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What is new direction of research in finite element modeling of heart? 


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Finite element modeling of the heart has seen several new directions of research. One such direction is the development of automated volumetric meshing techniques for patient-specific heart geometry. These techniques use deep learning methods to generate high-quality volumetric meshes with precise spatial accuracy and element quality . Another direction is the use of morphing algorithms to match anatomies acquired from medical imaging scans. These algorithms involve iterative finite element simulations to recreate subject-specific models with the same mesh connectivity, allowing for spatial comparison and analysis of pathologic progress . Additionally, there has been a focus on incorporating more accurate cellular-level mechanisms into finite element models to better represent the global performance and regional mechanical behaviors of the heart during the entire cardiac cycle . Finally, hyperelastic warping has been explored as a method to non-rigidly fit finite element models to new anatomies, allowing for the creation of diverse heart models for imaging research .

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The paper discusses the use of Hyperelastic Warping to non-rigidly fit the Living Heart Model (LHM) to new anatomies based on 4D CT data, allowing for efficient revision of the LHM geometry.
The paper proposes a novel cardiac mesh morphing algorithm for finite element modeling of heart failure with preserved ejection fraction (HFpEF).
The new direction of research in finite element modeling of the heart is the development of a novel model of cellular-level systolic contraction that includes both length- and velocity-dependence of force generation.
The provided paper discusses a novel cardiac mesh morphing method for finite element modeling of heart failure with preserved ejection fraction (HFpEF). It does not mention any new direction of research in finite element modeling of the heart.
The new direction of research in finite element modeling of the heart is the use of DeepCarve, a deformation-based deep learning method, for automated generation of patient-specific volumetric meshes with high spatial accuracy and element quality.

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