An atlas-based geometry pipeline for cardiac Hermite model construction and diffusion tensor reorientation

Zhang, Yongjie; Liang, Xinghua; Ma, Jun; Jing, Yiming; Gonzales, Matthew J.; Villongco, Christopher T.; Krishnamurthy, Adarsh; Frank, Lawrence R.; Nigam, Vishal; Stark, Paul; Narayan, Sanjiv M.; McCulloch, Andrew D.

doi:10.1016/j.media.2012.06.005

Cited by 44 publications

(25 citation statements)

References 36 publications

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“…Fernandez and colleagues (2004) constructed cubic Hermite models of more complicated shapes with extraordinary vertices and “hanging” vertices, but did not attempt to preserve smoothness near these points. We previously demonstrated deformable registration of a cubic Hermite four-chamber heart model (Zhang et al, 2012), but as we had not yet defined the local-to-global map in meshes with extraordinary vertices, we could only apply the deformations to the linear hexahedral mesh. In the future, our deformable registration scheme will be used on the higher-quality models created by the methods here, and may also utilize the local-to-global map to deform the cubic Hermite meshes directly.…”

Section: Discussionmentioning

confidence: 99%

A three-dimensional finite element model of human atrial anatomy: New methods for cubic Hermite meshes with extraordinary vertices

Gonzales

Sturgeon

Krishnamurthy

et al. 2013

Medical Image Analysis

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High-order cubic Hermite finite elements have been valuable in modeling cardiac geometry, fiber orientations, biomechanics, and electrophysiology, but their use in solving three-dimensional problems has been limited to ventricular models with simple topologies. Here, we utilized a subdivision surface scheme and derived a generalization of the “local-to-global” derivative mapping scheme of cubic Hermite finite elements to construct bicubic and tricubic Hermite models of the human atria with extraordinary vertices from computed tomography images of a patient with atrial fibrillation. To an accuracy of 0.6 millimeters, we were able to capture the left atrial geometry with only 142 bicubic Hermite finite elements, and the right atrial geometry with only 90. The left and right atrial bicubic Hermite meshes were G1 continuous everywhere except in the one-neighborhood of extraordinary vertices, where the mean dot products of normals at adjacent elements were 0.928 and 0.925. We also constructed two biatrial tricubic Hermite models and defined fiber orientation fields in agreement with diagrammatic data from the literature using only 42 angle parameters. The meshes all have good quality metrics, uniform element sizes, and elements with aspect ratios near unity, and are shared with the public. These new methods will allow for more compact and efficient patient-specific models of human atrial and whole heart physiology.

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Section: Discussionmentioning

confidence: 99%

A three-dimensional finite element model of human atrial anatomy: New methods for cubic Hermite meshes with extraordinary vertices

Gonzales

Sturgeon

Krishnamurthy

et al. 2013

Medical Image Analysis

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“…This mesh is then successively subdivided twice (Fig. 1E, F) using the methods outlined in [12, 13], to estimate the cubic Hermite derivatives at the nodes. Finally, the 3D cubic-Hermite finite element mesh consisting of 28 elements and 66 nodes (1584 degrees of freedom) is constructed using the estimated derivatives (Fig.…”

Section: Methodsmentioning

confidence: 99%

Left Ventricular Diastolic and Systolic Material Property Estimation from Image Data

Krishnamurthy

Villongco

Beck

et al. 2015

Lecture Notes in Computer Science

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Cardiovascular simulations using patient-specific geometries can help researchers understand the mechanical behavior of the heart under different loading or disease conditions. However, to replicate the regional mechanics of the heart accurately, both the nonlinear passive and active material properties must be estimated reliably. In this paper, automated methods were used to determine passive material properties while simultaneously computing the unloaded reference geometry of the ventricles for stress analysis. Two different approaches were used to model systole. In the first, a physiologically-based active contraction model [1] coupled to a hemodynamic three-element Windkessel model of the circulation was used to simulate ventricular ejection. In the second, developed active tension was directly adjusted to match ventricular volumes at end-systole while prescribing the known end-systolic pressure. These methods were tested in four normal dogs using the data provided for the LV mechanics challenge [2]. The resulting end-diastolic and end-systolic geometry from the simulation were compared with measured image data.

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“…A pipeline for creating such templates is possible using image analysis and shape modelling methods (Gonzales et al, 2013; Zhang et al, 2012) (Figure 2). Hexahedral bilinear elements were used to define the surface mesh topology, allowing for extraordinary nodes (i.e.…”

Section: Remodelling In Pre-clinical and Clinical Diseasementioning

confidence: 99%

Cardiac image modelling: Breadth and depth in heart disease

Suinesiaputra

McCulloch

Nash

et al. 2016

Medical Image Analysis

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With the advent of large-scale imaging studies and big health data, and the corresponding growth in analytics, machine learning and computational image analysis methods, there are now exciting opportunities for deepening our understanding of the mechanisms and characteristics of heart disease. Two emerging fields are computational analysis of cardiac remodelling (shape and motion changes due to disease) and computational analysis of physiology and mechanics to estimate biophysical properties from non-invasive imaging. Many large cohort studies now underway around the world have been specifically designed based on non-invasive imaging technologies in order to gain new information about the development of heart disease from asymptomatic to clinical manifestations. These give an unprecedented breadth to the quantification of population variation and disease development. Also, for the individual patient, it is now possible to determine biophysical properties of myocardial tissue in health and disease by interpreting detailed imaging data using computational modelling. For these population and patient-specific computational modelling methods to develop further, we need open benchmarks for algorithm comparison and validation, open sharing of data and algorithms, and demonstration of clinical efficacy in patient management and care. The combination of population and patient-specific modelling will give new insights into the mechanisms of cardiac disease, in particular the development of heart failure, congenital heart disease, myocardial infarction, contractile dysfunction and diastolic dysfunction.

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An atlas-based geometry pipeline for cardiac Hermite model construction and diffusion tensor reorientation

Cited by 44 publications

References 36 publications

A three-dimensional finite element model of human atrial anatomy: New methods for cubic Hermite meshes with extraordinary vertices

A three-dimensional finite element model of human atrial anatomy: New methods for cubic Hermite meshes with extraordinary vertices

Left Ventricular Diastolic and Systolic Material Property Estimation from Image Data

Cardiac image modelling: Breadth and depth in heart disease

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