Learning Whole Heart Mesh Generation From Patient Images For Computational Simulations

• URL: http://arxiv.org/abs/2203.10517v2
• Date: Wed, 8 Nov 2023 10:12:08 GMT
• Title: Learning Whole Heart Mesh Generation From Patient Images For Computational Simulations
• Authors: Fanwei Kong, Shawn Shadden
• Abstract summary: We present a fast and automated deep-learning method to construct simulation-suitable models of the heart from medical images. The approach constructs meshes from 3D patient images by learning to deform a small set of deformation handles on a whole heart template.
• Score: 2.2496714271898166
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Patient-specific cardiac modeling combines geometries of the heart derived from medical images and biophysical simulations to predict various aspects of cardiac function. However, generating simulation-suitable models of the heart from patient image data often requires complicated procedures and significant human effort. We present a fast and automated deep-learning method to construct simulation-suitable models of the heart from medical images. The approach constructs meshes from 3D patient images by learning to deform a small set of deformation handles on a whole heart template. For both 3D CT and MR data, this method achieves promising accuracy for whole heart reconstruction, consistently outperforming prior methods in constructing simulation-suitable meshes of the heart. When evaluated on time-series CT data, this method produced more anatomically and temporally consistent geometries than prior methods, and was able to produce geometries that better satisfy modeling requirements for cardiac flow simulations. Our source code and pretrained networks are available at https://github.com/fkong7/HeartDeformNets.

Related papers

• Explicit Differentiable Slicing and Global Deformation for Cardiac Mesh Reconstruction [8.730291904586656]
  Mesh reconstruction of the cardiac anatomy from medical images is useful for shape and motion measurements and biophysics simulations. Traditional voxel-based approaches rely on pre- and post-processing that compromises image fidelity. We propose a novel explicit differentiable voxelization and slicing (DVS) algorithm that allows gradient backpropagation to a mesh from its slices.
  arXiv  Detail & Related papers  (2024-09-03T17:19:31Z)
• Sequence-aware Pre-training for Echocardiography Probe Guidance [66.35766658717205]
  Cardiac ultrasound faces two major challenges: (1) the inherently complex structure of the heart, and (2) significant individual variations. Previous works have only learned the population-averaged 2D and 3D structures of the heart rather than personalized cardiac structural features. We propose a sequence-aware self-supervised pre-training method to learn personalized 2D and 3D cardiac structural features.
  arXiv  Detail & Related papers  (2024-08-27T12:55:54Z)
• 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. We show it can efficiently handle surface and volumetric meshes by encoding them as graph structures. 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)
• Shape of my heart: Cardiac models through learned signed distance functions [33.29148402516714]
  In this work, the cardiac shape is reconstructed by means of three-dimensional deep signed distance functions with Lipschitz regularity. For this purpose, the shapes of cardiac MRI reconstructions are learned to model the spatial relation of multiple chambers. We demonstrate that this approach is also capable of reconstructing anatomical models from partial data, such as point clouds from a single ventricle.
  arXiv  Detail & Related papers  (2023-08-31T09:02:53Z)
• Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models [75.52575380824051]
  We present a learning method to optimize sub-sampling patterns for compressed sensing multi-coil MRI. We use a single-step reconstruction based on the posterior mean estimate given by the diffusion model and the MRI measurement process. Our method requires as few as five training images to learn effective sampling patterns.
  arXiv  Detail & Related papers  (2023-06-05T22:09:06Z)
• A Generative Shape Compositional Framework to Synthesise Populations of Virtual Chimaeras [52.33206865588584]
  We introduce a generative shape model for complex anatomical structures, learnable from datasets of unpaired datasets. We build virtual chimaeras from databases of whole-heart shape assemblies that each contribute samples for heart substructures. Our approach significantly outperforms a PCA-based shape model (trained with complete data) in terms of generalisability and specificity.
  arXiv  Detail & Related papers  (2022-10-04T13:36:52Z)
• Solving Inverse Problems in Medical Imaging with Score-Based Generative Models [87.48867245544106]
  Reconstructing medical images from partial measurements is an important inverse problem in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) Existing solutions based on machine learning typically train a model to directly map measurements to medical images. We propose a fully unsupervised technique for inverse problem solving, leveraging the recently introduced score-based generative models.
  arXiv  Detail & Related papers  (2021-11-15T05:41:12Z)
• 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)
• A Deep-Learning Approach For Direct Whole-Heart Mesh Reconstruction [1.8047694351309207]
  We propose a novel deep-learning-based approach that directly predicts whole heart surface meshes from volumetric CT and MR image data. Our method demonstrated promising performance of generating high-resolution and high-quality whole heart reconstructions.
  arXiv  Detail & Related papers  (2021-02-16T00:39:43Z)
• Fed-Sim: Federated Simulation for Medical Imaging [131.56325440976207]
  We introduce a physics-driven generative approach that consists of two learnable neural modules. We show that our data synthesis framework improves the downstream segmentation performance on several datasets.
  arXiv  Detail & Related papers  (2020-09-01T19:17:46Z)

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.