Warianty tytułu
Języki publikacji
Abstrakty
Introduction: Due to its simplicity, block-matching is a popular motion-tracking method used in speckle-tracking echocardiography. Improvement of its robustness and accuracy is thus of prime interest. Although it seems plausible that the quality of block matching-based tracking depends on the local properties of image data, and thus, it should be possible to assess in advance how well certain portions of the image data are suited for displacement estimation, the potential relationship has not been studied extensively. Material and methods: This study aimed to search for a relationship between selected features of echocardiographic data and the quality of local displacement estimation. The study used a 3D synthetic B-mode imaging sequence data with known ground truth. Frame-to-frame displacements were estimated for 9856 points in five different frame pairs with mean inter-frame displacements of 0.15, 0.87, 2, 3.02, and 3.84 mm. In each case, tracking errors were evaluated against thirteen grayscale image features, the displacement’s magnitudes, and the normalized cross-correlation (NCX) values. Additionally, a multi-variable regression model was applied to test the combined ability of the proposed features to predict tracking quality. Results: Median tracking error magnitudes were 0.06, 0.13, 0.28, 0.74, and 1.5 mm for each image pair. Weak correlation between errors and individual data features was found only in the case of 3 features: NCX (Pearson’s correlation coefficients in the range of -0.366 to -0.223), number of speckles within the kernel (-0.283, -0.282, and -0.214 for three lowest deformations) and mean of the 3D gradient (-0.252, -0.237 and -0.25). The regression model, however, provided significant prediction improvement with R2 exceeding 0.5. Conclusions: In conclusion, only a weak relationship between the individual investigated kernel features and tracking accuracy has been established, but their combined strength can be assessed as at least moderate.
Rocznik
Tom
Strony
161--168
Opis fizyczny
Bibliogr. 17 poz., rys., tab.
Twórcy
autor
- Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, Warsaw, Poland, szymon.cygan@pw.edu.pl
autor
- Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, Warsaw, Poland
autor
- PMOD Technologies LLC, Fällanden, Switzerland
autor
- Cardiovascular Imaging and Dynamics, KU Leuven, Leuven, Belgium
Bibliografia
- 1. Alessandrini M, Heyde B, Queiros S, et al. Detailed Evaluation of Five 3D Speckle Tracking Algorithms Using Synthetic Echocardiographic Recordings. IEEE Trans Med Imaging. 2016;35(8):1915-1926. https://doi.org/10.1109/tmi.2016.2537848
- 2. Larsson M, Kremer F, Claus P, Brodin LA, D’hooge J. A novel measure to express tracking quality in ultrasound block matching. 2010 IEEE International Ultrasonics Symposium. 2010;28:1636-1639. https://doi.org/10.1109/ultsym.2010.5935715
- 3. D’hooge J, Barbosa D, Gao H, et al. Two-dimensional speckle tracking echocardiography: standardization efforts based on synthetic ultrasound data. Eur Heart J Cardiovasc Imaging. 2015;17(6):693-701. https://doi.org/10.1093/ehjci/jev197
- 4. Jasaityte R, Heyde B, D’hooge J. Current State of Three-Dimensional Myocardial Strain Estimation Using Echocardiography. Journal of the American Society of Echocardiography. 2013;26(1):15-28. https://doi.org/10.1016/j.echo.2012.10.005
- 5. Lesniak-Plewinska B, Cygan S, Kaluzynski K, et al. A Dual-Chamber, Thick-Walled Cardiac Phantom for Use in Cardiac Motion and Deformation Imaging by Ultrasound. Ultrasound in Medicine & Biology. 2010;36(7):1145-1156. https://doi.org/10.1016/j.ultrasmedbio.2010.04.008
- 6. Heyde B, Cygan S, Hon Fai Choi, et al. Regional cardiac motion and strain estimation in three-dimensional echocardiography: a validation study in thick-walled univentricular phantoms. IEEE Trans Ultrason, Ferroelect, Freq Contr. 2012;59(4):668-682. https://doi.org/10.1109/tuffc.2012.2245
- 7. Cygan S, Kumor M, Żmigrodzki J, Leśniak-Plewińska B, Kowalski M, Kałużyński K. Left ventricular phantoms with inclusions simulating transmural and non-transmural infarctions: FEM and EchoPAC study. Duric N, Heyde B, eds. SPIE Proceedings. 2017;10139:1013918. https://doi.org/10.1117/12.2254350
- 8. De Craene M, Marchesseau S, Heyde B, et al. 3D Strain Assessment in Ultrasound (Straus): A Synthetic Comparison of Five Tracking Methodologies. IEEE Trans Med Imaging. 2013;32(9):1632-1646. https://doi.org/10.1109/tmi.2013.2261823
- 9. Zmigrodzki J, Cygan S, Wilczewska A, Kaluzynski K. Quantitative Assessment of the Effect of the Out-of-Plane Movement of the Homogenous Ellipsoidal Model of the Left Ventricle on the Deformation Measures Estimated Using 2-D Speckle Tracking - An In-Silico Study. IEEE Trans Ultrason, Ferroelect, Freq Contr. 2018;65(10):1789-1803. https://doi.org/10.1109/tuffc.2018.2856127
- 10. Wilczewska A, Cygan S, Żmigrodzki J. Segmentation Enhanced Elastic Image Registration for 2D Speckle Tracking Echocardiography—Performance Study In Silico. Ultrason Imaging. 2022;44(1):39-54. https://doi.org/10.1177/01617346211068812
- 11. Voigt JU, Pedrizzetti G, Lysyansky P, et al. Definitions for a common standard for 2D speckle tracking echocardiography: consensus document of the EACVI/ASE/Industry Task Force to standardize deformation imaging. European Heart Journal – Cardiovascular Imaging. 2014;16(1):1-11. https://doi.org/10.1093/ehjci/jeu184
- 12. Heyde B, Alessandrini M, Hermans J, Barbosa D, Claus P, D’hooge J. Anatomical Image Registration Using Volume Conservation to Assess Cardiac Deformation From 3D Ultrasound Recordings. IEEE Trans Med Imaging. 2016;35(2):501-511. https://doi.org/10.1109/tmi.2015.2479556
- 13. Chapelle D, Le Tallec P, Moireau P, Sorine M. An energy-preserving muscle tissue model: formulation and compatible discretizations. Int J Mult Comp Eng. 2012;10(2):189-211. https://doi.org/10.1615/intjmultcompeng.2011002360
- 14. Hang Gao, Hon Fai Choi, Claus P, et al. A fast convolution-based methodology to simulate 2-Dd/3-D cardiac ultrasound images. IEEE Trans Ultrason, Ferroelect, Freq Contr. 2009;56(2):404-409. https://doi.org/10.1109/tuffc.2009.1051
- 15. Alessandrini M, De Craene M, Bernard O, et al. A Pipeline for the Generation of Realistic 3D Synthetic Echocardiographic Sequences: Methodology and Open-Access Database. IEEE Trans Med Imaging. 2015;34(7):1436-1451. https://doi.org/10.1109/tmi.2015.2396632
- 16. Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res. 2003;(3):1157-1182.
- 17. Wuensch KL, Evans JD. Straightforward Statistics for the Behavioral Sciences. Journal of the American Statistical Association. 1996;91(436):1750. https://doi.org/10.2307/2291607
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Bibliografia
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