Three‐dimensional canine heart model for cardiac elastography

Chen, Hao; Varghese, Tomy

doi:10.1118/1.3496326

Cited by 14 publications

(16 citation statements)

References 51 publications

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“…The incremental strain values directly derived from the 3D FEA cardiac model (denoted as “ideal strain”) can be utilized to evaluate the accuracy of strain estimation based on the corresponding estimated incremental strain along with the variations in the strain over several cardiac cycles (Chen and Varghese 2009; Chen and Varghese 2010; Ma and Varghese 2012b; Ma and Varghese 2012a). Plots of the strain probability distribution at different frame rates are shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…A 3D canine cardiac simulation model previously developed in our laboratory was utilized for this study (Chen and Varghese 2010). This model is based on a 3D finite element analysis (FEA) model of a canine heart developed by the Cardiac Mechanics Research Group at University of California San Diego (UCSD).…”

Section: Methodsmentioning

confidence: 99%

“…The joint pdf was first demonstrated for an inclusion phantom by Varghese and Ophir (Varghese and Ophir 1998) (see Figure 12 in Varghese and Ophir (1998)). The impact of the final processing 2D cross-correlation kernel dimensions (denoted as “final kernel dimensions” for the rest of the paper) of the multi-level algorithm on the joint pdf obtained will be evaluated on a three-dimensional (3D) finite element based cardiac simulation data set (Chen and Varghese 2010) and echo signal data acquired on 5 normal healthy volunteers. In addition, we also compare axial strain estimation results on short axis echocardiographic views estimated using RF and envelope echo signals.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of 2-D Ultrasound Cardiac Strain Imaging Using Joint Probability Density Functions

Varghese

2014

Ultrasound in Medicine & Biology

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Ultrasound frame rates play a key role for accurate cardiac deformation tracking. Insufficient frame rates lead to an increase in signal decorrelation artifacts; resulting in erroneous displacement and strain estimation. Joint probability density distributions generated from estimated axial strain and its associated signal-to-noise ratio provide a useful approach to assess the minimum frame rate requirements. Previous reports have demonstrated that bimodal distributions in the joint probability density indicate inaccurate strain estimation over a cardiac cycle. In this study, we utilize similar analysis to evaluate a two-dimensional multi-level displacement tracking and strain estimation algorithm for cardiac strain imaging. The impact of different frame rates, final kernel dimensions, and a comparison of radiofrequency and envelope based processing are evaluated using echo signals derived from a three-dimensional finite element cardiac model and 5 healthy volunteers. Cardiac simulation model analysis demonstrate that the minimum frame rates required to obtain accurate joint probability distributions for the signal to noise ratio and strain, for a final kernel dimension of 1 λ by 3 A-lines, was around 42 Hz for radiofrequency signals. On the other hand, even a frame rate of 250Hz with envelope signals did not replicate the ideal joint probability distribution. For the volunteer study, clinical data was acquired only at a 34 Hz frame rate which appears to be sufficient for radiofrequency analysis. We also show that an increase in the final kernel dimensions significantly impact the strain probability distribution and joint probability density function generated; with a smaller impact on the variation in the accumulated mean strain estimated over a cardiac cycle. Our results demonstrate that radiofrequency frame rates currently achievable on clinical cardiac ultrasound systems are sufficient for accurate analysis of the strain probability distribution, when a multi-level two-dimensional algorithm and kernel dimensions on the order of 1 λ by 3 A-lines or smaller are utilized.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Analysis of 2-D Ultrasound Cardiac Strain Imaging Using Joint Probability Density Functions

Varghese

2014

Ultrasound in Medicine & Biology

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“…A 3D canine heart model 29,30 for cardiac elastography 8 that was previously adapted in our laboratory was used to simulate cardiac deformation and subsequent tracking of the displacement field. This FEA based canine heart model provides two cardiac cycles of tissue deformation information.…”

Section: Iic Fea Cardiac Simulationsmentioning

confidence: 99%

Lagrangian displacement tracking using a polar grid between endocardial and epicardial contours for cardiac strain imaging

