Evaluation of an open-source pipeline to create patient-specific left atrial models: A reproducibility study

Solís-Lemus, José Alonso; Baptiste, Tiffany; Barrows, Rosie K; Sillett, Charles; Gharaviri, Ali; Raffaele, Giulia; Razeghi, Orod; Strocchi, Marina; Sim, Iain; Kotadia, Irum; Bodagh, Neil; O’Hare, Dermot; O’Neill, Mark; Williams, Steven E.; Roney, Caroline; Niederer, Steven

doi:10.1016/j.compbiomed.2023.107009

Cited by 10 publications

(4 citation statements)

References 42 publications

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“…To date, a wide range of automated anatomical model creation workflows based on machine learning have been proposed allowing large-scale population analysis such as on the UK Biobank dataset 1,23,24 , however, these models only account for surfaces depicting the overall heart structure, but not high-quality volumetric meshes. There are also volumetric mesh creation workflows existing for atria 25,26 , ventricles 12 and whole hearts 11,27 , but these have only been applied to smaller datasets (<100) due to the high computational costs and manual steps required. Here we reported a fully automated volumetric mesh generation workflow that can create personalized anatomical meshes at scale within clinical timescales (∼5 minutes/CDT).…”

Section: Discussionmentioning

confidence: 99%

Developing Cardiac Digital Twins at Scale: Insights from Personalised Myocardial Conduction Velocity

Qian,

Ugurlu,

Fairweather

et al. 2023

Preprint

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Large-cohort studies using cardiovascular imaging and diagnostic datasets have assessed cardiac anatomy, function, and outcomes, but typically do not reveal underlying biological mechanisms. Cardiac digital twins (CDTs) provide personalized physics- and physiology-constrainedin-silicorepresentations, enabling inference of multi-scale properties tied to these mechanisms.We constructed 3464 anatomically-accurate CDTs using cardiac magnetic resonance images from UK biobank and personalised their myocardial conduction velocities (CVs) from electrocardiograms (ECG), through an automated framework.We found well-known sex-specific differences in QRS duration were fully explained by myocardial anatomy, as CV remained consistent across sexes. Conversely, significant associations of CV with ageing and increased BMI suggest myocardial tissue remodelling. Novel associations were observed with left ventricular ejection fraction and mental-health phenotypes, through a phenome-wide association study, and CV was also linked with adverse clinical outcomes.Our study highlights the utility of population-based CDTs in assessing intersubject variability and uncovering strong links with mental health.

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Section: Discussionmentioning

confidence: 99%

Developing Cardiac Digital Twins at Scale: Insights from Personalised Myocardial Conduction Velocity

Qian,

Ugurlu,

Fairweather

et al. 2023

Preprint

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“…Intraclass correlation (ICC) has long been recognized as a vital reliability and reproducibility metric, especially for gauging similarity in paired data when the order of pairing is not preserved [96,106,107]. In brain imaging, it serves as a popular baseline for test-retest (TRT) reliability assessments, often in conjunction with the Dice coefficient [108][109][110][111][112].…”

Section: Plos Computational Biologymentioning

confidence: 99%

Topological state-space estimation of functional human brain networks

Chung,

Huang,

Carroll

et al. 2024

PLoS Comput Biol

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We introduce an innovative, data-driven topological data analysis (TDA) technique for estimating the state spaces of dynamically changing functional human brain networks at rest. Our method utilizes the Wasserstein distance to measure topological differences, enabling the clustering of brain networks into distinct topological states. This technique outperforms the commonly used k-means clustering in identifying brain network state spaces by effectively incorporating the temporal dynamics of the data without the need for explicit model specification. We further investigate the genetic underpinnings of these topological features using a twin study design, examining the heritability of such state changes. Our findings suggest that the topology of brain networks, particularly in their dynamic state changes, may hold significant hidden genetic information.

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“…Ventricular fibres can also be mapped from a geometry with a known fibre orientation using universal ventricular coordinates [ 80 ], a set of coordinates that uniquely define a point within a biventricular anatomy independently of the geometry. Similarly, universal atrial coordinates have been used to map ex-vivo DT-MRI fibres from an atlas onto patient-specific biatrial anatomies [ 59 , 60 , 81 ]. An example is shown in the right side of the second row of Figure 2 .…”

Section: Anatomical Model Generationmentioning

confidence: 99%

Advancing clinical translation of cardiac biomechanics models: a comprehensive review, applications and future pathways

Rodero,

Baptiste,

Barrows

et al. 2023

Front. Phys.

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Cardiac mechanics models are developed to represent a high level of detail, including refined anatomies, accurate cell mechanics models, and platforms to link microscale physiology to whole-organ function. However, cardiac biomechanics models still have limited clinical translation. In this review, we provide a picture of cardiac mechanics models, focusing on their clinical translation. We review the main experimental and clinical data used in cardiac models, as well as the steps followed in the literature to generate anatomical meshes ready for simulations. We describe the main models in active and passive mechanics and the different lumped parameter models to represent the circulatory system. Lastly, we provide a summary of the state-of-the-art in terms of ventricular, atrial, and four-chamber cardiac biomechanics models. We discuss the steps that may facilitate clinical translation of the biomechanics models we describe. A well-established software to simulate cardiac biomechanics is lacking, with all available platforms involving different levels of documentation, learning curves, accessibility, and cost. Furthermore, there is no regulatory framework that clearly outlines the verification and validation requirements a model has to satisfy in order to be reliably used in applications. Finally, better integration with increasingly rich clinical and/or experimental datasets as well as machine learning techniques to reduce computational costs might increase model reliability at feasible resources. Cardiac biomechanics models provide excellent opportunities to be integrated into clinical workflows, but more refinement and careful validation against clinical data are needed to improve their credibility. In addition, in each context of use, model complexity must be balanced with the associated high computational cost of running these models.

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Evaluation of an open-source pipeline to create patient-specific left atrial models: A reproducibility study

Cited by 10 publications

References 42 publications

Developing Cardiac Digital Twins at Scale: Insights from Personalised Myocardial Conduction Velocity

Developing Cardiac Digital Twins at Scale: Insights from Personalised Myocardial Conduction Velocity

Topological state-space estimation of functional human brain networks

Advancing clinical translation of cardiac biomechanics models: a comprehensive review, applications and future pathways

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