Key result
NMSR outperforms state-of-the-art methods in myocardial and cardiac blood flow dense tracking accuracy.
Why the study?
Manual measurement of myocardial motion and cardiac blood flow from echocardiograms is time-consuming and error-prone, while automated computer tracking algorithms have struggled with noise and high variability.
Does the neural multi-scale self-supervised registration (NMSR) method improve registration accuracy for echocardiogram dense tracking compared to existing state-of-the-art methods?
Does the neural multi-scale self-supervised registration (NMSR) method improve registration accuracy for echocardiogram dense tracking compared to existing state-of-the-art methods?
A novel neural multi-scale self-supervised registration method improves automated tracking of myocardial motion and cardiac blood flow on echocardiograms compared to existing computational tools.
Answers are grounded in this paper and cite their sources.
May improve automated echocardiographic tracking; leaves open clinical validation before practice adoption.
Echocardiography has become routinely used in the diagnosis of cardiomyopathy and abnormal cardiac blood flow. However, manually measuring myocardial motion and cardiac blood flow from echocar-diogram is time-consuming and error-prone. Computer algorithms that can automatically track and quantify myocardial motion and cardiac blood flow are highly sought after, but have not been very successful due to noise and high variability of echocardiography. In this work, we propose a neural multi-scale self-supervised registration (NMSR) method for automated myocardial and cardiac blood flow dense tracking. NMSR incorporates two novel components: 1) utilizing a deep neural net to parameterize the velocity field between two image frames, and 2) optimizing the parameters of the neural net in a sequential multi-scale fashion to account for large variations within the velocity field. Experiments demonstrate that NMSR yields significantly better registration accuracy than the state-of-the-art methods, such as advanced normalization tools (ANTs) and Voxel Morph, for both myocardial and cardiac blood flow dense tracking. Our approach promises to provide a fully automated method for fast and accurate analyses of echocardiograms.
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Zhu et al. (2019) studied Echocardiogram imaging (myocardial and cardiac blood flow tracking) (n=90). Neural Multi-Scale Self-Supervised Registration (NMSR) vs. ANTs and VoxelMorph was evaluated on Mean Square Error (MSE) and Mean Local Cross Correlation (Mean CC) for dense tracking. The Neural Multi-Scale Self-Supervised Registration (NMSR) method achieved superior registration accuracy for myocardial and cardiac blood flow dense tracking compared to state-of-the-art methods.
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