ArticleThe international journal of cardiovascular imaging2018
4D modelling for rapid assessment of biventricular function in congenital heart disease.
Article in The international journal of cardiovascular imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Motion correction and super-resolution for multi-slice cardiac magnetic resonance imaging via an end-to-end deep learning approach.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2024Article
- A deep learning approach for fully automated cardiac shape modeling in tetralogy of Fallot.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2023Article
- Estimating cardiomyofiber strain in vivo by solving a computational model.Medical image analysis · 2021Article
- Big Data, Extracting Insights, Comprehension, and Analytics in Cardiology: An Overview.Journal of healthcare engineering · 2021Review
- Three-dimensional biventricular strains in pulmonary arterial hypertension patients using hyperelastic warping.Computer methods and programs in biomedicine · 2020Article
- Atlas-Based Computational Analysis of Heart Shape and Function in Congenital Heart Disease.Journal of cardiovascular translational research · 2018Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Although more patients with congenital heart disease (CHD) are now living longer due to better surgical interventions, they require regular imaging to monitor cardiac performance. There is a need for robust clinical tools which can accurately assess cardiac function of both the left and right ventricles in these patients. We have developed methods to rapidly quantify 4D (3D + time) biventricular function from standard cardiac MRI examinations. A finite element model was interactively customized to patient images using guide-point modelling. Computational efficiency and ability to model large deformations was improved by predicting cardiac motion for the left ventricle and epicardium with a polar model. In addition, large deformations through the cycle were more accurately modeled using a Cartesian deformation penalty term. The model was fitted to user-defined guide points and image feature tracking displacements throughout the cardiac cycle. We tested the methods in 60 cases comprising a variety of congenital heart diseases and showed good correlation with the gold standard manual analysis, with acceptable inter-observer error. The algorithm was considerably faster than standard analysis and shows promise as a clinical tool for patients with CHD.
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Registered trials
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