A deep learning phase-based solution in 2D echocardiography motion estimation.

Autor: Khoubani S; Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Hafez, Tehran, Iran., Moradi MH; Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Hafez, Tehran, Iran. mhmoradi@aut.ac.ir.
Jazyk: angličtina
Zdroj: Physical and engineering sciences in medicine [Phys Eng Sci Med] 2024 Dec; Vol. 47 (4), pp. 1691-1703. Date of Electronic Publication: 2024 Sep 12.
DOI: 10.1007/s13246-024-01481-2
Abstrakt: In this paper, we propose a new deep learning method based on Quaternion Wavelet Transform (QWT) phases of 2D echocardiographic sequences to estimate the motion and strain of myocardium. The proposed method considers intensity and phases gained from QWT as the inputs of customized PWC-Net structure, a high-performance deep network in motion estimation. We have trained and tested our proposed method performance using two realistic simulated B-mode echocardiographic sequences. We have evaluated our proposed method in terms of both geometrical and clinical indices. Our method achieved an average endpoint error of 0.06 mm per frame and 0.59 mm between End Diastole and End Systole on a simulated dataset. Correlation analysis between ground truth and the computed strain shows a correlation coefficient of 0.89, much better than the most efficient methods in the state-of-the-art 2D echocardiography motion estimation. The results show the superiority of our proposed method in both geometrical and clinical indices.
Competing Interests: Declarations. Conflict of interest: All the authors did not have a conflict of interest. Ethical approval: This article does not contain any studies with human participants performed by any of the authors. Consent to participations: Not applicable. Consent to publications: Not applicable.
(© 2024. Australasian College of Physical Scientists and Engineers in Medicine.)
Databáze: MEDLINE
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