A Semi-automatic Pipeline for Generation of Large Cohorts of Four-Chamber Heart Meshes

Marina Strocchi; Cristobal Rodero; Caroline H. Roney; Caroline Mendonca Costa; Gernot Plank; Pablo Lamata; Steven A. Niederer

doi:10.1007/978-1-0716-3527-8_7

A Semi-automatic Pipeline for Generation of Large Cohorts of Four-Chamber Heart Meshes

Marina Strocchi, Cristobal Rodero, Caroline H. Roney, Caroline Mendonca Costa, Gernot Plank, Pablo Lamata, Steven A. Niederer

Research output: Contribution to journal › Article › peer-review

Abstract

Computational models for cardiac electro-mechanics have been increasingly used to further understand heart function. Small cohort and single patient computational studies provide useful insight into cardiac pathophysiology and response to therapy. However, these smaller studies have limited capability to capture the high level of anatomical variability seen in cardiology patients. Larger cohort studies are, on the other hand, more representative of the study population, but building several patient-specific anatomical meshes can be time-consuming and requires access to larger datasets of imaging data, image processing software to label anatomical structures and tools to create high fidelity anatomical meshes. Limited access to these tools and data might limit advances in this area of research. In this chapter, we present our semi-automatic pipeline to build patient-specific four-chamber heart meshes from CT imaging datasets, including ventricular myofibers and a set of universal ventricular and atrial coordinates. This pipeline was applied to CT images from both heart failure patients and healthy controls to generate cohorts of tetrahedral meshes suitable for electro-mechanics simulations. Both cohorts were made publicly available in order to promote computational studies employing large virtual cohorts.

Original language	English
Pages (from-to)	117-127
Number of pages	11
Journal	Methods in molecular biology (Clifton, N.J.)
Volume	2735
DOIs	https://doi.org/10.1007/978-1-0716-3527-8_7
Publication status	Published - 2024

Keywords

Computer models
Electromechanics
Finite elements
Four-chamber heart mesh
Virtual cohort

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10.1007/978-1-0716-3527-8_7

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title = "A Semi-automatic Pipeline for Generation of Large Cohorts of Four-Chamber Heart Meshes",

abstract = "Computational models for cardiac electro-mechanics have been increasingly used to further understand heart function. Small cohort and single patient computational studies provide useful insight into cardiac pathophysiology and response to therapy. However, these smaller studies have limited capability to capture the high level of anatomical variability seen in cardiology patients. Larger cohort studies are, on the other hand, more representative of the study population, but building several patient-specific anatomical meshes can be time-consuming and requires access to larger datasets of imaging data, image processing software to label anatomical structures and tools to create high fidelity anatomical meshes. Limited access to these tools and data might limit advances in this area of research. In this chapter, we present our semi-automatic pipeline to build patient-specific four-chamber heart meshes from CT imaging datasets, including ventricular myofibers and a set of universal ventricular and atrial coordinates. This pipeline was applied to CT images from both heart failure patients and healthy controls to generate cohorts of tetrahedral meshes suitable for electro-mechanics simulations. Both cohorts were made publicly available in order to promote computational studies employing large virtual cohorts.",

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doi = "10.1007/978-1-0716-3527-8_7",

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AU - Strocchi, Marina

AU - Rodero, Cristobal

AU - Roney, Caroline H.

AU - Mendonca Costa, Caroline

AU - Plank, Gernot

AU - Lamata, Pablo

AU - Niederer, Steven A.

PY - 2024

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AB - Computational models for cardiac electro-mechanics have been increasingly used to further understand heart function. Small cohort and single patient computational studies provide useful insight into cardiac pathophysiology and response to therapy. However, these smaller studies have limited capability to capture the high level of anatomical variability seen in cardiology patients. Larger cohort studies are, on the other hand, more representative of the study population, but building several patient-specific anatomical meshes can be time-consuming and requires access to larger datasets of imaging data, image processing software to label anatomical structures and tools to create high fidelity anatomical meshes. Limited access to these tools and data might limit advances in this area of research. In this chapter, we present our semi-automatic pipeline to build patient-specific four-chamber heart meshes from CT imaging datasets, including ventricular myofibers and a set of universal ventricular and atrial coordinates. This pipeline was applied to CT images from both heart failure patients and healthy controls to generate cohorts of tetrahedral meshes suitable for electro-mechanics simulations. Both cohorts were made publicly available in order to promote computational studies employing large virtual cohorts.

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A Semi-automatic Pipeline for Generation of Large Cohorts of Four-Chamber Heart Meshes

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