ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2021
A completely automated pipeline for 3D reconstruction of human heart from 2D cine magnetic resonance slices.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Technologies, Clinical Applications, and Implementation Barriers of Digital Twins in Precision Cardiology: Systematic Review.JMIR cardio · 2026Pooled it
- Cardiac remodelling in type 2 diabetes: Pathophysiological mechanisms and opportunities for multiscale computational modelling and simulation.The Journal of physiology · 2026Review
- Assessing the importance of sex and disease-specific anatomy in electrophysiology and mechanical simulations with a newly developed public virtual cohort of four-chamber heart models.PLoS computational biology · 2026Article
- 3D Segmentation of Multi-contrast Cardiac Magnetic Resonances With Topological Correction and Synthetic Data Augmentation.Annals of biomedical engineering · 2026Article
- 3D Spatiotemporal cardiac reconstruction for predicting MACE in acute myocardial infarction.NPJ digital medicine · 2026Article
- Learning from multiple readings for axial spondyloarthritis classification of the sacroiliac joints.Scientific reports · 2026Article
- Toward cardiac electrophysiology digital twins with an efficient open source scalable solver on GPU clusters.Scientific reports · 2026Article
- 3D cardiac shape analysis with variational point cloud autoencoders for myocardial infarction prediction and virtual heart synthesis.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2025Article
- Current progress of digital twin construction using medical imaging.Journal of applied clinical medical physics · 2025Review
- Harnessing 12-lead ECG and MRI data to personalise repolarisation profiles in cardiac digital twin models for enhanced virtual drug testing.Medical image analysis · 2025Article
- Multi-modal integration of MRI and global chamber charge density mapping for the evaluation of atrial fibrillation.Royal Society open science · 2025Article
- Deep conditional generative model for personalization of 12-lead electrocardiograms and cardiovascular risk prediction.Frontiers in digital health · 2025Article
- Cardiac digital twins at scale from MRI: Open tools and representative models from ~ 55000 UK Biobank participants.PloS one · 2025Article
- Leveraging 3D Atrial Geometry for the Evaluation of Atrial Fibrillation: A Comprehensive Review.Journal of clinical medicine · 2024Review
- Toward Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference.IEEE transactions on medical imaging · 2024Article
- 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 Comprehensive Review of Vision-Based 3D Reconstruction Methods.Sensors (Basel, Switzerland) · 2024Review
- Advancements in artificial intelligence-driven techniques for interventional cardiology.Cardiology journal · 2024Review
- The Lifelong Impact of Artificial Intelligence and Clinical Prediction Models on Patients With Tetralogy of Fallot.CJC pediatric and congenital heart disease · 2023Review
- Artificial Intelligence-Based Methods for Precision Cardiovascular Medicine.Journal of personalized medicine · 2023Review
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Authors and funding
8 authors.
Funding
Abstract
Cardiac magnetic resonance (CMR) imaging is a valuable modality in the diagnosis and characterization of cardiovascular diseases, since it can identify abnormalities in structure and function of the myocardium non-invasively and without the need for ionizing radiation. However, in clinical practice, it is commonly acquired as a collection of separated and independent 2D image planes, which limits its accuracy in 3D analysis. This paper presents a completely automated pipeline for generating patient-specific 3D biventricular heart models from cine magnetic resonance (MR) slices. Our pipeline automatically selects the relevant cine MR images, segments them using a deep learning-based method to extract the heart contours, and aligns the contours in 3D space correcting possible misalignments due to breathing or subject motion first using the intensity and contours information from the cine data and next with the help of a statistical shape model. Finally, the sparse 3D representation of the contours is used to generate a smooth 3D biventricular mesh. The computational pipeline is applied and evaluated in a CMR dataset of 20 healthy subjects. Our results show an average reduction of misalignment artefacts from 1.82 ± 1.60 mm to 0.72 ± 0.73 mm over 20 subjects, in terms of distance from the final reconstructed mesh. The high-resolution 3D biventricular meshes obtained with our computational pipeline are used for simulations of electrical activation patterns, showing agreement with non-invasive electrocardiographic imaging. The automatic methodologies presented here for patient-specific MR imaging-based 3D biventricular representations contribute to the efficient realization of precision medicine, enabling the enhanced interpretability of clinical data, the digital twin vision through patient-specific image-based modelling and simulation, and augmented reality applications. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.
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