Spatio-temporal neural distance fields for conditional generative modeling of the heart

• URL: http://arxiv.org/abs/2407.10663v1
• Date: Mon, 15 Jul 2024 12:26:52 GMT
• Title: Spatio-temporal neural distance fields for conditional generative modeling of the heart
• Authors: Kristine Sørensen, Paula Diez, Jan Margeta, Yasmin El Youssef, Michael Pham, Jonas Jalili Pedersen, Tobias Kühl, Ole de Backer, Klaus Kofoed, Oscar Camara, Rasmus Paulsen,
• Abstract summary: We introduce a conditional generative model, where the shape and movement is modeled implicitly in the form of atemporal neural field and conditioned on clinical demography. It is tested on the left atrium, where it outperforms current methods for sequence anatomical completion synthetic sequences. This means we can infer functional measurements from a static image, generate synthetic populations with specified demography or disease and investigate how non-imaging clinical data effect the shape and motion of cardiac anatomies.
• Score: 0.10481793104300056
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rhythmic pumping motion of the heart stands as a cornerstone in life, as it circulates blood to the entire human body through a series of carefully timed contractions of the individual chambers. Changes in the size, shape and movement of the chambers can be important markers for cardiac disease and modeling this in relation to clinical demography or disease is therefore of interest. Existing methods for spatio-temporal modeling of the human heart require shape correspondence over time or suffer from large memory requirements, making it difficult to use for complex anatomies. We introduce a novel conditional generative model, where the shape and movement is modeled implicitly in the form of a spatio-temporal neural distance field and conditioned on clinical demography. The model is based on an auto-decoder architecture and aims to disentangle the individual variations from that related to the clinical demography. It is tested on the left atrium (including the left atrial appendage), where it outperforms current state-of-the-art methods for anatomical sequence completion and generates synthetic sequences that realistically mimics the shape and motion of the real left atrium. In practice, this means we can infer functional measurements from a static image, generate synthetic populations with specified demography or disease and investigate how non-imaging clinical data effect the shape and motion of cardiac anatomies.

Related papers

• A Personalised 3D+t Mesh Generative Model for Unveiling Normal Heart Dynamics [6.6350578770951385]
  We develop a conditional generative model, MeshHeart, to learn the distribution of cardiac shape and motion patterns. MeshHeart is capable of generating 3D+t cardiac mesh sequences, taking into account clinical factors such as age, sex, weight and height. We propose a novel distance metric latent delta, which quantifies the deviation of a real heart from its personalised normative pattern in the latent space.
  arXiv  Detail & Related papers  (2024-09-20T18:08:37Z)
• Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation [56.34634121544929]
  In this study, we first construct the brain-effective network via the dynamic causal model. We then introduce an interpretable graph learning framework termed Spatio-Temporal Embedding ODE (STE-ODE) This framework incorporates specifically designed directed node embedding layers, aiming at capturing the dynamic interplay between structural and effective networks.
  arXiv  Detail & Related papers  (2024-05-21T20:37:07Z)
• 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)
• Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
  We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy. We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
  arXiv  Detail & Related papers  (2023-03-07T09:40:22Z)
• CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy [16.84316344437967]
  Two key questions in cardiac image analysis are to assess the anatomy and motion of the heart from images. We propose a conditional generative model to describe the 4D-temporal anatomy of the heart and its interaction with nonimaging clinical factors.
  arXiv  Detail & Related papers  (2023-01-30T17:36:12Z)
• 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)
• Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical Data [13.819131884449881]
  We propose a novel conditional generative model to describe the changes of 3D anatomy of the heart during ageing. We train the model on a large-scale cross-sectional dataset of cardiac anatomies and evaluate on both cross-sectional and longitudinal datasets.
  arXiv  Detail & Related papers  (2022-08-28T06:14:39Z)
• 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)
• Imposing Temporal Consistency on Deep Monocular Body Shape and Pose Estimation [67.23327074124855]
  This paper presents an elegant solution for the integration of temporal constraints in the fitting process. We derive parameters of a sequence of body models, representing shape and motion of a person, including jaw poses, facial expressions, and finger poses. Our approach enables the derivation of realistic 3D body models from image sequences, including facial expression and articulated hands.
  arXiv  Detail & Related papers  (2022-02-07T11:11:55Z)
• Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets [5.178090215294132]
  We introduce a probabilistic framework for joint cardiac data imputation and personalisation of cardiovascular mechanistic models. Our approach is based on a variational framework for the joint inference of an imputation model of cardiac information from the available features. We show that our model allows accurate imputation of missing cardiac features in datasets containing minimal heart information.
  arXiv  Detail & Related papers  (2020-10-02T15:31:36Z)
• Microvascular Dynamics from 4D Microscopy Using Temporal Segmentation [81.30750944868142]
  We are able to track changes in cerebral blood volume over time and identify spontaneous arterial dilations that propagate towards the pial surface. This new imaging capability is a promising step towards characterizing the hemodynamic response function upon which functional magnetic resonance imaging (fMRI) is based.
  arXiv  Detail & Related papers  (2020-01-14T22:55:03Z)

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.