Generative Myocardial Motion Tracking via Latent Space Exploration with Biomechanics-informed Prior

• URL: http://arxiv.org/abs/2206.03830v1
• Date: Wed, 8 Jun 2022 12:02:00 GMT
• Title: Generative Myocardial Motion Tracking via Latent Space Exploration with Biomechanics-informed Prior
• Authors: Chen Qin, Shuo Wang, Chen Chen, Wenjia Bai and Daniel Rueckert
• Abstract summary: Myocardial motion and deformation are rich descriptors that characterize cardiac function. Image registration, as the most commonly used technique for myocardial motion tracking, is an ill-posed inverse problem. We propose a novel method that can implicitly learn an application-specific biomechanics-informed prior.
• Score: 16.129107206314043
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Myocardial motion and deformation are rich descriptors that characterize cardiac function. Image registration, as the most commonly used technique for myocardial motion tracking, is an ill-posed inverse problem which often requires prior assumptions on the solution space. In contrast to most existing approaches which impose explicit generic regularization such as smoothness, in this work we propose a novel method that can implicitly learn an application-specific biomechanics-informed prior and embed it into a neural network-parameterized transformation model. Particularly, the proposed method leverages a variational autoencoder-based generative model to learn a manifold for biomechanically plausible deformations. The motion tracking then can be performed via traversing the learnt manifold to search for the optimal transformations while considering the sequence information. The proposed method is validated on three public cardiac cine MRI datasets with comprehensive evaluations. The results demonstrate that the proposed method can outperform other approaches, yielding higher motion tracking accuracy with reasonable volume preservation and better generalizability to varying data distributions. It also enables better estimates of myocardial strains, which indicates the potential of the method in characterizing spatiotemporal signatures for understanding cardiovascular diseases.

Related papers

• LaMoD: Latent Motion Diffusion Model For Myocardial Strain Generation [5.377722774297911]
  We introduce a novel Latent Motion Diffusion model (LaMoD) to predict highly accurate DENSE motions from standard CMR videos. Experimental results demonstrate that our proposed method, LaMoD, significantly improves the accuracy of motion analysis in standard CMR images.
  arXiv  Detail & Related papers  (2024-07-02T12:54:32Z)
• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• Refining Myocardial Infarction Detection: A Novel Multi-Modal Composite Kernel Strategy in One-Class Classification [14.469786240272365]
  Early detection of myocardial infarction (MI) is vital to prevent further damage. This study introduces a novel method for early MI detection using a one-class classification (OCC) algorithm in echocardiography. Our proposed multi-view approach achieves a geometric mean of 71.24%, signifying a substantial advancement in echocardiography-based MI diagnosis.
  arXiv  Detail & Related papers  (2024-02-09T16:41:50Z)
• Unsupervised Discovery of Interpretable Directions in h-space of Pre-trained Diffusion Models [63.1637853118899]
  We propose the first unsupervised and learning-based method to identify interpretable directions in h-space of pre-trained diffusion models. We employ a shift control module that works on h-space of pre-trained diffusion models to manipulate a sample into a shifted version of itself. By jointly optimizing them, the model will spontaneously discover disentangled and interpretable directions.
  arXiv  Detail & Related papers  (2023-10-15T18:44:30Z)
• RIDE: Self-Supervised Learning of Rotation-Equivariant Keypoint Detection and Invariant Description for Endoscopy [83.4885991036141]
  RIDE is a learning-based method for rotation-equivariant detection and invariant description. It is trained in a self-supervised manner on a large curation of endoscopic images. It sets a new state-of-the-art performance on matching and relative pose estimation tasks.
  arXiv  Detail & Related papers  (2023-09-18T08:16:30Z)
• Retrieving space-dependent polarization transformations via near-optimal quantum process tomography [55.41644538483948]
  We investigate the application of genetic and machine learning approaches to tomographic problems. We find that the neural network-based scheme provides a significant speed-up, that may be critical in applications requiring a characterization in real-time. We expect these results to lay the groundwork for the optimization of tomographic approaches in more general quantum processes.
  arXiv  Detail & Related papers  (2022-10-27T11:37:14Z)
• Learning correspondences of cardiac motion from images using biomechanics-informed modeling [7.193217430660012]
  We propose an explicit biomechanics-informed prior as regularization on the predicted displacement vector field (DVF) Our proposed methods better preserve biomechanical properties by visual assessment and show advantages in segmentation performance using quantitative evaluation metrics.
  arXiv  Detail & Related papers  (2022-09-01T20:59:26Z)
• Deep Statistic Shape Model for Myocardium Segmentation [10.381467202920303]
  We propose a novel end-to-end deep statistic shape model to focus on myocardium segmentation with both shape integrity and boundary correspondence preserving. Deep neural network is used to predict the transformation parameters, which are then used to warp the mean point cloud to the image domain. A differentiable rendering layer is introduced to incorporate mask supervision into the framework to learn more accurate point clouds.
  arXiv  Detail & Related papers  (2022-07-21T17:01:24Z)
• Motion Pyramid Networks for Accurate and Efficient Cardiac Motion Estimation [51.72616167073565]
  We propose Motion Pyramid Networks, a novel deep learning-based approach for accurate and efficient cardiac motion estimation. We predict and fuse a pyramid of motion fields from multiple scales of feature representations to generate a more refined motion field. We then use a novel cyclic teacher-student training strategy to make the inference end-to-end and further improve the tracking performance.
  arXiv  Detail & Related papers  (2020-06-28T21:03:19Z)
• Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI [15.686391154738006]
  We propose a novel method that can implicitly learn biomechanics-informed regularisation. The proposed method is validated in the context of myocardial motion tracking on 2D stacks of cardiac MRI data.
  arXiv  Detail & Related papers  (2020-06-08T16:29:13Z)
• Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis [64.82680813427054]
  Plant diseases serve as one of main threats to food security and crop production. One popular approach is to transform this problem as a leaf image classification task, which can be addressed by the powerful convolutional neural networks (CNNs) We propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information.
  arXiv  Detail & Related papers  (2020-03-17T09:51:30Z)

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.