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. 2020 Feb:11319:113190Z.
Epub 2020 Mar 16.

Unsupervised Motion Tracking of Left Ventricle in Echocardiography

Shawn S Ahn  1 , Kevinminh Ta  1 , Allen Lu  2 , John C Stendahl  3 , Albert J Sinusas  4   3 , James S Duncan  1   5   4
Affiliations

Unsupervised Motion Tracking of Left Ventricle in Echocardiography

Shawn S Ahn et al. Proc SPIE Int Soc Opt Eng. 2020 Feb.

Abstract

Accurate motion tracking of the left ventricle is critical in detecting wall motion abnormalities in the heart after an injury such as a myocardial infarction. We propose an unsupervised motion tracking framework with physiological constraints to learn dense displacement fields between sequential pairs of 2-D B-mode echocardiography images. Current deep-learning motion-tracking algorithms require large amounts of data to provide ground-truth, which is difficult to obtain for in vivo datasets (such as patient data and animal studies), or are unsuccessful in tracking motion between echocardiographic images due to inherent ultrasound properties (such as low signal-to-noise ratio and various image artifacts). We design a U-Net inspired convolutional neural network that uses manually traced segmentations as a guide to learn displacement estimations between a source and target image without ground-truth displacement fields by minimizing the difference between a transformed source frame and the original target frame. We then penalize divergence in the displacement field in order to enforce incompressibility within the left ventricle. We demonstrate the performance of our model on synthetic and in vivo canine 2-D echocardiography datasets by comparing it against a non-rigid registration algorithm and a shape-tracking algorithm. Our results show favorable performance of our model against both methods.

Keywords: Deep learning; Echocardiography; Unsupervised motion tracking.

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Figures

Figure 1.
Figure 1.
Overview of the model.
Figure 2.
Figure 2.
U-net based CNN architecture for unsupervised motion tracking.
Figure 3.
Figure 3.
Estimated displacement vector field for a normal (healthy) synthetic sequence: (a) Ground Truth, (b) Non-rigid Registration, (c) Shape Tracking, (d) Our Model (No Constraints), (e) Our Model (SR), (f) Our Model (SR + DF).
Figure 4.
Figure 4.
Estimated displacement vector field for an LAD stenosis in vivo canine sequence: (a) Ground Truth, (b) Non-rigid Registration, (c) Shape Tracking, (d) Our Model (No Constraints), (e) Our Model (SR), (f) Our Model (SR + DF).
See this image and copyright information in PMC

References

    1. Mensah GA and Brown DW, “An overview of cardiovascular disease burden in the united states,” Health affairs 26(1), 38–48 (2007). - PubMed
    1. Rueckert D, Sonoda LI, Hayes C, Hill DL, Leach MO, and Hawkes DJ, “Nonrigid registration using free-form deformations: application to breast mr images,” IEEE transactions on medical imaging 18(8), 712–721 (1999). - PubMed
    1. Heyde B, Barbosa D, F. M. PC and D’hooge J, “Three-dimensional cardiac motion estimation based on non-rigid image registration using a novel transformation model adapted to the heart,” 7746 (2012).
    1. Lin N and Duncan J, “Generalized robust point matching using an extended free-form deformation model: application to cardiac images,” 1 (2004).
    1. Shi P, Sinusas AJ, E. R. RC and Duncan J, “Point-tracked quantitative analysis of left ventricular surface motion from 3-d image sequences,” IEEE transactions on medical imaging (2000). - PubMed

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