Anatomy-Aware Cardiac Motion Estimation

• URL: http://arxiv.org/abs/2008.07579v1
• Date: Mon, 17 Aug 2020 19:14:32 GMT
• Title: Anatomy-Aware Cardiac Motion Estimation
• Authors: Pingjun Chen, Xiao Chen, Eric Z. Chen, Hanchao Yu, Terrence Chen, Shanhui Sun
• Abstract summary: Myocardium feature tracking can directly estimate cardiac motion from cine MRI. Current deep learning-based FT methods may result in unrealistic myocardium shapes. We propose a novel Anatomy-Aware Tracker (AATracker) for cardiac motion estimation.
• Score: 11.680533842892107
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cardiac motion estimation is critical to the assessment of cardiac function. Myocardium feature tracking (FT) can directly estimate cardiac motion from cine MRI, which requires no special scanning procedure. However, current deep learning-based FT methods may result in unrealistic myocardium shapes since the learning is solely guided by image intensities without considering anatomy. On the other hand, motion estimation through learning is challenging because ground-truth motion fields are almost impossible to obtain. In this study, we propose a novel Anatomy-Aware Tracker (AATracker) for cardiac motion estimation that preserves anatomy by weak supervision. A convolutional variational autoencoder (VAE) is trained to encapsulate realistic myocardium shapes. A baseline dense motion tracker is trained to approximate the motion fields and then refined to estimate anatomy-aware motion fields under the weak supervision from the VAE. We evaluate the proposed method on long-axis cardiac cine MRI, which has more complex myocardium appearances and motions than short-axis. Compared with other methods, AATracker significantly improves the tracking performance and provides visually more realistic tracking results, demonstrating the effectiveness of the proposed weakly-supervision scheme in cardiac motion estimation.

Related papers

• Unsupervised 4D Cardiac Motion Tracking with Spatiotemporal Optical Flow Networks [7.920406261260867]
  This paper presents a motion tracking method where unsupervised optical flow networks are designed with spatial reconstruction loss and temporal-consistency loss. Our proposed loss functions make use of the pair-wise and temporal correlation to estimate cardiac motion from noisy background. To the best of our knowledge, this is the first work performed that uses unsupervised end-to-end deep learning optical flow network for 4D cardiac motion tracking.
  arXiv  Detail & Related papers  (2024-07-05T17:18:46Z)
• 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)
• Continuous 3D Myocardial Motion Tracking via Echocardiography [30.19879953016694]
  Myocardial motion tracking is an essential clinical tool in the prevention and detection of cardiovascular diseases. Current techniques suffer from incomplete and inaccurate motion estimation of the myocardium in both spatial and temporal dimensions. This paper introduces the Neural Cardiac Motion Field (NeuralCMF) to model the 3D structure and the comprehensive 6D forward/backward motion of the heart.
  arXiv  Detail & Related papers  (2023-10-04T13:11:20Z)
• Motion Magnification in Robotic Sonography: Enabling Pulsation-Aware Artery Segmentation [44.868281669589194]
  In order to improve the artery segmentation accuracy and stability during scans, this work presents a novel pulsation-assisted segmentation neural network (PAS-NN) Motion magnification techniques are employed to amplify the subtle motion within the frequency band of interest to extract the pulsation signals from sequential US images. The extracted real-time pulsation information can help to locate the arteries on cross-section US images.
  arXiv  Detail & Related papers  (2023-07-07T16:14:17Z)
• Live image-based neurosurgical guidance and roadmap generation using unsupervised embedding [53.992124594124896]
  We present a method for live image-only guidance leveraging a large data set of annotated neurosurgical videos. A generated roadmap encodes the common anatomical paths taken in surgeries in the training set. We trained and evaluated the proposed method with a data set of 166 transsphenoidal adenomectomy procedures.
  arXiv  Detail & Related papers  (2023-03-31T12:52:24Z)
• Mesh-based 3D Motion Tracking in Cardiac MRI using Deep Learning [11.177851736773823]
  3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessment of cardiac function and diagnosis of cardiovascular diseases. Most of the previous methods focus on estimating pixel-/voxel-wise motion fields in the full image space. In this work, we model the heart as a 3D geometric mesh and propose a novel deep learning-based method that can estimate 3D motion of the heart mesh from 2D short-axis CMR images.
  arXiv  Detail & Related papers  (2022-09-05T15:10:27Z)
• MulViMotion: Shape-aware 3D Myocardial Motion Tracking from Multi-View Cardiac MRI [11.685829837689404]
  We propose a novel multi-view motion estimation network (MulViMotion) to learn a consistent 3D motion field of the heart. We extensively evaluate the proposed method on 2D cine CMR images from 580 subjects of the UK Biobank study for 3D motion tracking of the left ventricular myocardium.
  arXiv  Detail & Related papers  (2022-07-29T18:29:52Z)
• Continuous Decoding of Daily-Life Hand Movements from Forearm Muscle Activity for Enhanced Myoelectric Control of Hand Prostheses [78.120734120667]
  We introduce a novel method, based on a long short-term memory (LSTM) network, to continuously map forearm EMG activity onto hand kinematics. Ours is the first reported work on the prediction of hand kinematics that uses this challenging dataset. Our results suggest that the presented method is suitable for the generation of control signals for the independent and proportional actuation of the multiple DOFs of state-of-the-art hand prostheses.
  arXiv  Detail & Related papers  (2021-04-29T00:11:32Z)
• 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)
• 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.