3D Cardiac Anatomy Generation Using Mesh Latent Diffusion Models

• URL: http://arxiv.org/abs/2508.14122v1
• Date: Mon, 18 Aug 2025 16:53:20 GMT
• Title: 3D Cardiac Anatomy Generation Using Mesh Latent Diffusion Models
• Authors: Jolanta Mozyrska, Marcel Beetz, Luke Melas-Kyriazi, Vicente Grau, Abhirup Banerjee, Alfonso Bueno-Orovio,
• Abstract summary: We investigate the application of Latent Diffusion Models for generating 3D meshes of human cardiac anatomies.<n>We apply the proposed model on a dataset of 3D meshes of left ventricular cardiac anatomies from patients with acute myocardial infarction.<n>The MeshLDM successfully captures characteristics of the cardiac shapes at end-diastolic (relaxation) and end-systolic (contraction) cardiac phases, generating meshes with a 2.4% difference in population mean compared to the gold standard.
• Score: 9.749160335898255
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models have recently gained immense interest for their generative capabilities, specifically the high quality and diversity of the synthesized data. However, examples of their applications in 3D medical imaging are still scarce, especially in cardiology. Generating diverse realistic cardiac anatomies is crucial for applications such as in silico trials, electromechanical computer simulations, or data augmentations for machine learning models. In this work, we investigate the application of Latent Diffusion Models (LDMs) for generating 3D meshes of human cardiac anatomies. To this end, we propose a novel LDM architecture -- MeshLDM. We apply the proposed model on a dataset of 3D meshes of left ventricular cardiac anatomies from patients with acute myocardial infarction and evaluate its performance in terms of both qualitative and quantitative clinical and 3D mesh reconstruction metrics. The proposed MeshLDM successfully captures characteristics of the cardiac shapes at end-diastolic (relaxation) and end-systolic (contraction) cardiac phases, generating meshes with a 2.4% difference in population mean compared to the gold standard.

Related papers

• Patient-Specific Dynamic Digital-Physical Twin for Coronary Intervention Training: An Integrated Mixed Reality Approach [33.92599418560439]
  Existing training systems lack accurate simulation of cardiac physiological dynamics.<n>This study develops a comprehensive dynamic cardiac model research framework based on 4D-CTA.
  arXiv  Detail & Related papers  (2025-05-16T06:13:55Z)
• Leveraging Cardiovascular Simulations for In-Vivo Prediction of Cardiac Biomarkers [43.17768785084301]
  We train an amortized neural posterior estimator on a newly built large dataset of cardiac simulations.<n>We incorporate elements modeling effects to better align simulated data with real-world measurements.<n>The proposed framework can further integrate in-vivo data sources to refine its predictive capabilities on real-world data.
  arXiv  Detail & Related papers  (2024-12-23T13:05:17Z)
• A personalized time-resolved 3D mesh generative model for unveiling normal heart dynamics [6.6350578770951385]
  We develop a conditional generative model, MeshHeart, to learn the distribution of shape and motion patterns for the left ventricles of the heart.<n>Based on MeshHeart, we investigate the latent space of 3D+t cardiac mesh sequences and propose a distance metric, latent delta, which quantifies the deviation of a real heart from its personalised normative pattern.
  arXiv  Detail & Related papers  (2024-09-20T18:08:37Z)
• Generation of patient specific cardiac chamber models using generative neural networks under a Bayesian framework for electroanatomical mapping [1.9336815376402723]
  A probabilistic machine learning model trained on a library of CT/MRI scans of the heart can be used during electroanatomical mapping. We introduce a Bayesian approach to surface reconstruction of cardiac chamber models from a sparse 3D point cloud data. We show how they provide insight into what the neural network learns from the segmented CT/MRI images used to train the network.
  arXiv  Detail & Related papers  (2023-11-27T03:47:33Z)
• Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI [43.47826598981827]
  We introduce HybridVNet, a novel architecture for direct image-to-mesh extraction.<n>We show it can efficiently handle surface and volumetric meshes by encoding them as graph structures.<n>Our model combines traditional convolutional networks with variational graph generative models, deep supervision and mesh-specific regularisation.
  arXiv  Detail & Related papers  (2023-11-22T21:51:29Z)
• 3D Shape-Based Myocardial Infarction Prediction Using Point Cloud Classification Networks [12.231105631777881]
  Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases. We investigate the utility of complete 3D cardiac shapes in the form of point clouds for an improved detection of MI events.
  arXiv  Detail & Related papers  (2023-07-14T12:21:11Z)
• Validated respiratory drug deposition predictions from 2D and 3D medical images with statistical shape models and convolutional neural networks [47.187609203210705]
  We aim to develop and validate an automated computational framework for patient-specific deposition modelling. An image processing approach is proposed that could produce 3D patient respiratory geometries from 2D chest X-rays and 3D CT images.
  arXiv  Detail & Related papers  (2023-03-02T07:47:07Z)
• Three-dimensional micro-structurally informed in silico myocardium -- towards virtual imaging trials in cardiac diffusion weighted MRI [58.484353709077034]
  We propose a novel method to generate a realistic numerical phantom of myocardial microstructure. In-silico tissue models enable evaluating quantitative models of magnetic resonance imaging.
  arXiv  Detail & Related papers  (2022-08-22T22:01:44Z)
• Deep Implicit Statistical Shape Models for 3D Medical Image Delineation [47.78425002879612]
  3D delineation of anatomical structures is a cardinal goal in medical imaging analysis. Prior to deep learning, statistical shape models that imposed anatomical constraints and produced high quality surfaces were a core technology. We present deep implicit statistical shape models (DISSMs), a new approach to delineation that marries the representation power of CNNs with the robustness of SSMs.
  arXiv  Detail & Related papers  (2021-04-07T01:15:06Z)
• CNN-based Cardiac Motion Extraction to Generate Deformable Geometric Left Ventricle Myocardial Models from Cine MRI [0.0]
  We propose a framework for the development of patient-specific geometric models of LV myocardium from cine cardiac MR images. We use the VoxelMorph-based convolutional neural network (CNN) to propagate the isosurface mesh and volume mesh of the end-diastole frame to the subsequent frames of the cardiac cycle.
  arXiv  Detail & Related papers  (2021-03-30T21:34:29Z)
• Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE [66.63629641650572]
  We propose a method to model 3D MR brain volumes distribution by combining a 2D slice VAE with a Gaussian model that captures the relationships between slices. We also introduce a novel evaluation method for generated volumes that quantifies how well their segmentations match those of true brain anatomy.
  arXiv  Detail & Related papers  (2020-07-09T13:23:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.