Synthetic Velocity Mapping Cardiac MRI Coupled with Automated Left Ventricle Segmentation

• URL: http://arxiv.org/abs/2110.01304v1
• Date: Mon, 4 Oct 2021 10:20:27 GMT
• Title: Synthetic Velocity Mapping Cardiac MRI Coupled with Automated Left Ventricle Segmentation
• Authors: Xiaodan Xing, Yinzhe Wu, David Firmin, Peter Gatehouse, Guang Yang
• Abstract summary: We propose a frame synthesis algorithm to increase the temporal resolution of 3Dir MVM data. Our algorithm can not only increase the temporal resolution 3Dir MVMs, but can also generate the myocardium segmentation results at the same time.
• Score: 1.8268300764373178
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Temporal patterns of cardiac motion provide important information for cardiac disease diagnosis. This pattern could be obtained by three-directional CINE multi-slice left ventricular myocardial velocity mapping (3Dir MVM), which is a cardiac MR technique providing magnitude and phase information of the myocardial motion simultaneously. However, long acquisition time limits the usage of this technique by causing breathing artifacts, while shortening the time causes low temporal resolution and may provide an inaccurate assessment of cardiac motion. In this study, we proposed a frame synthesis algorithm to increase the temporal resolution of 3Dir MVM data. Our algorithm is featured by 1) three attention-based encoders which accept magnitude images, phase images, and myocardium segmentation masks respectively as inputs; 2) three decoders that output the interpolated frames and corresponding myocardium segmentation results; and 3) loss functions highlighting myocardium pixels. Our algorithm can not only increase the temporal resolution 3Dir MVMs, but can also generates the myocardium segmentation results at the same time.

Related papers

• Epicardium Prompt-guided Real-time Cardiac Ultrasound Frame-to-volume Registration [50.602074919305636]
  This paper introduces a lightweight end-to-end Cardiac Ultrasound frame-to-volume Registration network, termed CU-Reg. We use epicardium prompt-guided anatomical clues to reinforce the interaction of 2D sparse and 3D dense features, followed by a voxel-wise local-global aggregation of enhanced features.
  arXiv  Detail & Related papers  (2024-06-20T17:47:30Z)
• 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)
• A Comprehensive 3-D Framework for Automatic Quantification of Late Gadolinium Enhanced Cardiac Magnetic Resonance Images [5.947543669357994]
  Late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR) can directly visualize nonviable myocardium with hyperenhanced intensities. For heart attack patients, it is crucial to facilitate the decision of appropriate therapy by analyzing and quantifying their LGE CMR images. To achieve accurate quantification, LGE CMR images need to be processed in two steps: segmentation of the myocardium followed by classification of infarcts.
  arXiv  Detail & Related papers  (2022-05-21T11:54:39Z)
• HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis [12.999677043271133]
  We propose a hybrid deep learning (HDL) network, especially for synthetic 3Dir MVM data. Our proposed algorithm can still successfully synthesise high temporal resolution 3Dir MVM CMR data. This work is the first one in the literature investigating digital twins of the 3Dir MVM CMR.
  arXiv  Detail & Related papers  (2022-03-09T23:22:56Z)
• MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images [84.02849948202116]
  This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) MyoPS combines three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation.
  arXiv  Detail & Related papers  (2022-01-10T06:37:23Z)
• Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation [18.887520377396925]
  We propose a novel latent optimisation framework that jointly performs motion correction and super resolution for cardiac image segmentations. Experiments on two cardiac MR datasets show that the proposed framework achieves high performance, comparable to state-of-the-art super-resolution approaches.
  arXiv  Detail & Related papers  (2021-07-08T15:14:00Z)
• Myocardial Segmentation of Cardiac MRI Sequences with Temporal Consistency for Coronary Artery Disease Diagnosis [12.53412028532286]
  We propose a myocardial segmentation framework for sequence of cardiac MRI (CMR) scanning images of left ventricular cavity, right ventricular cavity, and myocardium. Our framework can improve the segmentation accuracy by up to 2% in Dice coefficient.
  arXiv  Detail & Related papers  (2020-12-29T01:54:09Z)
• 2-D Respiration Navigation Framework for 3-D Continuous Cardiac Magnetic Resonance Imaging [61.701281723900216]
  We propose a sampling adaption to acquire 2-D respiration information during a continuous scan. We develop a pipeline to extract the different respiration states from the acquired signals, which are used to reconstruct data from one respiration phase.
  arXiv  Detail & Related papers  (2020-12-26T08:29:57Z)
• 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)
• 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.