysis; Navier-Stokes based regularization; Navier-Stokes equations; RMSE; block-matching; blood flow; cardiac performance; computational fluid dynamics based ultrasound simulation environment; dynamic 3D anatomical model; dynamic focusing; estimation method; finite difference implementation; heart; injected bubbles; left ventricular 2D flow pattern estimation; low-dose contrast injection; realistic inflow velocity profile; speckle tracking; spline-interpolation; subsample motion estimation; ultrasound methodology; Blood; Computational fluid dynamics; Imaging; Speckle; Three dimensional displays; Tracking; Ultrasonic imaging;
fLanguage
English
Publisher
ieee
Conference_Titel
Ultrasonics Symposium (IUS), 2010 IEEE
Conference_Location
San Diego, CA
ISSN
1948-5719
Print_ISBN
978-1-4577-0382-9
Type
conf
DOI
10.1109/ULTSYM.2010.5935773
Filename
5935773
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