Neural Fields for Continuous Periodic Motion Estimation in 4D Cardiovascular Imaging

• URL: http://arxiv.org/abs/2407.20728v1
• Date: Tue, 30 Jul 2024 10:50:51 GMT
• Title: Neural Fields for Continuous Periodic Motion Estimation in 4D Cardiovascular Imaging
• Authors: Simone Garzia, Patryk Rygiel, Sven Dummer, Filippo Cademartiri, Simona Celi, Jelmer M. Wolterink,
• Abstract summary: Time-resolved three-dimensional flow MRI (4D flow MRI) provides a unique non-invasive solution to visualize and quantify hemodynamics in blood vessels such as the aortic arch. Most current analysis methods for arterial 4D flow MRI use static artery walls because of the difficulty in obtaining a full cycle segmentation. We propose a neural fields-based method that directly estimates continuous periodic wall deformations throughout the cardiac cycle.
• Score: 1.1786989970743633
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time-resolved three-dimensional flow MRI (4D flow MRI) provides a unique non-invasive solution to visualize and quantify hemodynamics in blood vessels such as the aortic arch. However, most current analysis methods for arterial 4D flow MRI use static artery walls because of the difficulty in obtaining a full cycle segmentation. To overcome this limitation, we propose a neural fields-based method that directly estimates continuous periodic wall deformations throughout the cardiac cycle. For a 3D + time imaging dataset, we optimize an implicit neural representation (INR) that represents a time-dependent velocity vector field (VVF). An ODE solver is used to integrate the VVF into a deformation vector field (DVF), that can deform images, segmentation masks, or meshes over time, thereby visualizing and quantifying local wall motion patterns. To properly reflect the periodic nature of 3D + time cardiovascular data, we impose periodicity in two ways. First, by periodically encoding the time input to the INR, and hence VVF. Second, by regularizing the DVF. We demonstrate the effectiveness of this approach on synthetic data with different periodic patterns, ECG-gated CT, and 4D flow MRI data. The obtained method could be used to improve 4D flow MRI analysis.

Related papers

• Physics-informed graph neural networks for flow field estimation in carotid arteries [2.0437999068326276]
  Hemodynamic quantities are valuable biomedical risk factors for cardiovascular pathology such as atherosclerosis. In this work, we create a surrogate model for hemodynamic flow field estimation, powered by machine learning. We train graph neural networks that include priors about the underlying symmetries and physics, limiting the amount of data required for training. This shows that physics-informed graph neural networks can be trained using 4D flow MRI data to estimate blood flow in unseen carotid artery geometries.
  arXiv  Detail & Related papers  (2024-08-13T13:09:28Z)
• Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI [76.60362295758596]
  Off-resonance artifacts in magnetic resonance imaging (MRI) are visual distortions that occur when the actual resonant frequencies of spins within the imaging volume differ from the expected frequencies used to encode spatial information. We propose to resolve these artifacts by lifting the 2D MRI reconstruction problem to 3D, introducing an additional "spectral" dimension to model this off-resonance.
  arXiv  Detail & Related papers  (2023-11-22T05:44:51Z)
• Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI [1.207455285737927]
  Our study investigates SIRENs for time-varying 3-directional velocity fields measured in the aorta by 4D flow MRI. We trained our method on voxel coordinates and benchmarked our approach using synthetic measurements and a real 4D flow MRI scan. Our optimized SIREN architecture outperformed state-of-the-art techniques, producing denoised and super-resolved velocity fields from clinical data.
  arXiv  Detail & Related papers  (2023-02-24T08:42:04Z)
• Diffusion Deformable Model for 4D Temporal Medical Image Generation [47.03842361418344]
  Temporal volume images with 3D+t (4D) information are often used in medical imaging to statistically analyze temporal dynamics or capture disease progression. We present a novel deep learning model that generates intermediate temporal volumes between source and target volumes.
  arXiv  Detail & Related papers  (2022-06-27T13:37:57Z)
• Non-invasive hemodynamic analysis for aortic regurgitation using computational fluid dynamics and deep learning [2.150638298922378]
  Changes in cardiovascular hemodynamics are closely related to the development of aortic regurgitation (AR) These metrics can be non-invasively obtained using four-dimensional (4D) flow magnetic resonance imaging (MRI) However, insufficient resolution often results from limitations in 4D flow MRI and complex AR hemodynamics. To address this, computational fluid dynamics simulations were transformed into synthetic 4D flow MRI data and used to train a variety of neural networks.
  arXiv  Detail & Related papers  (2021-11-23T05:19:42Z)
• Automated Multi-Channel Segmentation for the 4D Myocardial Velocity Mapping Cardiac MR [1.8653386811342048]
  Four-dimensional (4D) left ventricular myocardial velocity mapping (MVM) is a cardiac magnetic resonance (CMR) technique. We propose a novel framework that improves the standard U-Net based methods on these CMR multi-channel data. Our proposed network trained with multi-channel data shows enhanced performance compared to standard U-Net based networks trained with single-channel data.
  arXiv  Detail & Related papers  (2020-12-16T16:15:55Z)
• Multi-Temporal Convolutions for Human Action Recognition in Videos [83.43682368129072]
  We present a novel temporal-temporal convolution block that is capable of extracting at multiple resolutions. The proposed blocks are lightweight and can be integrated into any 3D-CNN architecture.
  arXiv  Detail & Related papers  (2020-11-08T10:40:26Z)
• 4D Spatio-Temporal Convolutional Networks for Object Position Estimation in OCT Volumes [69.62333053044712]
  3D convolutional neural networks (CNNs) have shown promising performance for pose estimation of a marker object using single OCT images. We extend 3D CNNs to 4D-temporal CNNs to evaluate the impact of additional temporal information for marker object tracking.
  arXiv  Detail & Related papers  (2020-07-02T12:02:20Z)
• 4DFlowNet: Super-Resolution 4D Flow MRI using Deep Learning and Computational Fluid Dynamics [0.0795451369160375]
  An increase in the image resolution would provide more accuracy and allow better assessment of the blood flow, especially for patients with abnormal flows. We utilized computational fluid dynamics simulations to generate synthetic 4D flow MRI data. Our novel 4DFlowNet network was trained on this synthetic 4D flow data and was capable in producing noise-free super resolution 4D flow phase images with upsample factor of 2.
  arXiv  Detail & Related papers  (2020-04-15T12:16:52Z)
• Multifold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE) [50.65891535040752]
  We propose a diffusion encoding scheme, called Slice-Interleaved Diffusion. SIDE, that interleaves each diffusion-weighted (DW) image volume with slices encoded with different diffusion gradients. We also present a method based on deep learning for effective reconstruction of DW images from the highly slice-undersampled data.
  arXiv  Detail & Related papers  (2020-02-25T14:48:17Z)
• 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.