Dynamic Cardiac Information From Optical Flow Using Four Dimensional Ultrasound

Duan, Qi; Angelini, Elsa D.; Herz, Susan L.; Ingrassia, Christopher M.; Gerard, Olivier; Costa, Kevin D.; Holmes, Jeffrey W.; Homma, Shunichi; Laine, Andrew F.

doi:10.1109/iembs.2005.1615458

Cited by 18 publications

(11 citation statements)

References 13 publications

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“…A sample result from one data set is shown in Figure 2. The time course of RMSE for this data set is graphed in Figure 2a, which shows small absolute errors (below 0.07 ~ 7%) despite error accumulation as previously observed in (Duan, Angelini 2005). Time courses of the LV volumes estimated from manual tracing (solid line) and tracking (dashed line) are shown in Figure 2b.…”

Section: Resultssupporting

confidence: 64%

See 1 more Smart Citation

Region-Based Endocardium Tracking on Real-Time Three-Dimensional Ultrasound

Duan

Angelini

Herz

et al. 2009

Ultrasound in Medicine & Biology

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Abstract-Matrix-phased array transducers for real-time 3-D ultrasound enable fast, noninvasive visualization of cardiac ventricles. Typically, 3-D ultrasound images are semiautomatically segmented to extract the left ventricular endocardial surface at end-diastole and end-systole. Automatic segmentation and propagation of this surface throughout the entire cardiac cycle is a challenging and cumbersome task. If the position of the endocardial surface is provided at one or two time frames during the cardiac cycle, automated tracking of the surface over the remaining time frames could reduce the workload of cardiologists and optimize analysis of 3-D ultrasound data. In this paper, we applied a region-based tracking algorithm to track the endocardial surface between two reference frames that were manually segmented. To evaluate the tracking of the endocardium, the method was applied to 40 open-chest dog datasets with 484 frames in total. Ventricular geometry and volumes derived from region-based endocardial surfaces and manual tracing were quantitatively compared, showing strong correlation between the two approaches. Statistical analysis showed that the errors from tracking were within the range of interobserver variability of manual tracing. Moreover, our algorithm performed well on ischemia datasets, suggesting that the method is robust-to-abnormal wall motion. In conclusion, the proposed optical flow-based surface tracking method is very efficient and accurate, providing dynamic "interpolation" of segmented endocardial surfaces.

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Section: Resultssupporting

confidence: 64%

“…The proposed method was not being applied as a segmentation tool, but used as a surface tracking tool for a given segmentation method. No additional reinitialization or forward-and-backward tracking as proposed in (Duan, Angelini 2005) was needed, due to high image quality provided by the open-chest acquisition setup.…”

Section: Methodsmentioning

confidence: 99%

Region-Based Endocardium Tracking on Real-Time Three-Dimensional Ultrasound

Duan

Angelini

Herz

et al. 2009

Ultrasound in Medicine & Biology

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“…Although initial work was primarily concerned with imaging the axial (along the ultrasound beam) strain field, several techniques have been developed to compute two- and three-dimensional strains (Baraldi et al, 1996; Crosby et al, 2009; Duan et al, 2005; Kaluzynski et al, 2001; Konofagou and Ophir, 1998; Papademetris et al, 2001; Suffoletto et al, 2006). Increasing the dimensionality of the strain measurement is of particular importance in cardiac imaging since heart motion is a complex, three-dimensional phenomenon.…”

Section: Introductionmentioning

confidence: 99%

Stochastic precision analysis of 2D cardiac strain estimationin vivo

2014

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Ultrasonic strain imaging has been applied to echocardiography and carries great potential to be used as a tool in the clinical setting. Two-dimensional (2-D) strain estimation may be useful when studying the heart due to the complex, three-dimensional deformation of the cardiac tissue. Increasing the framerate used for motion estimation, i.e. motion estimation rate (MER), has been shown to improve the precision of the strain estimation, although maintaining the spatial resolution necessary to view the entire heart structure in a single heartbeat remains challenging at high MERs. Two previously developed methods, the temporally unequispaced acquisition sequence (TUAS) and the diverging beam sequence (DBS), have been used in the past to successfully estimate in vivo axial strain at high MERs without compromising spatial resolution. In this study, a stochastic assessment of 2-D strain estimation precision is performed in vivo for both sequences at varying MERs (65, 272, 544, 815 Hz for TUAS; 250, 500, 1000, 2000 Hz for DBS). 2-D incremental strains were estimated in five healthy volunteers using a normalized cross-correlation function and a least-squares strain estimator. Both sequences were shown capable of estimating 2-D incremental strains in vivo. The conditional expected value of the elastographic signal-to-noise ratio (E(SNRe|ε)) was used to compare strain estimation precision of both sequences at multiple MERs over a wide range of clinical strain values. The results here indicate that axial strain estimation precision is much more dependent on MER than lateral strain estimation, while lateral estimation is more affected by strain magnitude. MER should be increased at least above 544 Hz to avoid suboptimal axial strain estimation. Radial and circumferential strain estimations were influenced by the axial and lateral strain in different ways. Furthermore, the TUAS and DBS were found to be of comparable precision at similar MERs.

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“…Meunier [3] analyzed the performance of 3D speckle tracking using simulated ultrasound images. Our group [6, 7] has shown that using a correlation-based optical flow method, myocardial motion fields can be estimated, from which directional displacements and strains can be reliably computed. In order to further evaluate the potential of this method, in this study, strain values estimated by our tracking method on ischemic regions were directly compared to “gold standard” strain measures acquired via sonomicrometry at the same locations, and recorded simultaneously during the ultrasound acquisition.…”

Section: Introductionmentioning

confidence: 99%

Quantitative validation of optical flow based myocardial strain measures using sonomicrometry

Duan

Parker

Lorsakul

et al. 2009

2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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Dynamic cardiac metrics, including myocardial strains and displacements, provide a quantitative approach to evaluate cardiac function. However, in current clinical diagnosis, largely 2D strain measures are used despite that cardiac motions are complex 3D volumes over time. Recent advances in 4D ultrasound enable the capability to capture such complex motion in a single image data set. In our previous work, a 4D optical flow based motion tracking algorithm was developed to extract full 4D dynamic cardiac metrics from such 4D ultrasound data. In order to quantitatively evaluate this tracking method, in-vivo coronary artery occlusion experiments at various locations were performed on three canine hearts. Each dog was screened with 4D ultrasound and sonomicrometry data was acquired during each occlusion study. The 4D ultrasound data from these experiments was then analyzed with the tracking method and estimated principal strain measures were directly compared to those recorded by sonomicrometry. Strong agreement was observed independently for the three canine hearts. This is the first validation study of optical flow based strain estimation for 4D ultrasound with a direct comparison with sonomicrometry using in-vivo data.

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Dynamic Cardiac Information From Optical Flow Using Four Dimensional Ultrasound

Cited by 18 publications

References 13 publications

Region-Based Endocardium Tracking on Real-Time Three-Dimensional Ultrasound

Region-Based Endocardium Tracking on Real-Time Three-Dimensional Ultrasound

Stochastic precision analysis of 2D cardiac strain estimationin vivo

Quantitative validation of optical flow based myocardial strain measures using sonomicrometry

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