Varghese

2012

Medical Physics

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Purpose: Accurate cardiac deformation analysis for cardiac displacement and strain imaging over time requires Lagrangian description of deformation of myocardial tissue structures. Failure to couple the estimated displacement and strain information with the correct myocardial tissue structures will lead to erroneous result in the displacement and strain distribution over time. Methods: Lagrangian based tracking in this paper divides the tissue structure into a fixed number of pixels whose deformation is tracked over the cardiac cycle. An algorithm that utilizes a polargrid generated between the estimated endocardial and epicardial contours for cardiac short axis images is proposed to ensure Lagrangian description of the pixels. Displacement estimates from consecutive radiofrequency frames were then mapped onto the polar grid to obtain a distribution of the actual displacement that is mapped to the polar grid over time. Results: A finite element based canine heart model coupled with an ultrasound simulation program was used to verify this approach. Segmental analysis of the accumulated displacement and strain over a cardiac cycle demonstrate excellent agreement between the ideal result obtained directly from the finite element model and our Lagrangian approach to strain estimation. Traditional Eulerian based estimation results, on the other hand, show significant deviation from the ideal result. An in vivo comparison of the displacement and strain estimated using parasternal short axis views is also presented. Conclusions: Lagrangian displacement tracking using a polar grid provides accurate tracking of myocardial deformation demonstrated using both finite element and in vivo radiofrequency data acquired on a volunteer. In addition to the cardiac application, this approach can also be utilized for transverse scans of arteries, where a polar grid can be generated between the contours delineating the outer and inner wall of the vessels from the blood flowing though the vessel.

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“…16 Furthermore, various efforts were made on providing gold standards for echocardiography strain analysis. [17][18][19] In order to simulate more realistic ventricular geometries and motion, Duan et al integrated an electromechanical model in the simulations. 20 Similar to this idea, a biomechanical model based simulation method was used to evaluate a sparse demons registration for calculating 3D cardiac motion and strain.…”

Section: Introductionmentioning

confidence: 99%

Simulating cardiac ultrasound image based on MR diffusion tensor imaging

et al. 2015

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Purpose: Cardiac ultrasound simulation can have important applications in the design of ultrasound systems, understanding the interaction effect between ultrasound and tissue and setting the ground truth for validating quantification methods. Current ultrasound simulation methods fail to simulate the myocardial intensity anisotropies. New simulation methods are needed in order to simulate realistic ultrasound images of the heart. Methods: The proposed cardiac ultrasound image simulation method is based on diffusion tensor imaging (DTI) data of the heart. The method utilizes both the cardiac geometry and the fiber orientation information to simulate the anisotropic intensities in B-mode ultrasound images. Before the simulation procedure, the geometry and fiber orientations of the heart are obtained from high-resolution structural MRI and DTI data, respectively. The simulation includes two important steps. First, the backscatter coefficients of the point scatterers inside the myocardium are processed according to the fiber orientations using an anisotropic model. Second, the cardiac ultrasound images are simulated with anisotropic myocardial intensities. The proposed method was also compared with two other nonanisotropic intensity methods using 50 B-mode ultrasound image volumes of five different rat hearts. The simulated images were also compared with the ultrasound images of a diseased rat heart in vivo. A new segmental evaluation method is proposed to validate the simulation results. The average relative errors (AREs) of five parameters, i.e., mean intensity, Rayleigh distribution parameter σ, and first, second, and third quartiles, were utilized as the evaluation metrics. The simulated images were quantitatively compared with real ultrasound images in both ex vivo and in vivo experiments. Results: The proposed ultrasound image simulation method can realistically simulate cardiac ultrasound images of the heart using high-resolution MR-DTI data. The AREs of their proposed method are 19% for the mean intensity, 17.7% for the scale parameter of Rayleigh distribution, 36.8% for the first quartile of the image intensities, 25.2% for the second quartile, and 19.9% for the third quartile. In contrast, the errors of the other two methods are generally five times more than those of their proposed method. Conclusions: The proposed simulation method uses MR-DTI data and realistically generates cardiac ultrasound images with anisotropic intensities inside the myocardium. The ultrasound simulation method could provide a tool for many potential research and clinical applications in cardiac ultrasound imaging. C 2015 American Association of Physicists in Medicine.

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Three‐dimensional canine heart model for cardiac elastography

Cited by 14 publications

References 51 publications

Analysis of 2-D Ultrasound Cardiac Strain Imaging Using Joint Probability Density Functions

Analysis of 2-D Ultrasound Cardiac Strain Imaging Using Joint Probability Density Functions

Lagrangian displacement tracking using a polar grid between endocardial and epicardial contours for cardiac strain imaging

Simulating cardiac ultrasound image based on MR diffusion tensor imaging

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