Cardiac function estimation from MRI using a heart model and data assimilation: Advances and difficulties

Sermesant, Maxime; Moireau, Philippe; Cámara, Óscar; Sainte-Marie, Jacques; Andriantsimiavona, Rado; Cimrman, Robert; Hill, Derek L. G.; Chapelle, Dominique; Razavi, Reza

doi:10.1016/j.media.2006.04.002

Cited by 137 publications

(84 citation statements)

References 58 publications

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“…Some of the works on LV segmentation also show results for RV segmentation. These include image-based approaches like thresholding [28][29][30], pixel classification approaches [31,32], deformable models [33][34][35][36], active appearance models [27,37], and atlas based methods [19,23,38].…”

Section: Introductionmentioning

confidence: 99%

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Mahapatra

2013

J Digit Imaging

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In this paper, we propose a novel method for segmentation of the left ventricle, right ventricle, and myocardium from cine cardiac magnetic resonance images of the STACOM database. Our method incorporates prior shape information in a graph cut framework to achieve segmentation. Poor edge information and large within-patient shape variation of the different parts necessitates the inclusion of prior shape information. But large interpatient shape variability makes it difficult to have a generalized shape model. Therefore, for every dataset the shape prior is chosen as a single image clearly showing the different parts. Prior shape information is obtained from a combination of distance functions and orientation angle histograms of each pixel relative to the prior shape. To account for shape changes, pixels near the boundary are allowed to change their labels by appropriate formulation of the penalty and smoothness costs. Our method consists of two stages. In the first stage, segmentation is performed using only intensity information which is the starting point for the second stage combining intensity and shape information to get the final segmentation. Experimental results on different subsets of 30 real patient datasets show higher segmentation accuracy in using shape information and our method's superior performance over other competing methods.

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Section: Introductionmentioning

confidence: 99%

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Mahapatra

2013

J Digit Imaging

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“…To estimate the contraction parameters, the optimization problems were solved using gradient-based algorithms on synthetic data in Sermesant et al (2006b); Sundar et al (2009), and on clinical data in Delingette et al (2012). In Delingette et al (2012), the gradient-based quasi-Newton L-BFGS-B algorithm was used.…”

Section: Derivative-free Optimizationmentioning

confidence: 99%

Velocity-based cardiac contractility personalization from images using derivative-free optimization

Wong

Sermesant

Rhode

et al. 2015

Journal of the Mechanical Behavior of Biomedical Materials

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Model personalization is a key aspect for biophysical models to impact clinical practice, and cardiac contractility personalization from medical images is a major step in this direction. Existing gradient-based optimization approaches show promising results of identifying the maximum contractility from images, but the contraction and relaxation rates are not accounted for. A main reason is the limited choices of objective functions when their gradients are required. For complicated cardiac models, analytical evaluations of gradients are very difficult if not impossible, and finite difference approximations are computationally expensive and may introduce numerical difficulties. By removing such limitations with derivative-free optimization, we found that a velocity-based objective function can properly identify regional maximum contraction stresses, contraction rates, and relaxation rates simultaneously with intact model complexity. Experiments on synthetic data show that the parameters are better identified using the velocity-based objective function than its position-based counterpart, and the proposed framework is insensitive to initial parameters with the adopted derivative-free optimization algorithm. Experiments on clinical data show that the framework can provide personalized contractility parameters which are consistent with the underlying physiologies of the patients and healthy volunteers.

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“…Another difficulty of employing a physical model is that the modeling of the complex heart motion is computationally expensive and hardly possible in real-time. The first reason for that is the usage of complex geometric models, such as models for a preoperative planning or a therapy testing [19,20]. Another reason, the numerical solution of the partial differential equations (PDE) with traditional methods, such as the finite-element method (FEM), generates system-related response delays.…”

Section: Related Workmentioning

confidence: 99%

Efficient physics-based tracking of heart surface motion for beating heart surgery robotic systems

2010

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Purpose Tracking of beating heart motion in a robotic surgery system is required for complex cardiovascular interventions. Methods A heart surface motion tracking method is developed, including a stochastic physics-based heart surface model and an efficient reconstruction algorithm. The algorithm uses the constraints provided by the model that exploits the physical characteristics of the heart. The main advantage of the model is that it is more realistic than most standard heart models. Additionally, no explicit matching between the measurements and the model is required. The application of meshless methods significantly reduces the complexity of physics-based tracking. Results Based on the stochastic physical model of the heart surface, this approach considers the motion of the intervention area and is robust to occlusions and reflections. The tracking algorithm is evaluated in simulations and experiments on an artificial heart. Providing higher accuracy than the standard model-based methods, it copes successfully with occlusions and provides high performance when all measurements are not available. Conclusions Combining the physical and stochastic description of the heart surface motion ensures physically correct and accurate prediction. Automatic initialization of the physics-based cardiac motion tracking enables system evaluation in a clinical environment.

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Cardiac function estimation from MRI using a heart model and data assimilation: Advances and difficulties

Cited by 137 publications

References 58 publications

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Cardiac Image Segmentation from Cine Cardiac MRI Using Graph Cuts and Shape Priors

Velocity-based cardiac contractility personalization from images using derivative-free optimization

Efficient physics-based tracking of heart surface motion for beating heart surgery robotic systems

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