Fighting MRI Anisotropy: Learning Multiple Cardiac Shapes From a Single Implicit Neural Representation

• URL: http://arxiv.org/abs/2602.11436v1
• Date: Wed, 11 Feb 2026 23:20:14 GMT
• Title: Fighting MRI Anisotropy: Learning Multiple Cardiac Shapes From a Single Implicit Neural Representation
• Authors: Carolina Brás, Soufiane Ben Haddou, Thijs P. Kuipers, Laura Alvarez-Florez, R. Nils Planken, Fleur V. Y. Tjong, Connie Bezzina, Ivana Išgum,
• Abstract summary: We propose to leverage near-isotropic, higher resolution computed tomography angiography (CTA) data of the heart.<n>We use this data to train a single neural implicit function to jointly represent cardiac shapes from CMRI at any resolution.<n>We evaluate the method for the reconstruction of right ventricle (RV) and myocardium (MYO), where MYO simultaneously models endocardial and epicardial left-ventricle surfaces.
• Score: 0.6558127228160233
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The anisotropic nature of short-axis (SAX) cardiovascular magnetic resonance imaging (CMRI) limits cardiac shape analysis. To address this, we propose to leverage near-isotropic, higher resolution computed tomography angiography (CTA) data of the heart. We use this data to train a single neural implicit function to jointly represent cardiac shapes from CMRI at any resolution. We evaluate the method for the reconstruction of right ventricle (RV) and myocardium (MYO), where MYO simultaneously models endocardial and epicardial left-ventricle surfaces. Since high-resolution SAX reference segmentations are unavailable, we evaluate performance by extracting a 4-chamber (4CH) slice of RV and MYO from their reconstructed shapes. When compared with the reference 4CH segmentation masks from CMRI, our method achieved a Dice similarity coefficient of 0.91 $\pm$ 0.07 and 0.75 $\pm$ 0.13, and a Hausdorff distance of 6.21 $\pm$ 3.97 mm and 7.53 $\pm$ 5.13 mm for RV and MYO, respectively. Quantitative and qualitative assessment demonstrate the model's ability to reconstruct accurate, smooth and anatomically plausible shapes, supporting improvements in cardiac shape analysis.

Related papers

• Deformable Image Registration for Self-supervised Cardiac Phase Detection in Multi-View Multi-Disease Cardiac Magnetic Resonance Images [0.613184366698301]
  Individual cardiac cycles complicate automatic temporal comparison or sub-phase analysis.<n>We propose a self-supervised deep learning method for cardiac magnetic resonance (CMR) detection.<n>Our approach achieves improved detection accuracy by 30% - 51% for SAX and 11% - 47% for 4CH in ED and ES.
  arXiv  Detail & Related papers  (2025-10-07T11:40:17Z)
• Time-Varying Coronary Artery Deformation: A Dynamic Skinning Framework for Surgical Training [33.92599418560439]
  This study proposes a novel dynamic modeling framework for coronary arteries using skeletal skinning weights.<n>It aims to achieve precise control over vessel deformation while maintaining real-time performance for surgical simulation applications.
  arXiv  Detail & Related papers  (2025-03-04T02:51:37Z)
• Whole Heart 3D+T Representation Learning Through Sparse 2D Cardiac MR Images [13.686473040836113]
  We introduce a whole-heart self-supervised learning framework to automatically uncover the correlations between spatial and temporal patches throughout the cardiac stacks. We train our model on 14,000 unlabeled CMR data from UK BioBank and evaluate it on 1,000 annotated data.
  arXiv  Detail & Related papers  (2024-06-01T07:08:45Z)
• 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)
• Semantic-aware Temporal Channel-wise Attention for Cardiac Function Assessment [69.02116920364311]
  Existing video-based methods do not pay much attention to the left ventricular region, nor the left ventricular changes caused by motion. We propose a semi-supervised auxiliary learning paradigm with a left ventricular segmentation task, which contributes to the representation learning for the left ventricular region. Our approach achieves state-of-the-art performance on the Stanford dataset with an improvement of 0.22 MAE, 0.26 RMSE, and 1.9% $R2$.
  arXiv  Detail & Related papers  (2023-10-09T05:57:01Z)
• 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)
• Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling [66.75096111651062]
  We created a large-scale dataset of 10,021 thoracic CTs with 157 labels. We applied an ensemble of 3D anatomy segmentation models to extract anatomical pseudo-labels. Our resulting segmentation models demonstrated remarkable performance on CXR.
  arXiv  Detail & Related papers  (2023-06-06T18:01:08Z)
• Bayesian Optimization of 2D Echocardiography Segmentation [2.6947715121689204]
  We use BO to optimize the architectural and training-related hyper parameters of a deep convolutional neural network model. Dice overlaps of 0.95, 0.96, and 0.93 on left ventricular (LV) endocardium, LV epicardium, and left atrium respectively. We also observe significant improvement in derived clinical indices, including smaller median absolute errors for LV end-diastolic volume.
  arXiv  Detail & Related papers  (2022-11-17T20:52:36Z)
• 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)
• A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images [14.185169567232055]
  The assessment of left ventricular (LV) function by myocardial perfusion SPECT (MPS) relies on accurate myocardial segmentation. The purpose of this paper is to develop and validate a new method incorporating deep learning with shape priors to accurately extract the LV myocardium for automatic measurement of LV functional parameters.
  arXiv  Detail & Related papers  (2022-06-07T22:12:11Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)

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.