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A geometrical approach for automatic shape restoration of the left ventricle.

Tan ML, Su Y, Lim CW, Selvaraj SK, Zhong L, Tan RS - PLoS ONE (2013)

Bottom Line: The goal of the optimization is to achieve a smooth epicardial shape by iterative in-plane and through-plane translation of vertices in the LV model.In the 20 in vivo patient-specific models, the results show that our method is able to restore the shape of LV models without altering the general shape of the model.The magnitudes of in-plane translations are also consistent with existing registration techniques and experimental findings.

View Article: PubMed Central - PubMed

Affiliation: Geometrical Modelling, Institute of High Performance Computing, ASTAR, Singapore, Singapore. tanml@ihpc.a-star.edu.sg

ABSTRACT
This paper describes an automatic algorithm that uses a geometry-driven optimization approach to restore the shape of three-dimensional (3D) left ventricular (LV) models created from magnetic resonance imaging (MRI) data. The basic premise is to restore the LV shape such that the LV epicardial surface is smooth after the restoration and that the general shape characteristic of the LV is not altered. The Maximum Principle Curvature (k1) and the Minimum Principle Curvature (k2) of the LV epicardial surface are used to construct a shape-based optimization objective function to restore the shape of a motion-affected LV via a dual-resolution semi-rigid deformation process and a free-form geometric deformation process. A limited memory quasi-Newton algorithm, L-BFGS-B, is then used to solve the optimization problem. The goal of the optimization is to achieve a smooth epicardial shape by iterative in-plane and through-plane translation of vertices in the LV model. We tested our algorithm on 30 sets of LV models with simulated motion artifact generated from a very smooth patient sample, and 20 in vivo patient-specific models which contain significant motion artifacts. In the 30 simulated samples, the Hausdorff distances with respect to the Ground Truth are significantly reduced after restoration, signifying that the algorithm can restore geometrical accuracy of motion-affected LV models. In the 20 in vivo patient-specific models, the results show that our method is able to restore the shape of LV models without altering the general shape of the model. The magnitudes of in-plane translations are also consistent with existing registration techniques and experimental findings.

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Shape restoration using dual resolution semi-rigid deformation.(a) original mesh with motion artifact, (b) intermediate mesh after optimization using n-ring  = 5, and (c) final mesh after optimization using n-ring  = 2.
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pone-0068615-g003: Shape restoration using dual resolution semi-rigid deformation.(a) original mesh with motion artifact, (b) intermediate mesh after optimization using n-ring  = 5, and (c) final mesh after optimization using n-ring  = 2.

Mentions: In the example shown in Figure 3(a), it shows an original mesh with motion artifact, where the -ring selected is 4 due to it having 8 MRI stacks. When -ring  = 4, and are calculated by taking into account points from 4 layers above and below the current SA slice, and 4 points to the right and left of the point of interest. All the slices will shift to minimize the objection function in Equation (5). Next, to further minimize surface concavity over a smaller region of consideration, the intermediate mesh (updated with the previously obtained solution using -ring  = 4) is subjected to a second pass of optimization using a fixed -ring  = 2. This second pass is essential to further minimize the concavity over a localized region. The results from setting -ring value  = 4 and then -ring  = 2 are shown in Figure 3(b), intermediate mesh after optimization using -ring  = 4 and Figure 3(c), final mesh after optimization using -ring  = 2. As observed, the LV smoothness was restored.


A geometrical approach for automatic shape restoration of the left ventricle.

Tan ML, Su Y, Lim CW, Selvaraj SK, Zhong L, Tan RS - PLoS ONE (2013)

Shape restoration using dual resolution semi-rigid deformation.(a) original mesh with motion artifact, (b) intermediate mesh after optimization using n-ring  = 5, and (c) final mesh after optimization using n-ring  = 2.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3716701&req=5

pone-0068615-g003: Shape restoration using dual resolution semi-rigid deformation.(a) original mesh with motion artifact, (b) intermediate mesh after optimization using n-ring  = 5, and (c) final mesh after optimization using n-ring  = 2.
Mentions: In the example shown in Figure 3(a), it shows an original mesh with motion artifact, where the -ring selected is 4 due to it having 8 MRI stacks. When -ring  = 4, and are calculated by taking into account points from 4 layers above and below the current SA slice, and 4 points to the right and left of the point of interest. All the slices will shift to minimize the objection function in Equation (5). Next, to further minimize surface concavity over a smaller region of consideration, the intermediate mesh (updated with the previously obtained solution using -ring  = 4) is subjected to a second pass of optimization using a fixed -ring  = 2. This second pass is essential to further minimize the concavity over a localized region. The results from setting -ring value  = 4 and then -ring  = 2 are shown in Figure 3(b), intermediate mesh after optimization using -ring  = 4 and Figure 3(c), final mesh after optimization using -ring  = 2. As observed, the LV smoothness was restored.

Bottom Line: The goal of the optimization is to achieve a smooth epicardial shape by iterative in-plane and through-plane translation of vertices in the LV model.In the 20 in vivo patient-specific models, the results show that our method is able to restore the shape of LV models without altering the general shape of the model.The magnitudes of in-plane translations are also consistent with existing registration techniques and experimental findings.

View Article: PubMed Central - PubMed

Affiliation: Geometrical Modelling, Institute of High Performance Computing, ASTAR, Singapore, Singapore. tanml@ihpc.a-star.edu.sg

ABSTRACT
This paper describes an automatic algorithm that uses a geometry-driven optimization approach to restore the shape of three-dimensional (3D) left ventricular (LV) models created from magnetic resonance imaging (MRI) data. The basic premise is to restore the LV shape such that the LV epicardial surface is smooth after the restoration and that the general shape characteristic of the LV is not altered. The Maximum Principle Curvature (k1) and the Minimum Principle Curvature (k2) of the LV epicardial surface are used to construct a shape-based optimization objective function to restore the shape of a motion-affected LV via a dual-resolution semi-rigid deformation process and a free-form geometric deformation process. A limited memory quasi-Newton algorithm, L-BFGS-B, is then used to solve the optimization problem. The goal of the optimization is to achieve a smooth epicardial shape by iterative in-plane and through-plane translation of vertices in the LV model. We tested our algorithm on 30 sets of LV models with simulated motion artifact generated from a very smooth patient sample, and 20 in vivo patient-specific models which contain significant motion artifacts. In the 30 simulated samples, the Hausdorff distances with respect to the Ground Truth are significantly reduced after restoration, signifying that the algorithm can restore geometrical accuracy of motion-affected LV models. In the 20 in vivo patient-specific models, the results show that our method is able to restore the shape of LV models without altering the general shape of the model. The magnitudes of in-plane translations are also consistent with existing registration techniques and experimental findings.

Show MeSH